ArticleBioinformatics (Oxford, England)2024
Expert-guided protein language models enable accurate and blazingly fast fitness prediction.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- ProteoCast: a web server to predict, validate, and interpret missense variant effects.Journal of molecular biology · 2026Article
- Transformers for molecular property prediction: domain adaptation efficiently improves performance.Journal of cheminformatics · 2026Article
- SPPIDER-seq: sequence-based partner-aware predictor of protein-protein interaction sites.Bioinformatics (Oxford, England) · 2026Article
- SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites.bioRxiv : the preprint server for biology · 2026Article
- Proteome-wide prediction of the functional impact of missense variants with ProteoCast.Nature communications · 2026Article
- AI-enabled protein design facilitates future plant research and crop breeding.Plant physiology · 2026Review
- Compressing the collective knowledge of ESM into a single protein language model.Nature methods · 2026Article
- DEX: a consensus-based amino acid exchangeability measure for improved codon substitution modelling.bioRxiv : the preprint server for biology · 2026Article
- Inferring context-specific site variation with evotuned protein language models.NAR genomics and bioinformatics · 2026Article
- Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score Distributions.bioRxiv : the preprint server for biology · 2025Article
- Protein language model identifies disordered, conserved motifs implicated in phase separation.eLife · 2025Article
- AFToolkit: a framework for molecular modeling of proteins with AlphaFold-derived representations.Briefings in bioinformatics · 2025Article
- From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced model.Bioinformatics (Oxford, England) · 2025Article
- A generalized platform for artificial intelligence-powered autonomous enzyme engineering.Nature communications · 2025Article
- ProtMamba: a homology-aware but alignment-free protein state space model.Bioinformatics (Oxford, England) · 2025Article
- Residue conservation and solvent accessibility are (almost) all you need for predicting mutational effects in proteins.Bioinformatics (Oxford, England) · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationExhaustive 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.
resultsTo 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.