ArticleHuman genetics2025
Structure-informed protein language models are robust predictors for variant effects.
Article in Human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- SPPIDER-seq: sequence-based partner-aware predictor of protein-protein interaction sites.Bioinformatics (Oxford, England) · 2026Article
- Scaling SMILES-based chemical language models for therapeutic peptide engineering.bioRxiv : the preprint server for biology · 2026Article
- SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites.bioRxiv : the preprint server for biology · 2026Article
- MAVISp: A modular structure-based framework for protein variant effects.Protein science : a publication of the Protein Society · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Article
- Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation.bioRxiv : the preprint server for biology · 2025Article
- Integrated antibody language model accelerates IgG screening and design for broad-spectrum antiviral therapy.iScience · 2025Article
- PLMFit: benchmarking transfer learning with protein language models for protein engineering.Briefings in bioinformatics · 2025Article
- Leveraging protein structural information to improve variant effect prediction.Current opinion in structural biology · 2025Review
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.Human genetics · 2025Article
- Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.Human genetics · 2025Article
- Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges.Human genetics · 2025Article
- Assessing the predicted impact of single amino acid substitutions in MAPK proteins for CAGI6 challenges.Human genetics · 2025Article
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.Research square · 2024Article
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2 authors.
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Abstract
Emerging variant effect predictors, protein language models (pLMs) learn evolutionary distribution of functional sequences to capture fitness landscape. Considering that variant effects are manifested through biological contexts beyond sequence (such as structure), we first assess how much structure context is learned in sequence-only pLMs and affecting variant effect prediction. And we establish a need to inject into pLMs protein structural context purposely and controllably. We thus introduce a framework of structure-informed pLMs (SI-pLMs), by extending masked sequence denoising to cross-modality denoising for both sequence and structure. Numerical results over deep mutagenesis scanning benchmarks show that our SI-pLMs, even when using smaller models and less data, are robustly top performers against competing methods including other pLMs, which shows that introducing biological context can be more effective at capturing fitness landscape than simply using larger models or bigger data. Case studies reveal that, compared to sequence-only pLMs, SI-pLMs can be better at capturing fitness landscape because (a) learned embeddings of low/high-fitness sequences can be more separable and (b) learned amino-acid distributions of functionally and evolutionarily conserved residues can be of much lower entropy, thus much more conserved, than other residues. Our SI-pLMs are applicable to revising any sequence-only pLMs through model architecture and training objectives. They do not require structure data as model inputs for variant effect prediction and only use structures as context provider and model regularizer during training.
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Registered trials
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.