We recently released a review of machine learning methods in protein engineering, but the field changes so fast and there are so many new papers that any static document will inevitably be missing important work. This format also allows us to broaden the scope beyond engineering-specific applications. We hope that this will be a useful resource for people interested in the field.

To the best of our knowledge, this is the first public, collaborative list of machine learning papers on protein applications. We try to classify papers based on a combination of their applications and model type. If you have suggestions for other papers or categories, please make a pull request or issue!

Within each category, papers are listed in reverse chronological order (newest first). Where possible, a link should be provided.

Reviews
Tools and datasets
Machine-learning guided directed evolution
Representation learning
Unsupervised variant prediction
Generative models
Biophysics
Predicting stability
Predicting structure from sequence
Predicting sequence from structure
Classification, annotation, search, and alignments
Predicting interactions with other molecules
Other supervised learning

Machine Learning for Protein Engineering.
Kadina E. Johnston, Clara Fannjiang, Bruce J. Wittmann, Brian L. Hie, Kevin K. Yang & Zachary Wu.
Machine Learning in Molecular Sciences, October 2023.
[10.1007/978-3-031-37196-7_9]

Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.
Wen Jun Xie, Arieh Warshel.
Preprint, October 2023.
[10.1101/2023.10.10.561808]

Machine Learning-Guided Protein Engineering.
Petr Kouba, Pavel Kohout, Faraneh Haddadi, Anton Bushuiev, Raman Samusevich, Jiri Sedlar, Jiri Damborsky, Tomas Pluskal, Josef Sivic, and Stanislav Mazurenko.
ACS Catalysis, October 2023.
[10.1021/acscatal.3c02743]

Generative artificial intelligence for de novo protein design.
Adam Winnifrith, Carlos Outeiral, Brian Hie.
Preprint, October 2023.
[arxiv]

Growing ecosystem of deep learning methods for modeling protein.
Julia R. Rogers, Gergő Nikolényi, Mohammed AlQuraishi.
Preprint, October 2023.
[arxiv]

Exploring the Protein Sequence Space with Global Generative Models.
Sergio Romero-Romero, Sebastian Lindner, Noelia Ferruz.
Preprint, May 2023.
[arxiv]

Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action.
Zhiye Guo, Jian Liu, Yanli Wang, Mengrui Chen, Duolin Wang, Dong Xu, Jianlin Cheng.
Preprint, February 2023.
[arxiv]

Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering.
Marcel Wittmund, Frederic Cadet and Mehdi D. Davari.
ACS Catalysis, November 2022.
[10.1021/acscatal.2c01426]

From sequence to function through structure: deep learning for protein design.
Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, Alexander Goncearenco, Luca Naef, Christian Dallago.
Preprint, September 2022.
[10.1101/2022.08.31.505981]

Computational protein design with evolutionary-based and physics-inspired modeling: current and future synergies.
Cyril Malbranke, David Bikard, Simona Cocco, Rémi Monasson, Jérôme Tubiana.
Preprint, August 2022.
[arxiv]

Deep learning approaches for conformational flexibility and switching properties in protein design.
Lucas S. P. Rudde, Mahdi Hijazi, Patrick Barth.
Front. Mol. Biosci., August 2022.
[10.3389/fmolb.2022.928534]

Controllable protein design with language models.
Noelia Ferruz, Birte Höker.
Nature Machine Intelligence, June 2022.
[10.1038/s42256-022-00499-z]

The road to fully programmable protein catalysis.
Sarah L. Lovelock, Rebecca Crawshaw, Sophie Basler, Colin Levy, David Baker, Donald Hilvert, Anthony P. Green.
Nature, June 2022.
[10.1038/s41586-022-04456-z]

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design.
Ben E. Clifton, Dan Kozome, and Paola Laurino.
Biochemistry, March 2022.
[10.1021/acs.biochem.1c00757]

Learning functional properties of proteins with language models.
Serbulent Unsal, Heval Atas, Muammer Albayrak, Kemal Turhan, Aybar C. Acar & Tunca Doğan.
Nature Machine Intelligence, March 2022.
[10.1038/s42256-022-00457-9]

Applications of artificial intelligence to enzyme and pathway design for metabolic engineering.
Woo Dae Jang, Gi Bae Kim, Yeji Kim, Sang Yup Lee.
Current Opinion in Biotechnology, February 2022.
[10.1016/j.copbio.2021.07.024]

Adaptive machine learning for protein engineering.
Brian L. Hie, Kevin K. Yang.
Current Opinion in Structural Biology, February 2022.
[10.1016/j.sbi.2021.11.002]

Protein sequence design with deep generative models.
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang.
Current Opinion in Chemical Biology, December 2021.
[10.1016/j.cbpa.2021.04.004]

AI challenges for predicting the impact of mutations on protein stability.
Fabrizio Pucci, Martin Schwersensky, Marianne Rooman.
Preprint, November 2021.
[arxiv]

Advances in machine learning for directed evolution. Bruce J Wittmann, Kadina E Johnston, Zachary Wu, Frances H Arnold.
Current Opinion in Structural Biology, August 2021.
10.1016/j.sbi.2021.01.008]

A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction.
Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, Elham Yavari. Preprint, August 2021.
[arxiv]

Learning the protein language: Evolution, structure, and function.
Tristan Bepler, Bonnie Berger.
Cell Systems, June 2021.
[10.1016/j.cels.2021.05.017]

Representation learning applications in biological sequence analysis.
Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada, Natsuki Iwano, Shunsuke Sumi, Shion Hosoda, Shitao Zhao, Tsukasa Fukunaga, Michiaki Hamada.
Computational and Structural Biotechnology Journal, May 2021.
[10.1016/j.csbj.2021.05.039]

Data-driven computational protein design.
Vincent Frappier, Amy E. Keating.
Current Opinion in Structural Biology, May 2021.
/10.1016/j.sbi.2021.03.009]

Machine learning in protein structure prediction.
Mohammed AlQuraishi.
Current Opinion in Chemical Biology, May 2021.
[10.1016/j.cbpa.2021.04.005]

Protein sequence-to-structure learning: Is this the end(-to-end revolution)?.
Elodie Laine, Stephan Eismann, Arne Elofsson, Sergei Grudinin.
Preprint, May 2021.
[arxiv]

Revolutionizing enzyme engineering through artificial intelligence and machine learning.
Nitu Singh, Sunny Malik, Anvita Gupta, Kinshuk Raj Srivastava.
Emerging topics in life sciences, April 2021.
[10.1042/ETLS20200257]

The language of proteins: NLP, machine learning & protein sequences.
Dan Ofer, Nadav Brandes, Michal Linial.
Computational and Structural Biotechnology Journal, January 2021.
[10.1016/j.csbj.2021.03.022]

Chapter Twelve - Machine learning-assisted enzyme engineering.
Niklas E. Siedhoff, Ulrich Schwaneberg and Mehdi D. Davari.
Methods in Enzymology, November 2020.
[10.1016/bs.mie.2020.05.005]

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.
Sebastian Raschka, Benjamin Kaufman.
Preprint, January 2020.
[arXiv]

Machine Learning in Enzyme Engineering.
Stanislav Mazurenko, Zbynek Prokop, Jiri Damborsky.
ACS Catalysis, December 2019.
[10.1021/acscatal.9b04321]

Machine learning-guided directed evolution for protein engineering.
Kevin K. Yang, Zachary Wu, Frances H. Arnold.
Nature Methods, July 2019.
[10.1038/s41592-019-0496-6]
Preprint available on arxiv.

Evaluating Protein Transfer Learning with TAPE.
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song.
Preprint, June 2019.
[arxiv]

Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes?
Guangyue Li, Yijie Dong, Manfred T. Reetz.
Advanced Synthesis & Catalysis, March 2019.
[10.1002/adsc.201900149]

Scaffold-Lab: Critical Evaluation and Ranking of Protein Backbone Generation Methods in A Unified Framework.
Zhuoqi Zheng, Bo Zhang, Bozitao Zhong, Kexin Liu, Zhengxin Li, Junjie Zhu, Jinyu Yu, Ting Wei, Hai-Feng Chen.
Preprint, May 2024.
[10.1101/2024.02.10.579743]

Computational Scoring and Experimental Evaluation of Enzymes Generated by Neural Networks.
Sean R. Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak, Kevin K. Yang.
Nature Biotechnology, April 2024.
[10.1038/s41587-024-02214-2]

Deep indel mutagenesis reveals the impact of insertions and deletions on protein stability and function.
Magdalena Topolska, Antoni Beltran, Ben Lehner.
Preprint, October 2023.
[10.1101/2023.10.06.561180]

OpenProteinSet: Training data for structural biology at scale.
Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Daniel Berenberg, Ian Fisk, Andrew M. Watkins, Stephen Ra, Richard Bonneau, Mohammed AlQuraishi.
Preprint, August 2023.
[arxiv]

Mega-scale experimental analysis of protein folding stability in biology and design.
Kotaro Tsuboyama, Justas Dauparas, Jonathan Chen, Elodie Laine, Yasser Mohseni Behbahani, Jonathan J. Weinstein, Niall M. Mangan, Sergey Ovchinnikov & Gabriel J. Rocklin.
Nature, July 2023.
[10.1038/s41586-023-06328-6]

FLOP: Tasks for Fitness Landscapes Of Protein wildtypes.
Peter Mørch Groth, Richard Michael, Jesper Salomon, Pengfei Tian, Wouter Boomsma.
Preprint, June 2023.
[10.1101/2023.06.21.545880]

PDBench: Evaluating Computational Methods for Protein-Sequence Design.
Leonardo V Castorina, Rokas Petrenas, Kartic Subr, Christopher W Wood.
Bioinformatics, January 2023.
[10.1093/bioinformatics/btad027]]

The energetic and allosteric landscape for KRAS inhibition.
Chenchun Weng, Andre J. Faure, Ben Lehner.
Preprint, December 2022.
[10.1101/2022.12.06.519122]

ManyFold: an efficient and flexible library for training and validating protein folding models.
Amelia Villegas-Morcillo, Louis Robinson, Arthur Flajolet, Thomas D Barrett.
Bioinformatics, December 2022.
[10.1093/bioinformatics/btac773]

Mega-scale experimental analysis of protein folding stability in biology and protein design.
Kotaro Tsuboyama, Justas Dauparas, Jonathan Chen, Elodie Laine, Yasser Mohseni Behbahani, Jonathan J. Weinstein, Niall M. Mangan, Sergey Ovchinnikov, Gabriel J. Rocklin.
Preprint, December 2022.
[10.1101/2022.12.06.519132]

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design.
Neil Thomas, Atish Agarwala, David Belanger, Yun S. Song, Lucy J. Colwell.
Preprint, October 2022.
[10.1101/2022.10.28.514293]

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.
Megan Leander, Zhuang Liu, Qiang Cui, Srivatsan Raman.
Elife, October 2022.
[10.7554/eLife.79932]

Randomized gates eliminate bias in sort-seq assays.
Brian L. Trippe, Buwei Huang, Erika A. DeBenedictis, Brian Coventry, Nicholas Bhattacharya, Kevin K. Yang, David Baker, Lorin Crawford.
Protein Science, August 2022.
[10.1002/pro.4401]

Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold.
Ziyao Li, Xuyang Liu, Weijie Chen, Fan Shen, Hangrui Bi, Guolin Ke, Linfeng Zhang.
Preprint, August 2022.
[10.1101/2022.08.04.502811]

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding.
Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang.
Preprint, June 2022.
[arxiv]

FLIP: Benchmark tasks in fitness landscape inference for proteins.
Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, Kevin K. Yang.
NeurIPS 2021 Datasets and Benchmarks Track, December 2021.
[10.1101/2021.11.09.467890]

evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library.
Bruce J. Wittmann, Kadina E. Johnston, Patrick J. Almhjell, Frances H. Arnold.
Preprint, November 2021.
[10.1101/2021.11.18.469179]

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.
Milena Pavlović, Lonneke Scheffer, Keshav Motwani, Chakravarthi Kanduri, Radmila Kompova, Nikolay Vazov, Knut Waagan, Fabian L. M. Bernal, Alexandre Almeida Costa, Brian Corrie, Rahmad Akbar, Ghadi S. Al Hajj, Gabriel Balaban, Todd M. Brusko, Maria Chernigovskaya, Scott Christley, Lindsay G. Cowell, Robert Frank, Ivar Grytten, Sveinung Gundersen, Ingrid Hobæk Haff, Eivind Hovig, Ping-Han Hsieh, Günter Klambauer, Marieke L. Kuijjer, Christin Lund-Andersen, Antonio Martini, Thomas Minotto, Johan Pensar, Knut Rand, Enrico Riccardi, Philippe A. Robert, Artur Rocha, Andrei Slabodkin, Igor Snapkov, Ludvig M. Sollid, Dmytro Titov, Cédric R. Weber, Michael Widrich, Gur Yaari, Victor Greiff & Geir Kjetil Sandve.
Nature Machine Intelligence, November 2021.
[10.1038/s42256-021-00413-z]

Learned embeddings from deep learning to visualize and predict protein sets.
Christian Dallago, Konstantin Schütze, Michael Heinzinger, Tobias Olenyi, Maria Littmann, Amy X Lu, Kevin K Yang, Seonwoo Min, Sungroh Yoon, James T Morton, Burkhard Rost.
Current Protocols, May 2021.
[10.1002/cpz1.113]

Population-Based Black-Box Optimization for Biological Sequence Design.
Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley.
ICML, July 2020.
[ICML]

Selene: a PyTorch-based deep learning library for sequence data.
Kathleen M. Chen, Evan M. Cofer, Jian Zhou, Olga G. Troyanskaya.
Nature Methods, March 2019.
[10.1038/s41592-019-0360-8]

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning.
Tobias Vornholt, Mojmír Mutný, Gregor W. Schmidt, Christian Schellhaas, Ryo Tachibana, Sven Panke, Thomas R. Ward, Andreas Krause*, and Markus Jeschek.
ACS Central Science, May 2024.
[10.1021/acscentsci.4c00258]

Machine Learning and Directed Evolution of Base Editing Enzymes.
Ramiro M. Perrotta, Svenja Vinke, Raphaël Ferreira, Michaël Moret, Ahmed Mahas, Anush Chiappino-Pepe, Lisa M. Riedmayr, Anna-Thérèse Mehra, Louisa S. Lehmann, George M. Church.
Preprint, May 2024.
[10.1101/2024.05.17.594556]

Aligning protein generative models with experimental fitness via Direct Preference Optimization.
Talal Widatalla, Rafael Rafailov, Brian Hie.
Preprint, May 2024.
[10.1101/2024.05.20.595026]

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function.
Brianne R. King, Kiera H. Sumida, Jessica L. Caruso, David Baker, Jesse G. Zalatan.
Preprint, May 2024.
[10.1101/2024.04.18.590141]

Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering.
Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel, Paul J. Zurek, Tanja Knaus, Elie Patel, Daniel Boros, Friedrich-Maximilian Weberling, Matthew R. A. Kenneth, Oskar J. Klein, Elliot J. Medcalf, Jacob Moss, Michael Herger, Tomasz S. Kaminski, Francesco G. Mutti, Pietro Lio, Florian Hollfelder.
Preprint, April 2024.
[10.1101/2024.04.08.588565]

Automated in vivo enzyme engineering accelerates biocatalyst optimization.
Enrico Orsi, Lennart Schada von Borzyskowski, Stephan Noack, Pablo I. Nikel & Steffen N. Lindner.
Nature Communications, April 2024.
[10.1038/s41467-024-46574-4]

Engineering highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening.
Neil Thomas, David Belanger, Chenling Xu, Hanson Lee, Kathleen Hirano, Kosuke Iwai, Vanja Polic, Kendra D Nyberg, Kevin Hoff, Lucas Frenz, Charlie A Emrich, Jun W Kim, Mariya Chavarha, Abi Ramanan, Jeremy J Agresti, Lucy J Colwell.
Preprint, April 2024.
[10.1101/2024.03.21.585615]

Interpretable and explainable predictive machine learning models for data-driven protein engineering.
David Medina-Ortiz, Ashkan Khalifeh, Hoda Anvari-Kazemabad and Mehdi D. Davari.
Preprint, March 2024.
[arxiv]

Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime.
Stefanie Hemmer, Niklas Erik Siedhoff, Sophia Werner, Gizem Ölçücü, Ulrich Schwaneberg, Karl-Erich Jaeger, Mehdi D. Davari, and Ulrich Krauss JACS Au, November 2023.
[10.1021/jacsau.3c00440]

Biophysics-based protein language models for protein engineering.
Sam Gelman, Bryce Johnson, Chase Freschlin, Sameer D'Costa, Anthony Gitter, Philip A. Romero.
Preprint, March 2024.
[10.1101/2024.03.15.585128]

Improving protein expression, stability, and function with ProteinMPNN.
Kiera H. Sumida, Reyes Núñez-Franco, Indrek Kalvet, Samuel J. Pellock, Basile I. M. Wicky, Lukas F. Milles, Justas Dauparas, Jue Wang, Yakov Kipnis, Noel Jameson, Alex Kang, Joshmyn De La Cruz, Banumathi Sankaran, Asim K. Bera, Gonzalo Jiménez-Osés, David Baker.
Preprint, October 2023.
[10.1101/2023.10.03.560713]

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions.
Aerin Yang, Kevin M Jude, Ben Lai, Mason Minot, Anna M Kocyla, Caleb R Glassman, Daisuke Nishimiya, Yoon Seok Kim, Sai T Reddy, Aly A Khan, K Christopher Garcia.
Science, July 2023
[10.1126/science.adh1720]

Bidirectional Learning for Offline Model-based Biological Sequence Design.
Can Chen, Yingxue Zhang, Xue Liu, Mark Coates.
Preprint, January 2023.
[arxiv]

Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC.
Patrick Emami, Aidan Perreault, Jeffrey Law, David Biagioni, Peter C. St. John.
Preprint, December 2022.
[arxiv]

Combinatorial assembly and design of enzymes.
Rosalie Lipsh-Sokolik, Olga Khersonsky, Sybrin P. Schröder, Casper de Boer, Shlomo-Yakir Hoch, Gideon J. Davies, Hermen S. Overkleeft, Sarel J. Fleishman.
Preprint, December 2022.
[10.1101/2022.09.17.508230]

Forecasting labels under distribution-shift for machine-guided sequence design.
Lauren Berk Wheelock, Stephen Malina, Jeffrey Gerold, Sam Sinai.
Preprint, November 2022
[arxiv]

PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design.
Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew Watkins, Henri Dwyer, Vladimir Gligorijevic, Richard Bonneau, Stephen Ra, Kyunghyun Cho.
Preprint, October 2022.
[arxiv]

Designed active-site library reveals thousands of functional GFP variants.
Jonathan Yaacov Weinstein, Carlos Marti Gomez Aldaravi, Rosalie Lipsh-Sokolik, Shlomo Yakir Hoch, Demian Liebermann, Reinat Nevo, Haim Weissman, Ekaterina Petrovich-Kopitman, David Margulies, Dmitry Ivankov, David McCandlish, Sarel Jacob Fleishman.
Preprint, October 2022.
[10.1101/2022.10.11.511732]

Accelerated rational PROTAC design via deep learning and molecular simulations.
Shuangjia Zheng, Youhai Tan, Zhenyu Wang, Chengtao Li, Zhiqing Zhang, Xu Sang, Hongming Chen & Yuedong Yang.
Nature Machine Intelligence, September 2022.
[10.1038/s42256-022-00527-y]

Inferring protein fitness landscapes from laboratory evolution experiments.
Sameer D’Costa, Emily C. Hinds, Chase R. Freschlin, Hyebin Song, Philip A. Romero.
Preprint, September 2022.
[10.1101/2022.09.01.506224]

Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness.
Sharrol Bachas, Goran Rakocevic, David Spencer, Anand V. Sastry, Robel Haile, John M. Sutton, George Kasun, Andrew Stachyra, Jahir M. Gutierrez, Edriss Yassine, Borka Medjo, Vincent Blay, Christa Kohnert, Jennifer T. Stanton, Alexander Brown, Nebojsa Tijanic, Cailen McCloskey, Rebecca Viazzo, Rebecca Consbruck, Hayley Carter, Simon Levine, Shaheed Abdulhaqq, Jacob Shaul, Abigail B. Ventura, Randal S. Olson, Engin Yapici, Joshua Meier, Sean McClain, Matthew Weinstock, Gregory Hannum, Ariel Schwartz, Miles Gander, Roberto Spreafico.
Preprint, August 2022.
[10.1101/2022.08.16.504181]

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.
Emily K. Makowski, Patrick C. Kinnunen, Jie Huang, Lina Wu, Matthew D. Smith, Tiexin Wang, Alec A. Desai, Craig N. Streu, Yulei Zhang, Jennifer M. Zupancic, John S. Schardt, Jennifer J. Linderman, Peter M. Tessier.
Nature communications, July 2022.
[10.1038/s41467-022-31457-3]

A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering.
Alexander-Maurice Illig, Niklas E. Siedhoff, Ulrich Schwaneberg and Mehdi D. Davari.
Preprint, June 2022.
[arxiv]

Heterogeneity of the GFP fitness landscape and data-driven protein design.
Louisa Gonzalez Somermeyer, Aubin Fleiss, Alexander S Mishin, Nina G Bozhanova, Anna A Igolkina, Jens Meiler, Maria-Elisenda Alaball Pujol, Ekaterina V Putintseva, Karen S Sarkisyan.
eLife, May 2022.
[10.7554/eLife.75842]

De novo protein design by deep network hallucination.
Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku, Theresa A. Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K. Bera, Frank DiMaio, Lauren Carter, Cameron M. Chow, Gaetano T. Montelione & David Baker.
Nature, December 2021.
[10.1038/s41586-021-04184-w]

Informed training set design enables efficient machine learning-assisted directed protein evolution.
Bruce J. Wittmann, Yisong Yue, Frances H. Arnold.
Cell Systems, November 2021.
[10.1016/j.cels.2021.07.008]

Machine learning-based library design improves packaging and diversity of adeno-associated virus (AAV) libraries.
Danqing Zhu, David H. Brookes, Akosua Busia, Ana Carneiro, Clara Fannjiang, Galina Popova, David Shin, Edward F. Chang, Tomasz J. Nowakowski, Jennifer Listgarten, David. V. Schaffer.
Preprint, November 2021.
[10.1101/2021.11.02.467003]

Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models.
Eli N. Weinstein, Alan N. Amin, Will Grathwohl, Daniel Kassler, Jean Disset, Debora S. Marks.
Preprint, October 2021.
[10.1101/2021.10.28.466307]

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond.
Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville.
Preprint, October 2021.
[arxiv]

Machine-Directed Evolution of an Imine Reductase for Activity and Stereoselectivity.
Eric J. Ma, Elina Siirola, Charles Moore, Arkadij Kummer, Markus Stoeckli, Michael Faller, Caroline Bouquet, Fabian Eggimann, Mathieu Ligibel, Dan Huynh, Geoffrey Cutler, Luca Siegrist, Richard A. Lewis, Anne-Christine Acker, Ernst Freund, Elke Koch, Markus Vogel, Holger Schlingensiepen, Edward J. Oakeley, and Radka Snajdrova.
ACS Catalysis, September 2021.
[10.1021/acscatal.1c02786]

PyPEF—An Integrated Framework for Data-Driven Protein Engineering.
Niklas E. Siedhoff, Alexander-Maurice Illig, Ulrich Schwaneberg, and Mehdi D. Davari* J. Chem. Inf. Model, July 2021.
[10.1021/acs.jcim.1c00099]

Conservative Objective Models for Effective Offline Model-Based Optimization.
Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine.
Preprint, July 2021.
[arxiv]

Deep Extrapolation for Attribute-Enhanced Generation.
Alvin Chan, Ali Madani, Ben Krause, Nikhil Naik.
Preprint, July 2021.
[arxiv]

Effective Surrogate Models for Protein Design with Bayesian Optimization.
Nate Gruver, Samuel Stanton, Polina Kirichenko, Marc Finzi, Phillip Maffettone, Vivek Myers, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson.
2021 ICML Workshop on Computational Biology, July 2021.
[pdf]

Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution.
Trevor S. Frisby, Christopher James Langmead.
Algorithms for Molecular Biology, July 2021.
[10.1186/s13015-021-00195-4]

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design.
Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth.
Preprint, July 2021.
[arxiv]

In silico proof of principle of machine learning-based antibody design at unconstrained scale.
Rahmad Akbar,Philippe A. Robert,Cédric R. Weber,Michael Widrich,Robert Frank,Milena Pavlović,Lonneke Scheffer,Maria Chernigovskaya,Igor Snapkov,Andrei Slabodkin,Brij Bhushan Mehta,Enkelejda Miho,Fridtjof Lund-Johansen,Jan Terje Andersen,Sepp Hochreiter, Ingrid Hobæk Haff,Günter Klambauer,Geir Kjetil Sandve,Victor Greiff.
Preprint, July 2021.
[10.1101/2021.07.08.451480]

Deep diversification of an AAV capsid protein by machine learning.
Drew H. Bryant, Ali Bashir, Sam Sinai, Nina K. Jain, Pierce J. Ogden, Patrick F. Riley, George M. Church, Lucy J. Colwell & Eric D. Kelsic.
Nature Biotechnology, February 2021.
[10.1038/s41587-020-00793-4]

Deep Uncertainty and the Search for Proteins.
Zelda Mariet, Ghassen Jerfel, Zi Wang, Christof Angermüller, David Belanger, Suhani Vora, Maxwell Bileschi, Lucy Colwell, D Sculley, Dustin Tran, Jasper Snoek.
NeurIPS 2020 ML for Molecules Workshop, December 2020.
[pdf]

Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production.
Jonathan C. Greenhalgh, Sarah A. Fahlberg, Brian F. Pfleger, Philip A. Romero.
Preprint, May 2021.
[10.1101/2021.05.21.445192]

Large-scale design and refinement of stable proteins using sequence-only models.
Jedediah M. Singer, Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta, … Eric Klavins.
Preprint, March 2021.
[10.1101/2021.03.12.435185]

AdaLead: A simple and robust adaptive greedy search algorithm for sequence design.
Sam Sinai, Richard Wang, Alexander Whatley, Stewart Slocum, Elina Locane, Eric D. Kelsic. Preprint, October 2020.
[arxiv]

The NK Landscape as a Versatile Benchmark for Machine Learning Driven Protein Engineering.
Adam C. Mater, Mahakaran Sandhu, Colin Jackson.
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[10.1101/2020.09.30.319780]

Learning with uncertainty for biological discovery and design.
Brian Hie, Bryan Bryson, Bonnie Berger.
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Population-Based Black-Box Optimization for Biological Sequence Design.
Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D Sculley.
ICML, July 2020.
[ICML]

Autofocused oracles for model-based design.
Clara Fannjiang, Jennifer Listgarten.
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[arxiv]

Domain Extrapolation via Regret Minimization.
Wengong Jin, Regina Barzilay, Tommi Jaakkola.
Preprint, June 2020.
[arxiv]

Fast differentiable DNA and protein sequence optimization for molecular design.
Johannes Linder, Georg Seelig.
Preprint, May 2020.
[arxiv]

A Deep Dive into Machine Learning Models for Protein Engineering.
Yuting Xu, Deeptak Verma, Robert P Sheridan, Andy Liaw, Junshui Ma, Nicholas Marshall, John McIntosh, Edward C. Sherer, Vladimir Svetnik, Jennifer Johnston.
Journal of Chemical Information and Modeling, April 2020.
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Evolutionary context-integrated deep sequence modeling for protein engineering.
Yunan Luo, Lam Vo, Hantian Ding, Yufeng Su, Yang Liu, Wesley Wei Qian, Huimin Zhao, Jian Peng.
Preprint, January 2020.
[10.1101/2020.01.16.908509]

Biological Sequence Design using Batched Bayesian Optimization.
David Belanger, Suhani Vora, Zelda Mariet, Ramya Deshpande, David Dohan, Christof Angermueller, Kevin Murphy, Olivier Chapelle, Lucy Colwell.
NeurIPS Workshop on Machine Learning and the Physical Sciences, December 2019.
[ML4PS]

Model Inversion Networks for Model-Based Optimization.
Aviral Kumar, Sergey Levine Preprint, December 2019.
[arxiv]

Interpreting mutational effects predictions, one substitution at a time.
C. K. Sruthi, Meher K. Prakash.
bioRxiv, December 2019
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A structure-based deep learning framework for protein engineering.
Raghav Shroff, Austin W. Cole, Barrett R. Morrow, Daniel J. Diaz, Isaac Donnell, Jimmy Gollihar, Andrew D. Ellington, Ross Thyer.
Preprint, November 2019.
[10.1101/833905]

Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design.
Pierce J. Ogden, Eric D. Kelsic, Sam Sinai, George M. Church.
Science, November 2019.
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Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics.
Claire N. Bedbrook, Kevin K. Yang, J. Elliott Robinson, Viviana Gradinaru, Frances H Arnold.
Nature Methods, October 2019.
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Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design.
Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue.
International Conference on Artificial Intelligence and Statistics (AISTATS), April 2019.
[arxiv] [PMLR]

Machine learning-assisted directed protein evolution with combinatorial libraries.
Zachary Wu, S. B. Jennifer Kan, Russell D. Lewis, Bruce J. Wittmann, Frances H. Arnold.
PNAS, April 2019.
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Conditioning by adaptive sampling for robust design.
David H. Brookes, Hahnbeom Park, Jennifer Listgarten.
Preprint, January 2019.
[arxiv]

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.
Frédéric Cadet, Nicolas Fontaine, Guangyue Li, Joaquin Sanchis, Matthieu Ng Fuk Chong, Rudy Pandjaitan, Iyanar Vetrivel, Bernard Offmann, Manfred T. Reetz.
Scientific Reports, November 2018.
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Design by adaptive sampling.
David H. Brookes, Jennifer Listgarten.
Preprint, October 2018.
[arxiv]

Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins.
Yutaka Saito, Misaki Oikawa, Hikaru Nakazawa, Teppei Niide, Tomoshi Kameda, Koji Tsuda, and Mitsuo Umetsu.
ACS Synthetic Biology, August 2018.
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Toward machine-guided design of proteins.
Surojit Biswas, Gleb Kuznetsov, Pierce J. Ogden, Nicholas J. Conway, Ryan P. Adams, George M. Church.
Preprint, June 2018.
[10.1101/337154] [bioRxiv]

Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions.
Anvita Gupta, James Zou.
Preprint, April 2018.
[arxiv]

Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization.
Claire N. Bedbrook, Kevin K. Yang, Austin J. Rice, Viviana Gradinaru, Frances H. Arnold.
PLOS Computational Biology, October 2017.
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Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants.
Yaman Musdal, Sridhar Govindarajan, Bengt Mannervik.
Protein Engineering, Design, and Selection, August 2017.
[10.1093%2Fprotein%2Fgzx045]

Navigating the protein fitness landscape with Gaussian processes.
Philip A. Romero, Andreas Krause, Frances H. Arnold.
PNAS, January 2013.
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Engineering proteinase K using machine learning and synthetic genes.
Jun Liao, Manfred K. Warmuth, Sridhar Govindarajan, Jon E. Ness, Rebecca P Wang, Claes Gustafsson, Jeremy Minshull.
BMC Biotechnology, March 2007.
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Improving catalytic function by ProSAR-driven enzyme evolution.
Richard J. Fox, S. Christopher Davis, Emily C. Mundorff, Lisa M. Newman, Vesna Gavrilovic, Steven K. Ma, Loleta M. Chung, Charlene Ching, Sarena Tam, Sheela Muley, John Grate, John Gruber, John C. Whitman, Roger A. Sheldon, Gjalt W. Huisman.
Nature Biotechnology, February 2007.
[Nature Biotechnology]

Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models.
Francesca-Zhoufan Li, Ava P. Amini, Yisong Yue, Kevin K. Yang, Alex X. Lu. ICML, July 2024.
[10.1101/2024.02.05.578959]

ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention.
Mingchen Li, Yang Tan, Xinzhu Ma, Bozitao Zhong, Huiqun Yu, Ziyi Zhou, Wanli Ouyang, Bingxin Zhou, Liang Hong, Pan Tan.
Preprint, May 2024.
[10.1101/2024.04.15.589672]

Biophysics-based protein language models for protein engineering.
Sam Gelman, Bryce Johnson, Chase Freschlin, Sameer D’Costa, Anthony Gitter, Philip A. Romero.
Preprint, March 2024.
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Convolutions are competitive with transformers for protein sequence pretraining.
Kevin K. Yang, Nicolo Fusi, Alex X. Lu.
Cell Systems, February 2024.
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Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry.
Anastasiya V. Kulikova, Daniel J. Diaz, Tianlong Chen, T. Jeffrey Cole, Andrew D. Ellington & Claus O. Scientific reports, August 2023.
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Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations.
Nabil Ibtehaz, Yuki Kagaya, Daisuke Kihara.
Preprint, August 2023.
[10.1101/2023.08.23.554486]

Contextual protein and antibody encodings from equivariant graph transformers.
Sai Pooja Mahajan, Jeffrey A. Ruffolo, Jeffrey J. Gray.
Preprint, July 2023.
[10.1101/2023.07.15.549154]

Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling.
Ahmed Elnaggar, Hazem Essam, Wafaa Salah-Eldin, Walid Moustafa, Mohamed Elkerdawy, Charlotte Rochereau, Burkhard Rost.
Preprint, June 2023.
[arxiv]

Structure-aware protein self-supervised learning.
Can (Sam) Chen, Jingbo Zhou, Fan Wang, Xue Liu, Dejing Dou.
Bioinformatics, April 2023.
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Lightweight Contrastive Protein Structure-Sequence Transformation.
Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, Stan Z. Li.
Preprint, March 2023.
[arxiv]

A Systematic Study of Joint Representation Learning on Protein Sequences and Structures.
Zuobai Zhang, Chuanrui Wang, Minghao Xu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, Jian Tang.
Preprint, March 2023.
[arxiv]

Structure-informed Language Models Are Protein Designers.
Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu.
Preprint, Feb 2023.
[10.1101/2023.02.03.526917]

Retrieved Sequence Augmentation for Protein Representation Learning.
Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Lingpeng Kong.
Preprint, Feb 2023.
[10.1101/2023.02.22.529597]

ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts.
Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang.
Preprint, January 2023.
[arxiv]

Codon language embeddings provide strong signals for protein engineering.
Carlos Outeiral, Charlotte M. Deane.
Preprint, December 2022.
[10.1101/2022.12.15.519894]

When Geometric Deep Learning Meets Pretrained Protein Language Models.
Fang Wu, Yu Tao, Dragomir Radev, Jinbo Xu.
Preprint, December 2022.
[arxiv]

Contrastive learning of protein representations with graph neural networks for structural and functional annotations.
Jiaqi Luo, Yunan Luo.
Preprint, December 2022.
[10.1101/2022.11.29.518451]

Training self-supervised peptide sequence models on artificially chopped proteins .
Gil Sadeh, Zichen Wang, Jasleen Grewal, Huzefa Rangwala, Layne Price.
Preprint, November 2022.
[arxiv]

Masked inverse folding with sequence transfer for protein representation learning.
Kevin K. Yang, Niccolò Zanichelli, Hugh Yeh.
Protein Engineering, Design, and Selection, October 2022.
[10.1093/protein/gzad015]

Language models of protein sequences at the scale of evolution enable accurate structure prediction.
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
Preprint, July 2022.
[10.1101/2022.07.20.500902]

Advancing protein language models with linguistics: a roadmap for improved interpretability.
Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak, Victor Greiff, Geir Kjetil Sandve, Dag Trygve Truslew Haug.
Preprint, July 2022.
[arxiv]

Self-supervised deep learning encodes high-resolution features of protein subcellular localization.
Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti & Loic A. Royer.
Nature Methods, July 2022.
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COLLAPSE: A representation learning framework for identification and characterization of protein structural sites.
Alexander Derry, Russ B. Altman.
Preprint, July 2022.
[10.1101/2022.07.20.500713]

CoSP: Co-supervised pretraining of pocket and ligand.
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li.
Preprint, June 2022.
[arxiv]

Pre-training Protein Models with Molecular Dynamics Simulations for Drug Binding.
Wu F, Zhang Q, Radev D, Wang Y, Jin X, Jiang Y, Li SZ, Niu Z.
Preprint, June 2022.
[10.21203/rs.3.rs-1566483/v1]

Exploring evolution-based &-free protein language models as protein function predictors.
Mingyang Hu, Fajie Yuan, Kevin K. Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding.
Preprint, June 2022.
[arxiv]

Evolutionary velocity with protein language models.
Brian L. Hie, Kevin K. Yang, Peter S. Kim.
Cell Systems, April 2022.
[10.1016/j.cels.2022.01.003]

Identification of Enzymatic Active Sites with Unsupervised Language Modeling.
Loïc Kwate Dassi, Matteo Manica, Daniel Probst, Philippe Schwaller, Yves Gaetan Nana Teukam, Teodoro Laino.
Preprint, November 2021.
[10.33774/chemrxiv-2021-m20gg]

Artificial Intelligence Guided Conformational Mining of Intrinsically Disordered Proteins.
Aayush Gupta, Souvik Dey, Huan-Xiang Zhou.
Preprint, November 2021.
[10.1101/2021.11.21.469457]

Deciphering the language of antibodies using self-supervised learning.
Jinwoo Leem, Laura S. Mitchell, James H.R. Farmery, Justin Barton, Jacob D. Galson.
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[10.1101/2021.11.10.468064]

Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model.
Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, Bin Shao, Tao Qin, Tie-Yan Liu.
Preprint, October 2021.
[arxiv]

Neural Distance Embeddings for Biological Sequences.
Gabriele Corso, Rex Ying, Michal Pándy, Petar Veličković, Jure Leskovec, Pietro Liò.
Preprint, September 2021.
[arxiv]

Biologically relevant transfer learning improves transcription factor binding prediction.
Gherman Novakovsky, Manu Saraswat, Oriol Fornes, Sara Mostafavi & Wyeth W. Wasserman.
Genome Biology, September 2021.
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Toward More General Embeddings for Protein Design: Harnessing Joint Representations of Sequence and Structure.
Sanaa Mansoor, Minkyung Baek, Umesh Madan, Eric Horvitz.
Preprint, September 2021.
[10.1101/2021.09.01.458592]

Hydrogen bonds meet self-attention: all you need for general-purpose protein structure embedding.
Cheng Chen, Yuguo Zha, Daming Zhu, Kang Ning, Xuefeng Cui.
Preprint, August 2021.
[10.1101/2021.01.31.428935]

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.
Alex X Lu, Amy X Lu, Iva Pritišanac, Taraneh Zarin, Julie D Forman-Kay, Alan M Moses.
Preprint, July 2021.
[10.1101/2021.07.29.454330]

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs.
Dan Rosenbaum, Marta Garnelo, Michal Zielinski, Charlie Beattie, Ellen Clancy, Andrea Huber, Pushmeet Kohli, Andrew W. Senior, John Jumper, Carl Doersch, S. M. Ali Eslami, Olaf Ronneberger, Jonas Adler.
Preprint, June 2021.. [arxiv]

Pretraining model for biological sequence data.
Bosheng Song, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, Xiangzheng Fu.
Briefings in Functional Genomics, May 2021.
[10.1093/bfgp/elab025]

ProteinBERT: A universal deep-learning model of protein sequence and function.
Nadav Brandes, Dan Ofer, Yam Peleg, Nadav Rappoport, Michal Linial.
Preprint, May 2021.
[10.1101/2021.05.24.445464]

Random Embeddings and Linear Regression can Predict Protein Function.
Tianyu Lu, Alex X. Lu, Alan M. Moses.
Preprint, April 2021.
[arxiv]

Combining evolutionary and assay-labelled data for protein fitness prediction.
Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, Jennifer Listgarten.
Preprint, March 2021.
[10.1101/2021.03.28.437402]

MSA Transformer.
Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John F. Canny, Pieter Abbeel, Tom Sercu, Alexander Rives.
Preprint, February 2021.
[10.1101/2021.02.12.430858]

Improving Generalizability of Protein Sequence Models with Data Augmentations.
Hongyu Shen, Layne C. Price, Taha Bahadori, Franziska Seeger.
Preprint, February 2021.
[10.1101/2021.02.18.431877]

Capturing Protein Domain Structure and Function Using Self-Supervision on Domain Architectures.
Damianos P. Melidis, Wolfgang Nejdl.
Algorithms, January 2021.
[10.3390/a14010028]

Adversarial Contrastive Pre-training for Protein Sequences.
Matthew B. A. McDermott, Brendan Yap, Harry Hsu, Di Jin, Peter Szolovits. Preprint, January 2021.
[arxiv]

Fast end-to-end learning on protein surfaces.
Freyr Sverrisson, Jean Feydy, Bruno E. Correia, Michael M. Bronstein.
Preprint, December 2020.
[10.1101/2020.12.28.424589]

Transformer protein language models are unsupervised structure learners.
Roshan Rao, Sergey Ovchinnikov, Joshua Meier, Alexander Rives, Tom Sercu.
Preprint, December 2020.
[10.1101/2020.12.15.422761]

Self-Supervised Representation Learning of Protein Tertiary Structures (PtsRep): Protein Engineering as A Case Study.
Junwen Luo, Yi Cai, Jialin Wu, Hongmin Cai, Xiaofeng Yang, Zhanglin Lin.
Preprint, December 2020.
[10.1101/2020.12.22.423916]

What is a meaningful representation of protein sequences?. Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma.
Preprint, November 2020.
[arxiv]

Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models.
Pascal Sturmfels, Jesse Vig, Ali Madani, Nazneen Fatema Rajani. Preprint, November 2020.
[arxiv]

Fixed-Length Protein Embeddings using Contextual Lenses.
Amir Shanehsazzadeh, David Belanger, David Dohan. Preprint, October 2020.
[arxiv]

Evaluation of Methods for Protein Representation Learning: A Quantitative Analysis.
Serbulent Unsal, Heval Ataş, Muammer Albayrak, Kemal Turhan, Aybar C. Acar, Tunca Doğan.
Preprint, October 2020.
[10.1101/2020.10.28.359828]

Self-Supervised Contrastive Learning of Protein Representations By Mutual Information Maximization.
Amy X. Lu, Haoran Zhang, Marzyeh Ghassemi, Alan Moses.
Preprint, September 2020.
[10.1101/2020.09.04.283929]

ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing.
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost.
Preprint, July 2020.
[10.1101/2020.07.12.199554]

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function.
Amelia Villegas-Morcillo, Stavros Makrodimitris, Roeland van Ham, Angel M. Gomez, Victoria Sanchez, Marcel Reinders.
Preprint, April 2020.
[10.1101/2020.04.07.028373]

Site2Vec: a reference frame invariant algorithm for vector embedding of protein-ligand binding sites.
Arnab Bhadra, Kalidas Y.
Preprint, March 2020.
[arxiv]

Evolutionary context-integrated deep sequence modeling for protein engineering.
Yunan Luo, Lam Vo, Hantian Ding, Yufeng Su, Yang Liu, Wesley Wei Qian, Huimin Zhao, Jian Peng.
Preprint, January 2020.
[10.1101/2020.01.16.908509]

Sequence representations and their utility for predicting protein-protein interactions.
Dhananjay Kimothi, Pravesh Biyani, James M Hogan.
Preprint, December 2019.
[10.1101/2019.12.31.890699]

Language modelling for biological sequences – curated datasets and baselines.
Jose Juan Almagro Armenteros, Alexander Rosenberg Johansen, Ole Winther, Henrik Nielsen.
Preprint, December 2019.
[alrojo.github.io]

Deciphering protein evolution and fitness landscapes with latent space models
Xinqiang Ding, Zhengting Zou, Charles L. Brooks III.
Nature Communications, December 2019.
[10.1038/s41467-019-13633-0]

End-to-end multitask learning, from protein language to protein features without alignments.
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Burkhard Rost.
Preprint, December 2019.
[10.1101/864405]

Unified rational protein engineering with sequence-only deep representation learning.
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, George M. Church.
Nature Methods, October 2019
[10.1038/s41592-019-0598-1]

Structure-Based Function Prediction using Graph Convolutional Networks.
Vladimir Gligorijevic, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Kunghyun Cho, Tommi Vatanen, Daniel Berenberg, Bryn Taylor, Ian M. Fisk, Ramnik J. Xavier, Rob Knight, Richard Bonneau.
Preprint, October 2019.
[0.1101/786236]

Modeling the language of life – Deep Learning Protein Sequences.
Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Christian Dallago, Dmitrii Nechaev, Florian Matthes, Burkhard Rost.
Preprint, September 2019.
[10.1101/614313]

Augmenting Protein Network Embeddings with Sequence Information.
Hassan Kane, Mohamed K. Coulibali, Pelkins Ajanoh, Ali Abdallah.
Preprint, August 2019.
[10.1101/730481]

Universal Deep Sequence Models for Protein Classification.
Nils Strodthoff, Patrick Wagner, Markus Wenzel, Wojciech Samek.
Preprint, July 2019.
[10.1101/704874]

DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences.
Ehsaneddin Asgari, Nina Poerner, Alice C. McHardy, Mohammad R.K. Mofrad.
Preprint, July 2019.
[10.1101/705426]

A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence.
Chi-Hua Yu, Zhao Qin, Francisco J. Martin-Martinez, Markus J. Buehler.
ACS Nano, June 2019.
[10.1021/acsnano.9b02180]

Evaluating Protein Transfer Learning with TAPE.
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song.
Preprint, June 2019.
[arxiv]

Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins.
Julius Upmeier zu Belzen, Thore Bürgel, Stefan Holderbach, Felix Bubeck, Lukas Adam, Catharina Gandor, Marita Klein, Jan Mathony, Pauline Pfuderer, Lukas Platz, Moritz Przybilla, Max Schwendemann, Daniel Heid, Mareike Daniela Hoffmann, Michael Jendrusch, Carolin Schmelas, Max Waldhauer, Irina Lehmann, Dominik Niopek, Roland Eils.
Nature Machine Intelligence, May 2019.
[Nature Machine Intelligence]

Modeling the Language of Life – Deep Learning Protein Sequences.
Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Christian Dallago, Dmitrii Nechaev, Florian Matthes, Burkhard Rost.
Preprint, May 2019.
[10.1101/614313] [bioRxiv]

Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences.
Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus.
Preprint, April 2019.
[10.1101/622803] [bioRxiv]

Learning protein constitutive motifs from sequence data.
Jérôme Tubiana, Simona Cocco, Rémi Monasson.
eLife, March 2019.
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Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX).
Ehsaneddin Asgari, Alice C. McHardy, Mohammad R. K. Mofrad.
Scientific Reports, March 2019.
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Learning protein sequence embeddings using information from structure.
Tristan Bepler, Bonnie Berger.
International Conference on Learning Representations, February 2019.
[ICLR]

Application of fourier transform and proteochemometrics principles to protein engineering.
Frédéric Cadet, Nicolas Fontaine, Iyanar Vetrivel, Matthieu Ng Fuk Chong, Olivier Savriama, Xavier Cadet, Philippe Charton.
BMC Bioinformatics, October 2018.
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Learned protein embeddings for machine learning.
Kevin K Yang, Zachary Wu, Claire N Bedbrook, Frances H Arnold
Bioinformatics, August 2018
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Deep Semantic Protein Representation for Annotation, Discovery, and Engineering.
Ariel S Schwartz, Gregory J Hannum, Zach R Dwiel, Michael E Smoot, Ana R Grant, Jason M Knight, Scott A Becker, Jonathan R Eads, Matthew C LaFave, Harini Eavani, Yinyin Liu, Arjun K Bansal, Toby H Richardson
Preprint, July 2018
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Improved Descriptors for the Quantitative Structure–Activity Relationship Modeling of Peptides and Proteins.
Mark H. Barley, Nicholas J. Turner, Royston Goodacre.
Journal of Chemical Information and Modeling, January 2018.
[10.1021/acs.jcim.7b00488]

Variational auto-encoding of protein sequences.
Sam Sinai, Eric Kelsic, George M. Church, Martin A. Nowak
Preprint, December 2017
[arxiv]

Predicting Protein Binding Affinity With Word Embeddings and Recurrent Neural Networks.
Carlo Mazzaferro.
Preprint, April 2017.
[10.1101/128223] [bioRxiv]

dna2vec: Consistent vector representations of variable-length k-mers.
Patrick Ng
Preprint, January 2017
[arxiv]

Distributed Representations for Biological Sequence Analysis.
Dhananjay Kimothi, Akshay Soni, Pravesh Biyani, James M. Hogan
Preprint, August 2016
[arxiv]

ProFET: Feature engineering captures high-level protein functions.
Dan Ofer, Michal Linial.
Bioinformatics, June 2015.
[10.1093/bioinformatics/btv345]

AAindex: amino acid index database, progress report 2008.
Shuichi Kawashima, Piotr Pokarowski, Maria Pokarowska, Andrzej Kolinski, Toshiaki Katayama, Minoru Kanehisa.
Nucleic Acids Research, January 2008.
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Predicted mechanistic impacts of human protein missense variants.
Jurgen Janes, Marc Muller, Senthil Selvaraj, Diogo Manoel, James Stephenson, Catarina Goncalves, Aleix Lafita, Benjamin Polacco, Kirsten Obernier, Kaur Alasoo, Manuel C Lemos, Nevan Krogan, Maria Martin, Luis R. Saraiva, David Burke, Pedro Beltrao.
Preprint, May 2024.
[10.1101/2024.05.29.596373]

Decoding molecular mechanisms for loss of function variants in the human proteome.
Matteo Cagiada, Nicolas Jonsson, Kresten Lindorff-Larsen.
Preprint, May 2024.
[10.1101/2024.05.21.595203]

AlphaFold2 can predict single-mutation effects.
John M. McBride, Konstantin Polev, Amirbek Abdirasulov, Vladimir Reinharz, Bartosz A. Grzybowski, Tsvi Tlusty.
Nature, October 2023.
[10.1101/2022.04.14.488301]

Learning from prepandemic data to forecast viral escape.
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Nature, October 2023.
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Predicting Immune Escape with Pretrained Protein Language Model Embeddings.
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Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.
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Efficient and accurate sequence generation with small-scale protein language models.
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SE(3) diffusion model with application to protein backbone generation.
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ICML, July 2023.
ACM

De novo design of protein structure and function with RFdiffusion.
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ProtWave-VAE: Integrating autoregressive sampling with latent-based inference for data-driven protein design.
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ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Models.
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ProteinVAE: Variational AutoEncoder for Translational Protein Design.
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Evaluating Prompt Tuning for Conditional Protein Sequence Generation.
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De novo design of luciferases using deep learning.
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A Text-guided Protein Design Framework.
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Unlocking de novo antibody design with generative artificial intelligence.
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A high-level programming language for generative protein design.
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Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization.
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Illuminating protein space with a programmable generative model.
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models.
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De novo PROTAC design using graph-based deep generative models.
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Neural Network-Derived Potts Models for Structure-Based Protein Design using Backbone Atomic Coordinates and Tertiary Motifs.
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ProtGPT2 is a deep unsupervised language model for protein design.
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ProteinSGM: Score-based generative modeling for de novo protein design.
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Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models. Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, Jianzhu Ma.
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Predicting the antigenic evolution of SARS-COV-2 with deep learning.
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Hallucinating protein assemblies.
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ProGen2: Exploring the Boundaries of Protein Language Models.
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DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations.
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Fragment-Based Ligand Generation Guided By Geometric Deep Learning On Protein-Ligand Structure.
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Design in the DARK: Learning Deep Generative Models for De Novo Protein Design.
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Sampling the conformational landscapes of transporters and receptors with AlphaFold2.
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Benchmarking deep generative models for diverse antibody sequence design.
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Navigating the amino acid sequence space between functional proteins using a deep learning framework.
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BioPhi: A platform for antibody design, humanization and humanness evaluation based on natural antibody repertoires and deep learning.
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Ancestral Sequence Reconstruction for Co-evolutionary models.
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AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational approximated Landscape.
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International Journal of Molecular Sciences, August 2021.
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Modeling sequence-space exploration and emergence of epistatic signals in protein evolution.
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[arxiv]

Generative AAV capsid diversification by latent interpolation.
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Protein design and variant prediction using autoregressive generative models.
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Expanding functional protein sequence spaces using generative adversarial networks.
Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Jan Zrimec, Simona Poviloniene, Irmantas Rokaitis, Audrius Laurynenas, Wissam Abuajwa, Otto Savolainen, Rolandas Meskys, Martin K. M. Engqvist, Aleksej Zelezniak.
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Generating functional protein variants with variational autoencoders.
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Generating novel protein sequences using Gibbs sampling of masked language models.
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The structure-fitness landscape of pairwise relations in generative sequence models.
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De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks.
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Deep learning enables the design of functional de novo antimicrobial proteins.
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Generative probabilistic biological sequence models that account for mutational variability.
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IG-VAE: Generative Modeling of Immunoglobulin Proteins by Direct 3D Coordinate Generation.
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A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences. Johannes Linder, Nicholas Bogard, Alexander B. Rosenberg, Georg Seelig Cell Systems, July 2020 [10.1016/j.cels.2020.05.007]

Signal Peptides Generated by Attention-Based Neural Networks.
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Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction.
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Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks.
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ProGen: Language Modeling for Protein Generation.
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, Richard Socher.
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De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN).
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Reconstructing continuous distributions of 3D protein structure from cryo-EM images.
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Deep generative models for T cell receptor protein sequences.
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Generative Models for Graph-Based Protein Design.
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ICLR workshop on Deep Generative Models for Highly Structured Data, May 2019.
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How to Hallucinate Functional Proteins.
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Generative modeling for protein structures.
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NeurIPS, December 2018.
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Design of metalloproteins and novel protein folds using variational autoencoders.
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Design by adaptive sampling.
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[arxiv]

Deep generative models of genetic variation capture the effects of mutations.
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Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions.
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[arxiv]

Recurrent Neural Network Model for Constructive Peptide Design.
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Variational auto-encoding of protein sequences.
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Accurate Conformation Sampling via Protein Structural Diffusion.
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ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model.
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Chemically Transferable Generative Backmapping of Coarse-Grained Proteins.
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Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni De Fabritiis.
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[arxiv]

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PROSTATA: Protein Stability Assessment using Transformers.
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Towards generalizable prediction of antibody thermostability using machine learning on sequence and structure features.
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Learning deep representations of enzyme thermal adaptation.
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Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset. Peishan Huang, Simon K. S. Chu, Henrique N. Frizzo, Morgan P. Connolly, Ryan W. Caster, and Justin B. Siegel.
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Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic Optima Gang Li, Kersten S. Rabe, Jens Nielsen, Martin K. M. Engqvist.
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Accurate structure prediction of biomolecular interactions with AlphaFold 3.
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ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours.
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[arxiv]

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Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model.
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[arxiv]

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Highfold: accurately predicting cyclic peptide monomers and complexes with AlphaFold.
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Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation.
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[arxiv]

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[arxiv]

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EigenFold: Generative Protein Structure Prediction with Diffusion Models.
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[arxiv]

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DR-BERT: A Protein Language Model to Annotate Disordered Regions.
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AlphaFold Prediction of Structural Ensembles of Disordered Proteins.
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AFsample: Improving Multimer Prediction with AlphaFold using Aggressive Sampling.
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OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.
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Improved the Protein Complex Prediction with Protein Language Models.
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tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence Homologs.
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Ultra-fast protein structure prediction to capture effects of sequence variation in mutation movies.
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Improving protein secondary structure prediction by deep language models and transformer networks.
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Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.
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Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA.
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Hallucination of closed repeat proteins containing central pockets.
Linna An, Derrick R Hicks, Dmitri Zorine, Justas Dauparas, Basile I. M. Wicky, Lukas F. Milles, Alexis Courbet, Asim K. Bera, Hannah Nguyen, Alex Kang, Lauren Carter, David Baker.
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SE(3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation.
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SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2.
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PLoS Computational Biology, August 2022.
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Few-Shot Learning of Accurate Folding Landscape for Protein Structure Prediction.
Jun Zhang, Sirui Liu, Mengyun Chen, Haotian Chu, Min Wang, Zidong Wang, Jialiang Yu, Ningxi Ni, Fan Yu, Diqing Chen, Yi Isaac Yang, Boxin Xue, Lijiang Yang, Yuan Liu, Yi Qin Gao.
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[arxiv]

Atomic protein structure refinement using all-atom graph representations and SE(3)-equivariant graph neural networks.
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HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative.. Xiaomin Fang, Fan Wang, Lihang Liu, Jingzhou He, Dayong Lin, Yingfei Xiang, Xiaonan Zhang, Hua Wu, Hui Li, Le Song.
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NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.
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High-resolution de novo structure prediction from primary sequence.
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Protein Structure Prediction with Expectation Reflection.
Evan Cresswell-Clay, Danh-Tai Hoang, Joe McKenna, Chris Yang, Eric Zhang, Vipul Periwal.
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PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction.
Sirui Liu, Jun Zhang, Haotian Chu, Min Wang, Boxin Xue, Ningxi Ni, Jialiang Yu, Yuhao Xie, Zhenyu Chen, Mengyun Chen, Yuan Liu, Piya Patra, Fan Xu, Jie Chen, Zidong Wang, Lijiang Yang, Fan Yu, Lei Chen, Yi Qin Gao.
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[arxiv]

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Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments.
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Accurate prediction of protein structures and interactions using a three-track neural network.
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Distillation of MSA Embeddings to Folded Protein Structures with Graph Transformers.
Allan Costa, Manvitha Ponnapati, Joseph M. Jacobson, Pranam Chatterjee.
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Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
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Multi-task deep learning for concurrent prediction of protein structural properties.
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A multi-task deep-learning system for predicting membrane associations and secondary structures of proteins.
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Single Layers of Attention Suffice to Predict Protein Contacts.
Nicholas Bhattacharya, Neil Thomas, Roshan Rao, Justas Dauparas, Peter K. Koo, David Baker, Yun S. Song, Sergey Ovchinnikov.
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Fast and effective protein model refinement by deep graph neural networks.
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Protein Structural Alignments From Sequence.
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Study of Real-Valued Distance Prediction For Protein Structure Prediction with Deep Learning.
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REALDIST: Real-valued protein distance prediction.
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Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments.
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Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.
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Phylogenetic correlations have limited effect on coevolution-based contact prediction in proteins.
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Near-complete protein structural modelling of the minimal genome.
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[arxiv]

Template-based prediction of protein structure with deep learning.
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[arXiv]

A fully open-source framework for deep learning protein real-valued distances.
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PhANNs, a fast and accurate tool and web server to classify phage structural proteins.
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DeepDist: real-value inter-residue distance prediction with deep residual convolutional network.
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Improved protein structure prediction using predicted inter-residue orientations.
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Deep learning methods in protein structure prediction.
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Improved protein structure prediction using potentials from deep learning.
Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis.
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Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.
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DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences.
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End-to-End Differentiable Learning of Protein Structure.
Mohammed AlQuraishi.
Cell Systems, April 2019.
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DESTINI: A deep-learning approach to contact-driven protein structure prediction.
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Learning protein sequence embeddings using information from structure.
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International Conference on Learning Representations, February 2019.
[ICLR]

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[arxiv]

Porter 5: fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes.
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Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method.
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Protein Secondary Structure Prediction with Long Short Term Memory Networks.
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[arxiv]

Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction.
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[arxiv]

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Inverse Protein Folding Using Deep Bayesian Optimization.
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[arxiv]

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De novo protein design by inversion of the AlphaFold structure prediction network.
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Robust deep learning–based protein sequence design using ProteinMPNN.
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arxiv

PeTriBERT : Augmenting BERT with tridimensional encoding for inverse protein folding and design.
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SIPF: Sampling Method for Inverse Protein Folding.
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KDD, August 2022.
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Rotamer-free protein sequence design based on deep learning and self-consistency.
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Accurate and efficient protein sequence design through learning concise local environment of residues.
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Protein sequence sampling and prediction from structural data.
Gabriel Andres Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P Dunne, Leonardo Álvarez.
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[10.1101/2021.09.06.459171]

Design of proteins presenting discontinuous functional sites using deep learning.
Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, Ivan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker. Preprint, November 2020.
[10.1101/2020.11.29.402743]

Learning from Protein Structure with Geometric Vector Perceptrons.
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J.L. Townshend, Ron Dror.
Preprint, September 2020.
[arxiv]

Protein Sequence Design with a Learned Potential.
Namrata Anand, Raphael R. Eguchi, Alexander Derry, Russ B. Altman, Po-Ssu Huang.
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[10.1101/2020.01.06.895466]

Designing real novel proteins using deep graph neural networks.
Alexey Strokach, David Becerra, Carles Corbi, Albert Perez-Riba, Philip M. Kim.
Preprint, December 2019.
[10.1101/868935] [bioRxiv]

ProDCoNN: Protein design using a convolutional neural network.
Yuan Zhang, Yang Chen, Chenran Wang, Chun‐Chao Lo, Xiuwen Liu, Wei Wu, Jinfeng Zhang.
Proteins: Structure, Function, Bioinformatics, December 2019.
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RamaNet: Computational De Novo Protein Design using a Long Short-Term Memory Generative Adversarial Neural Network.
Sari Sabban, Mikhail Markovsky.
Preprint, June 2019.
[10.1101/671552] [bioRxiv]

Generative Models for Graph-Based Protein Design.
John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola.
ICLR workshop on Deep Generative Models for Highly Structured Data, May 2019.
[OpenReview]

SPIN2: Predicting sequence profiles from protein structures using deep neural networks.
James O'Connell, Zhixiu Li, Jack Hansonm, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou.
Proteins, March 2018.
[10.1002/prot.25489]

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling.
Wenkai Xiang, Zhaoping Xiong, Huan Chen, Jiacheng Xiong, Wei Zhang, Zunyun Fu, Mingyue Zheng, Bing Liu, Qian Shi.
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[10.1101/2024.05.07.593067]

Sequence alignment using large protein structure alphabets doubles sensitivity to remote homologs.
Robert C. Edgar.
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[10.1101/2024.05.24.595840]

ProteinCLIP: enhancing protein language models with natural language.
Kevin E. Wu, Howard Chang, James Zou.
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[10.1101/2024.05.14.594226]

Protein Language Models Expose Viral Mimicry and Immune Escape.
Dan Ofer, Michal Linial.
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[10.1101/2024.03.14.585057]

Predicting enzymatic function of protein sequences with attention.
Nicolas Buton, François Coste, Yann Le Cunff.
Bioinformatics, October 2023.
[10.1093/bioinformatics/btad620]

Clustering predicted structures at the scale of the known protein universe.
Inigo Barrio-Hernandez, Jingi Yeo, Jürgen Jänes, Milot Mirdita, Cameron L. M. Gilchrist, Tanita Wein, Mihaly Varadi, Sameer Velankar, Pedro Beltrao & Martin Steinegger.
Nature, September 2023.
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TEMPROT: protein function annotation using transformers embeddings and homology search.
Gabriel B. Oliveira, Helio Pedrini & Zanoni Dias.
BMC Bioinformatics, June 2023.
[10.1186/s12859-023-05375-0]

Enzyme function prediction using contrastive learning.
Tianhao Yu, Haiyang Cui, Jianan Canal Li, Yunan Luo, Guangde Jiang, Huimin Zhao. Science, March 2023.
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ProteInfer, deep neural networks for protein functional inference.
Theo Sanderson, Maxwell L Bileschi, David Belanger, Lucy J Colwell.
eLife, February 2023.
[10.7554/eLife.80942]

Machine learning models for the prediction of enzyme properties should be tested on proteins not used for model training.
Alexander Kroll, Martin J. Lercher.
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[10.1101/2023.02.06.526991]

Language models can identify enzymatic active sites in protein sequences.
Yves Gaetan Nana Teukam ,Loïc Kwate Dassi ,Matteo Manica, Daniel Probst ,Philippe Schwaller ,Teodoro Laino.
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Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings.
Wayland Yeung, Zhongliang Zhou, Sheng Li, Natarajan Kannan.
Briefings in Bioinformatics, January 2023.
[10.1093/bib/bbac599]

Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion.
Qianmu Yuan, Junjie Xie, Jiancong Xie, Huiying Zhao, Yuedong Yang Preprint, December 2022.
[10.1101/2022.12.05.519119]

Vector-clustering Multiple Sequence Alignment: Aligning into the twilight zone of protein sequence similarity with protein language models.
Claire D. McWhite, Mona Singh.
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[10.1101/2022.10.21.513099]

Highly significant improvement of protein sequence alignments with AlphaFold2.
Athanasios Baltzis, Leila Mansouri, Suzanne Jin, Björn E Langer, Ionas Erb, Cedric Notredame.
Bioinformatics, September 2022.
[doi.org/10.1093/bioinformatics/btac625]

GO Bench: Shared-hub for Universal Benchmarking of Machine Learning-Based Protein Functional Annotations.
Andrew Dickson, Ehsaneddin Asgari, Alice C. McHardy, Mohammad R.K. Mofrad.
Preprint, July 2022.
[10.1101/2022.07.19.500685]

SETH predicts nuances of residue disorder from protein embeddings.
Dagmar Ilzhoefer, Michael Heinzinger, Burkhard Rost.
Preprint, June 2022.
[10.1101/2022.06.23.497276]

TSignal: A transformer model for signal peptide prediction.
Alexandru Dumitrescu, Emmi Jokinen, Juho Kellosalo, Ville Paavilainen, Harri Lähdesmäki.
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[10.1101/2022.06.02.493958]

TMbed – Transmembrane proteins predicted through Language Model embeddings.
Michael Bernhofer, Burkhard Rost.
Preprint, June 2022.
[10.1101/2022.06.12.495804]

Contrastive learning on protein embeddings enlightens midnight zone at lightning speed.
Michael Heinzinger, Maria Littmann, Ian Sillitoe, Nicola Bordin, Christine Orengo, Burkhard Rost.
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[10.1101/2021.11.14.468528]

SignalP 6.0 achieves signal peptide prediction across all types using protein language models.
Felix Teufel, José Juan Almagro Armenteros, Alexander Rosenberg Johansen, Magnús Halldór Gíslason, Silas Irby Pihl,Konstantinos D. Tsirigos,Ole Winther, Søren Brunak,Gunnar von Heijne, Henrik Nielsen. Preprint, July 2021.
[10.1101/2021.06.09.447770]

Convolutional neural networks with image representation of amino acid sequences for protein function prediction.
Samia Tasnim Sara, Md Mehedi Hasan, Ahsan Ahmada, Swakkhar Shatabda.
Computational Biology and Chemistry, June 2021.
[10.1016/j.compbiolchem.2021.107494]

Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures. Pedro Hermosilla, Marco Schäfer, Matěj Lang, Gloria Fackelmann, Pere Pau Vázquez, Barbora Kozlíková, Michael Krone, Tobias Ritschel, Timo Ropinski.
Preprint, April 2021.
[arxiv]

Embeddings from deep learning transfer GO annotations beyond homology.
Maria Littmann, Michael Heinzinger, Christian Dallago, Tobias Olenyi, Burkhard Rost.
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[10.1101/2020.09.04.282814]

Structure-Based Protein Function Prediction using Graph Convolutional Networks.
Vladimir Gligorijevic, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, Ian M. Fisk, Hera Vlamakis, Ramnik J. Xavier, Rob Knight, Kyunghyun Cho, Richard Bonneau.
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[10.1101/786236]

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function.
Amelia Villegas-Morcillo, Stavros Makrodimitris, Roeland van Ham, Angel M. Gomez, Victoria Sanchez, Marcel Reinders.
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[10.1101/2020.04.07.028373]

Machine Learning Predicts New Anti-CRISPR Proteins.
Gavin J. Knott, Jennifer A. Doudna, Fayyaz ul Amir Afsar Minhas.
Preprint, November 2019.
[10.1101/854950]

Improving protein function prediction with synthetic feature samples created by generative adversarial networks.
Cen Wan, David T. Jones.
Preprint, August 2019.
[10.1101/730143]

Universal Deep Sequence Models for Protein Classification.
Nils Strodthoff, Patrick Wagner, Markus Wenzel, Wojciech Samek.
Preprint, July 2019.
[10.1101/704874]

Critiquing Protein Family Classification Models Using Sufficient Input Subsets.
Brandon Carter, Maxwell L. Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy J. Colwell.
Preprint, June 2019.
[10.1101/674119] [bioRxiv]

A Brief History of Protein Sorting Prediction.
Henrik Nielsen, Konstantinos D. Tsirigos, Søren Brunak, Gunnar von Heijne.
The Protein Journal, May 2019.
[10.1007/s10930-019-09838-3]

DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.
Ahmet Sureyya Rifaioglu, Tunca Dogan, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay.
Scientific Reports, May 2019.
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Using Deep Learning to Annotate the Protein Universe.
Maxwell L. Bileschi, David Belanger, Drew Bryant, Theo Sanderson, Brandon Carter, D. Sculley, Mark A. DePristo, Lucy J. Colwell�.
Preprint, May 2019.
[10.1101/626507] [bioRxiv]

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.
Alperen Dalkiran, Ahmet Sureyya Rifaioglu, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay, Tunca Dogan.
BMC Bioinformatics, September 2018.
[10.1186/s12859-018-2368-y]

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.
Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf.
Bioinformatics, February 2018.
[10.1093/bioinformatics/btx624]

Near perfect protein multi-label classification with deep neural networks.
Balázs Szalkaia, Vince Grolmuszab.
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Large‐scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants.
Ahmet Sureyya Rifaioglu, Tunca Dogan, Omer Sinan Sarac, Tulin Ersahin, Rabie Saidi, Mehmet Volkan Atalay, Maria Jesus Martin, Rengul Cetin‐Atalay.
Proteins, November 2017.
[10.1002/prot.25416]

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.
Renzhi Cao, Colton Freitas, Leong Chan, Miao Sun, Haiqing Jiang, Zhangxin Chen.
Molecules, October 2017.
[10.3390/molecules22101732]

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.
Ehsaneddin Asgari, Mohammad R. K. Mofrad
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A structural alignment kernel for protein structures.
Jian Qiu, Martial Hue, Asa Ben-Hur, Jean-Philippe Vert, William Stafford Noble
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The spectrum kernel: A string kernel for SVM protein classification.
Christina S Leslie, Eleazar Eskin, William Stafford Noble.
Pacific Symposium on Biocomputing, January 2002.
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A universal framework for accurate and efficient geometric deep learning of molecular systems.
Shuo Zhang, Yang Liu, Lei Xie.
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Pairing interacting protein sequences using masked language modeling.
Umberto Lupo, Damiano Sgarbossa, Anne-Florence Bitbol.
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[10.1101/2023.08.14.553209]

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.
Rohan Gorantla, Ažbeta Kubincová, Andrea Y. Weiße, Antonia S. J. S. Mey.
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[10.1101/2023.08.01.551483]

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.
Leslie Salas-Estrada, Davide Provasi, Xing Qiu, Husnu Ümit Kaniskan, Xi-Ping Huang, Jeffrey F. DiBerto, João Marcelo Lamim Ribeiro, Jian Jin, Bryan L. Roth, and Marta Filizola.
Journal of Chemical Informatics and Modeling, August 2023.
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Sequence-Based Nanobody-Antigen Binding Prediction.
Usama Sardar, Sarwan Ali, Muhammad Sohaib Ayub, Muhammad Shoaib, Khurram Bashir, Imdad Ullah Khan, Murray Patterson.
Preprint, July 2023.
[arxiv]

Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries.
Lin Li, Esther Gupta, John Spaeth, Leslie Shing, Rafael Jaimes, Emily Engelhart, Randolph Lopez, Rajmonda S. Caceres, Tristan Bepler & Matthew E. Walsh.
Nature Communications, June 2023.
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Matching receptor to odorant with protein language and graph neural networks.
Matej Hladiš, Maxence Lalis, Sebastien Fiorucci, Jérémie Topin.
ICLR, May 2023.
ICLR

A general model to predict small molecule substrates of enzymes based on machine and deep learning.
Alexander Kroll, Sahasra Ranjan, Martin K. M. Engqvist & Martin J. Lercher.
Nature Communications, May 2023.
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De novo design of protein interactions with learned surface fingerprints.
Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlène Raclot, Alexandra Teslenko, Martin Pacesa, Stéphane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F. Gao, …Bruno E. Correia.
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[10.1038/s41586-023-05993-x]

PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.
Lucien F. Krapp, Luciano A. Abriata, Fabio Cortés Rodriguez & Matteo Dal Peraro.
Nature Communications, April 2023.
[10.1038/s41467-023-37701-8]

DRPBind: prediction of DNA, RNA and protein binding residues in intrinsically disordered protein sequences.
Ronesh Sharma, Tatsuhiko Tsunoda, Alok Sharma.
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[10.1101/2023.03.20.533427]

FlexVDW: A machine learning approach to account for protein flexibility in ligand docking.
Patricia Suriana, Joseph M. Paggi, Ron O. Dror.
Preprint, March 2023.
[arxiv]

End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins.
Tian Cai, Li Xie, Shuo Zhang, Muge Chen, Di He, Amitesh Badkul, Yang Liu, Hari Krishna Namballa, Michael Dorogan, Wayne W. Harding, Cameron Mura, Philip E. Bourne, Lei Xie.
PLOS Computational Biology, January 2023.
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Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules.
Rayane Achebouche, Anne Tromelin, Karine Audouze & Olivier Taboureau.
Scientific Reports, November 2022.
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DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding.
Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, Stan Z. Li.
Preprint, November 2022.
[arxiv]

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola.
Preprint, October 2022.
[arxiv]

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery.
Felix Wong, Aarti Krishnan, Erica J Zheng, Hannes Stärk, Abigail L Manson, Ashlee M Earl, Tommi Jaakkola, James J Collins.
Molecular Systems Biology, September 2022.
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Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models.
Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Anima Anandkumar.
Preprint, September 2022.
[arxiv]

Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model.
Yaqin Li, Lingli Li, Yongjin Xu, Yi Yu.
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[10.1101/2022.08.18.504370]

Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement.
Wengong Jin, Regina Barzilay, Tommi Jaakkola.
Preprint, July 2022.
[arxiv]

Cross-Modality and Self-Supervised Protein Embedding for Compound–Protein Affinity and Contact Prediction.
Yuning You, Yang Shen.
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[10.1101/2022.07.18.500559]

EvoBind: in silico directed evolution of peptide binders with AlphaFold.
Patrick Bryant, Arne Elofsson.
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[10.1101/2022.07.23.501214]

BepiPred-3.0: Improved B-cell epitope prediction using protein language models.
Joakim Clifford, Magnus Haraldson Høie, Morten Nielsen, Sebastian Deleuran, Bjoern Peters, Paolo Marcatili.
Preprint, July 2022.
[10.1101/2022.07.11.499418]

Predicting the specific substrate for transmembrane transport proteins using BERT language model.
Sima Ataei, Gregory Butler.
Preprint, July 2022.
[10.1101/2022.07.23.501263]

Peptide binding specificity prediction using fine-tuned protein structure prediction networks.
Amir Motmaen, Justas Dauparas, Minkyung Baek, Mohamad H. Abedi, David Baker, Philip Bradley Preprint, July 2022.
[10.1101/2022.07.12.499365]

Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network.
Artur Meller, Michael Ward, Jonathan Borowsky, Jeffrey M. Lotthammer, Meghana Kshirsagar, Felipe Oviedo, Juan Lavista Ferres, Gregory R. Bowman Preprint, June 2022.
[10.1101/2022.06.28.497399]

Topsy-Turvy: integrating a global view into sequence-based PPI prediction.
Rohit Singh, Kapil Devkota, Samuel Sledzieski, Bonnie Berger, Lenore Cowen. Bioinformatics, July 2022.
[10.1093/bioinformatics/btac258]

Scaffolding protein functional sites using deep learning.
Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Joseph L. Watson, Karla M. Castro, Robert Ragotte, Amijai Saragovi, Lukas F. Milles, Minkyung Baek, Ivan Anishchenko, Wei Yang, Derrick R. Hicks, Marc Expòsit, Thomas Schlichthaerle, Jung-Ho Chun, Justas Dauparas, Nathaniel Bennett, Basile I. M. Wicky, Andrew Muenks, Frank DiMaio, Bruno Correia, Sergey Ovchinnikov and David Baker.
Science, July 2022.
[10.1126/science.abn2100]

The substrate scopes of enzymes: a general prediction model based on machine and deep learning. Alexander Kroll, Sahasra Ranjan, Martin K. M. Engqvist, Martin J. Lercher.
Preprint, May 2022.
[10.1101/2022.05.24.493213]

Machine learning modeling of family wide enzyme-substrate specificity screens.
Samuel Goldman, Ria Das, Kevin K. Yang, Connor W. Coley.
PLoS Computational Biology, February 2022.
[10.1371/journal.pcbi.1009853] The substrate scopes of enzymes: a general prediction model based on machine and deep learning Alexander Kroll, Sahasra Ranjan, Martin K. M. Engqvist, Martin J. Lercher.
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[10.1101/2022.05.24.493213]

AlphaFold encodes the principles to identify high affinity peptide binders.. Liwei Chang, Alberto Perez.
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Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes.
Joseph M. Paggi, Julia A. Belk, Scott A. Hollingsworth, Nicolas Villanueva, Alexander S. Powers, Mary J. Clark, Augustine G. Chemparathy, Jonathan E. Tynan, Thomas K. Lau, Roger K. Sunahara, and Ron O. Dror.
PNAS, December 2021.
[10.1073/pnas.2112621118]

AlphaFill: enriching the AlphaFold models with ligands and co-factors.
Maarten L Hekkelman, Ida de de Vries, Robbie P Joosten, Anastassis Perrakis.
Preprint, November 2021.
[10.1101/2021.11.26.470110]

Deep learning allows genome-scale prediction of Michaelis constants from structural features.
Alexander Kroll, Martin K. M. Engqvist, David Heckmann, Martin J. Lercher.
PLoS Biology, October 2021
[10.1371/journal.pbio.3001402]

Probing T-cell response by sequence-based probabilistic modeling.. Barbara Bravi, Vinod P. Balachandran, Benjamin D. Greenbaum, Aleksandra M. Walczak, Thierry Mora, Rémi Monasson, Simona Cocco.
PLOS Computational Biology, September 2021.
[10.1371/journal.pcbi.1009297]

Biologically relevant transfer learning improves transcription factor binding prediction.
Gherman Novakovsky, Manu Saraswat, Oriol Fornes, Sara Mostafavi & Wyeth W. Wasserman.
Genome Biology, September 2021.
[10.1186/s13059-021-02499-5]

Deep learning based kcat prediction enables improved enzyme constrained model reconstruction.
Feiran Li, Le Yuan, Hongzhong Lu, Gang Li, Yu Chen, Martin K. M. Engqvist, Eduard J Kerkhoven, Jens Nielsen.
Preprint, August 2021
[10.1101/2021.08.06.455417 ]

A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction.. Philippe A. Robert,Rahmad Akbar,Robert Frank,Milena Pavlović,Michael Widrich,Igor Snapkov,Maria Chernigovskaya,Lonneke Scheffer,Andrei Slabodkin,Brij Bhushan Mehta, Mai Ha Vu, Aurél Prósz, Krzysztof Abram, Alex Olar,Enkelejda Miho, Dag Trygve Tryslew Haug,Fridtjof Lund-Johansen,Sepp Hochreiter, Ingrid Hobæk Haff,Günter Klambauer,Geir K. Sandve,Victor Greiff.
Preprint, July 2021.
[10.1101/2021.07.06.451258]

Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction.
Shuai Lu, Yuguang Li, Fei Wang, Xiaofei Nan, Shoutao Zhang.

• IEEE/ACM Transactions on Computational Biology and Bioinformatics, May 2021.*
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Neural message passing for joint paratope-epitope prediction.
Alice Del Vecchio, Andreea Deac, Pietro Liò, Petar Veličković.
Preprint, May 2021.
[arxiv]

Interpreting Neural Networks for Biological Sequences by Learning Stochastic Masks.
Johannes Linder, Alyssa La Fleur, Zibo Chen, Ajasja Ljubetič, David Baker, Sreeram Kannan, Georg Seelig.
Preprint, April 2021.
[10.1101/2021.04.29.441979]

GraphProt2: A graph neural network-based method for predicting binding sites of RNA-binding proteins.
Michael Uhl, Van Dinh Tran, Florian Heyl, Rolf Backofen.
Preprint, March 2021.
[10.1101/850024]

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification.
Daniel R. Ripoll, Sidhartha Chaudhury, Anders Wallqvist.
PLOS Computational Biology, March 2021.
[10.1371/journal.pcbi.1008864]

A multitask transfer learning framework for novel virus-human protein interactions.
Ngan Thi Dong, Megha Khosla.
Preprint, March 2021.
[10.1101/2021.03.25.437037]

EGRET: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction.
Sazan Mahbub, Md Shamsuzzoha Bayzid.
Preprint, February 2021.
[10.1101/2020.11.07.372466]

Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework.
Varun S. Sharma, Andrea Fossati, Rodolfo Ciuffa, Marija Buljan, Evan G. Williams, Zhen Chen, Wenguang Shao, Patrick G. A. Pedrioli, Anthony W. Purcell, María Rodríguez Martínez, … Chen Li. Preprint, February 2021.
[10.1101/2021.02.24.432789]

Capsule network for protein ubiquitination site prediction.
Qiyi Huang, Jiulei Jiang, Yin Luo, Weimin Li, Ying Wang.
Preprint, January 2021.
[10.1101/2021.01.07.425697]

Accurate neoantigen prediction depends on mutation position relative to patient allele-specific MHC anchor location.
Huiming Xia, Joshua F. McMichael, Suangson Supabphol, Megan M. Richters, Anamika Basu, Cody A. Ramirez, Cristina Puig-Saus, Kelsy C. Cotto, Jasreet Hundal, Susanna Kiwala, … Malachi Griffith.
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[10.1101/2020.12.08.416271]

DeepPurpose: a deep learning library for drug–target interaction prediction.
Kexin Huang, Tianfan Fu, Lucas M. Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun.
Bioinformatics, December 2020.
[10.1093/bioinformatics/btaa1005]

Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods.
Sanni Voutilainen, Markus Heinonen, Martina Andberg, Emmi Jokinen, Hannu Maaheimo, Johan Pääkkönen, Nina Hakulinen, Juha Rouvinen, Harri Lähdesmäki, Samuel Kaski, Juho Rousu, Merja Penttilä & Anu Koivula.
Applied Microbiology and Biotechnology, November 2020.
[10.1007/s00253-020-10960-x]

BERTMHC: Improves MHC-peptide class II interaction prediction with transformer and multiple instance learning.
Jun Cheng, Kaïdre Bendjama, Karola Rittner, Brandon Malone.
Preprint, November 2020.
[10.1101/2020.11.24.396101]

Predicting Cell-Penetrating Peptides: Building and Interpreting Random Forest based prediction Models.
Shilpa Yadahalli, Chandra S. Verma.
Preprint, October 2020.
[10.1101/2020.10.15.341149]

Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions.
Mayank Baranwal, Abram Magner, Jacob Saldinger, Emine S. Turali-Emre, Shivani Kozarekar, Paolo Elvati, J. Scott VanEpps, Nicholas A. Kotov, Angela Violi, Alfred O. Hero.
Preprint, September 2020.
[10.1101/2020.09.17.301200]

Predicting antigen specificity of single T cells based on TCR CDR3 regions.
David S Fischer, Yihan Wu, Benjamin Schubert, Fabian J Theis.
Molecular Systems Biology, August 2020.
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EpiDope: A Deep neural network for linear B-cell epitope prediction.
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Using Single Protein/Ligand Binding Models to Predict Active Ligands for Previously Unseen Proteins.
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Assessing the performance of protein regression models.
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Flattening the curve - How to get better results with small deep-mutational-scanning datasets.
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PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.
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PRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model.
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Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions.
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AllerStat: Finding Statistically Significant Allergen-Specific Patterns in Protein Sequences by Machine Learning.
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A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics.
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Predicting and interpreting large scale mutagenesis data using analyses of protein stability and conservation.
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Machine learning differentiates enzymatic and non-enzymatic metals in proteins.
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Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations.
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Predicting enzymatic reactions with a molecular transformer.
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On the sparsity of fitness functions and implications for learning.
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Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images. Thierry Pécot, Maria C. Cuitiño, Roger H. Johnson, Cynthia Timmers, Gustavo Leone.
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Light Attention Predicts Protein Location from the Language of Life.
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Positional SHAP (PoSHAP) for Interpretation of Machine Learning Models Trained from Biological Sequences.
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Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2.
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Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.
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DeepPSC (protein structure camera): computer vision-based reconstruction of proteins backbone structure from alpha carbon trace as a case study.
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TransINT: an interface-based prediction of membrane protein-protein interactions.
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DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.
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ProtTox: Toxin identification from Protein Sequences.
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Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness.
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Classifying protein structures into folds by convolutional neural networks, distance maps, and persistent homology.
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Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.
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Extraction of Protein Dynamics Information Hidden in Cryo-EM Map Using Deep Learning.
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Transformer neural network for protein specific de novo drug generation as machine translation problem.
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Iterative Peptide Modeling With Active Learning And Meta-Learning.
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BCrystal: an interpretable sequence-based protein crystallization predictor.
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Using machine learning to predict organismal growth temperatures from protein primary sequences.
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SolXplain: An Explainable Sequence-Based Protein Solubility Predictor.
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Develop machine learning-based regression predictive models for engineering protein solubility.
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DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction.
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