Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
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Updated
Jul 23, 2024 - Python
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
Optimizing AlphaFold Training and Inference on GPU Clusters
[ICLR'24 spotlight] Saprot: Protein Language Model with Structural Alphabet
Trainable PyTorch framework for developing protein, RNA and complex models.
Predicting direct protein-protein interactions with AlphaFold deep learning neural network models.
Modified version of Alphafold to divide CPU part (MSA and template searching) and GPU part. This can accelerate Alphafold when predicting multiple structures
PyMOL extension to color AlphaFold structures by confidence (pLDDT).
Exploring Evolution-aware & free protein language models as protein function predictors
MMseqs2 app to run on your workstation or servers
FrameDiPT: an SE(3) diffusion model for protein structure inpainting
Protein 3D structure prediction pipeline
Local Interaction Score (LIS) Calculation from AlphaFold-Multimer (Enhanced Protein-Protein Interaction Discovery via AlphaFold-Multimer)
Run AlphaFold2 (and multimer) step by step
A curated list of awesome self-learning materials in Computational Structural Biology, such as sources, tutorials, etc.
Infrastructure template and Jupyter notebooks for running RoseTTAFold on AWS Batch.
Cryptic pocket prediction using AlphaFold 2
A tool for predicting the effects of missense mutations on protein stability changes upon missense mutation using protein sequence only. PROST uses colab AlhpaFold2 for the prediction of pdb struture from FASTA sequence.
This repository contains the AlphaCutter.py for the removal of non-globular regions from predicted protein structures.
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