Expert-guided protein language models enable accurate and blazingly fast fitness prediction
Open accessPublished version of a preprint
Abstract
Motivation: Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast missense amino acid variant effect predictor, leveraging protein language model (pLM) embeddings as input to a minimal deep learning model. Results: To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. This setup increases interpretability compared to the baseline pLM and is easily retrainable with novel or updated pLMs. Assessed against the ProteinGym benchmark (217 multiplex assays of variant effect—MAVE—with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 ± 0.02, matching top-performing methods evaluated on the same data. VespaG has the advantage of being orders of magnitude faster, predicting all mutational landscapes of all proteins in proteomes such as Homo sapiens or Drosophila melanogaster in under 30 min on a consumer laptop (12-core CPU, 16 GB RAM). Availability and implementation: VespaG is available freely at https://github.com/jschlensok/vespag. The associated training data and predictions are available at https://doi.org/10.5281/zenodo.11085958.
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- Authors
- Marquet, C., Schlensok, J., Abakarova, M., Rost, B., & Laine, E.
- Affiliations
- Technical University of Munich (DE)
- Centre National de la Recherche Scientifique (FR)
- Laboratory of Computational and Quantitative Biology, UMR 7238, Sorbonne Université, CNRS, IBPS , Paris 75005,
- Corresponding author
- Marquet, Céline
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-11-01
- Volume
- 40
- Issue
- 11
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC11588025
- OpenAlex
- W4404633904
- Cited by
- 33
- References
- 13
- Citations by year
- 2026: 12 · 2025: 20 · 2012: 1
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- Funders
- German Ministry for Research and Education
- Deutsche Forschungsgemeinschaft (DFG-GZ: RO1320/4-1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein language models, protein language model embeddings, single amino acid variants, human proteome, Drosophila melanogaster, mutational landscape
- MeSH terms
- Deep Learning, Animals, Drosophila melanogaster, Humans, Proteins, Software, Computational Biology, Mutation, Missense, Proteome, Databases, Protein
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