VespaG is a blazingly fast single amino acid variant effect predictor, leveraging embeddings of the protein language model ESM-2 (Lin et al. 2022) as input to a minimal deep learning model.

To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from a subset of the Human proteome, which we then annotated using predictions from the multiple sequence alignment-based effect predictor GEMME (Laine et al. 2019) as a proxy for experimental scores.

Assessed on the ProteinGym (Notin et al. 2023) benchmark, VespaG matches state-of-the-art methods while being several orders of magnitude faster, predicting the entire single-site mutational landscape for a human proteome in under a half hour on a consumer-grade laptop.

More details on VespaG can be found in the corresponding preprint.

1. Install necessary dependencies: conda env create -f environment.yml
2. Run python -m vespag predict with the following options:
   Required:

• --input/-i: Path to FASTA-formatted file containing protein sequence(s).
  Optional:
• --output/-o:Path for saving created CSV and/or H5 files. Defaults to ./output.
• --embeddings/-e: Path to pre-generated ESM2 (esm2_t36_3B_UR50D) input embeddings. Embeddings will be generated from scratch if no path is provided and saved in ./output. Please note that embedding generation on CPU is extremely slow and not recommended.
• --mutation-file: CSV file specifying specific mutations to score. If not provided, the whole single-site mutational landscape of all input proteins will be scored.
• --id-map: CSV file mapping embedding IDs (first column) to FASTA IDs (second column) if they're different. Does not have to cover cases with identical IDs.
• --single-csv: Whether to return one CSV file for all proteins instead of a single file for each protein.
• --no-csv: Whether no CSV output should be produced.
• --h5-output: Whether a file containing predictions in HDF5 format should be created.
• --zero-idx: Whether to enumerate protein sequences (both in- and output) starting at 0.

After installing the dependencies above and cloning the VespaG repo, you can try out the following examples:

• Run VespaG without precomputed embeddings for the example fasta file with 3 sequences in data/example/example.fasta:
  • python -m vespag predict -i data/example/example.fasta. This will save a CSV file for each sequence in the folder ./output
• Run VespaG with precomputed embeddings for the example fasta file with 3 sequences in data/example/example.fasta:
  • python -m vespag predict -i data/example/example.fasta -e output/esm2_embeddings.h5 --single-csv. This will save a single CSV file for all sequences in the folder ./output

Kindly note that we are working on making data pre-processing, model training, and evaluation available in the public GitHub repository as soon as possible.

@article{vespag,
	author = {Celine Marquet and Julius Schlensok and Marina Abakarova and Burkhard Rost and Elodie Laine},
	title = {VespaG: Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction},
	year = {2024},
	doi = {10.1101/2024.04.24.590982},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/04/28/2024.04.24.590982},
	journal = {bioRxiv}}