ArticleNature communications2023
Discovering functionally important sites in proteins.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 81 citations in OpenAlex.
- Mechanistic Interpretability of Protein Language Models Reveals Encoded Structural and Functional Properties of Intrinsically Disordered Proteins.bioRxiv : the preprint server for biology · 2026Article
- Enhancing Enzyme Activity With Mutation Combinations Guided by Few-Shot Learning and Causal Inference.Angewandte Chemie (International ed. in English) · 2026Article
- The unconventional G protein AtYchF1 interacts with ribosomal protein AtRPS7 to modulate selective translation for balancing plant growth and stress responses in Arabidopsis.Plant communications · 2026Article
- Combining multiplexed functional data to improve variant classification.Genome medicine · 2026Article
- T2Pdecoder enables protein-centric analyses from transcriptomic data.Nature communications · 2026Article
- Article
- Proteome-wide prediction of the functional impact of missense variants with ProteoCast.Nature communications · 2026Article
- Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deconvolving mutation effects on protein stability and function with disentangled protein language models.bioRxiv : the preprint server for biology · 2026Article
- Probing the dark energy in the functional protein universe.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Unlocking Protein Evolution Insights: Efficient and Interpretable Mutational Effect Predictions with GEMME.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- Hepatitis E Virus ORF1 Polyprotein Harbors a Pocket-Like Cavity That Is Vital for Virus Replication and Represents a Novel Antiviral Target.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Integrating protein sequence design and evolutionary sequence conservation to uncover spectral tuning sites in red-light photoreceptors.Structure (London, England : 1993) · 2025Article
- Mapping cryptic phosphorylation sites in the human proteome.The EMBO journal · 2025Article
- Multi-feature fusion network with marginal focal dice loss for multi-label therapeutic peptide prediction.PLoS computational biology · 2025Article
- Approaches for regulating enzyme activities: Recent advances in experiment and computation.Current opinion in structural biology · 2025Review
- Saturation mapping of MUTYH variant effects using DNA repair reporters.American journal of human genetics · 2025Article
- Quantitative functional profiling of ERCC2 mutations deciphers cisplatin sensitivity in bladder cancer.The Journal of clinical investigation · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.
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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.