ArticleeLife2019
Learning protein constitutive motifs from sequence data.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed.
- Dual role of glycosylation in resistance to CD4-binding site broadly neutralizing antibodies.Journal of virology · 2026Article
- Constrained evolutionary funnels shape viral immune escape.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Predicting viral sensitivity to antibodies using genetic sequences and antibody similarities.PLoS computational biology · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
- Optimal sequencing depth for measuring the concentrations of molecular barcodes.Nucleic acids research · 2025Article
- Inferring protein-folding mechanisms from natural sequence diversity.Biophysical journal · 2025Article
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.Methods in molecular biology (Clifton, N.J.) · 2025Article
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- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Towards parsimonious generative modeling of RNA families.Nucleic acids research · 2024Article
- Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.National science review · 2023Review
- Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment.PLoS computational biology · 2023Article
- Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.bioRxiv : the preprint server for biology · 2023Article
- Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach.Journal of chemical theory and computation · 2023Article
- A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.eLife · 2023Article
- Generating interacting protein sequences using domain-to-domain translation.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Statistical analysis of evolutionary-related protein sequences provides information about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We here apply RBM to 20 protein families, and present detailed results for two short protein domains (Kunitz and WW), one long chaperone protein (Hsp70), and synthetic lattice proteins for benchmarking. The features inferred by the RBM are biologically interpretable: they are related to structure (residue-residue tertiary contacts, extended secondary motifs (α-helixes and β-sheets) and intrinsically disordered regions), to function (activity and ligand specificity), or to phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and 'turning up' or 'turning down' the different modes at will. Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype-phenotype relationship for protein families.
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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.