ArticleScientific reports2024
Enhancing missense variant pathogenicity prediction with protein language models using VariPred.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Protocol for tri-modal pretraining and downstream fine-tuning of protein representations using ProteinAligner.STAR protocols · 2026Article
- Semantic embedding of variant effect annotations enables rapid and accurate pathogenicity prediction with VUS.Life.Scientific reports · 2026Article
- A tri-modal contrastive learning framework for protein representation learning.Cell reports methods · 2026Article
- Leveraging protein language models and a scoring function for indel characterization and transfer learning.Patterns (New York, N.Y.) · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- pLM-SAV: a Δ-embedding approach for predicting pathogenic single amino acid variants.Bioinformatics advances · 2026Article
- Article
- Protein language model identifies disordered, conserved motifs implicated in phase separation.eLife · 2025Article
- Prediction of pathogenic mutations in human transmembrane proteins and their associated diseases via utilizing pre-trained Bio-LLMs.Communications biology · 2025Article
- A Report of a Child with SEC31A-Related Neurodevelopmental Disorder.International journal of molecular sciences · 2025Article
- XGBMUT: Predicting the Functional Impact of Missense Mutations Using an Extreme Gradient Boost Classifier.ACS omega · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- PanEffect: a pan-genome visualization tool for variant effects in maize.Bioinformatics (Oxford, England) · 2024Article
- Accurate prediction of functional effect of single amino acid variants with deep learning.Computational and structural biotechnology journal · 2023Article
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Authors and funding
5 authors.
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Abstract
Computational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (Variant impact Predictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. Using one of the best-performing protein language models (ESM-1b), we establish a robust classifier that requires no calculation of structural features or multiple sequence alignments. We compare the performance of VariPred with other representative models including 3Cnet, Polyphen-2, REVEL, MetaLR, FATHMM and ESM variant. VariPred performs as well as, or in most cases better than these other predictors using six variant impact prediction benchmarks despite requiring only sequence data and no pre-processing of the data.
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