ArticleMolecular biology and evolution2018
Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function.
Article in Molecular biology and evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Mechanisms of Phenotypic Evolution from Molecules to Organisms: Integrating Evolutionary Biochemistry and Evolutionary Developmental Biology.Annual review of ecology, evolution, and systematics · 2026Article
- Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness.bioRxiv : the preprint server for biology · 2026Article
- A simple model captures key characteristics of biological non-deterministic genotype-phenotype maps.PLoS computational biology · 2026Article
- Seven complete comparative maps of allosteric mutations in a protein family.Nature communications · 2026Article
- PoolParty: streamlined design of DNA sequence libraries in Python.bioRxiv : the preprint server for biology · 2026Article
- Origins and breadth of pairwise epistasis in an α-helix of β-lactamase TEM-1.Nature communications · 2026Article
- D-LIM: A neural network for interpretable gene-gene interactions.PLoS computational biology · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- The allosteric landscape of the Src kinase.Science advances · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- Epistatic drift in protein evolution.Current opinion in genetics & development · 2025Review
- Distinguishing direct interactions from global epistasis using rank statistics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Considering Metabolic Context in Enzyme Evolution and Design.Biochemistry · 2025Review
- A Thermodynamic Cycle to Predict the Competitive Inhibition Outcomes of an Evolving Enzyme.Journal of chemical theory and computation · 2025Article
- A thermodynamic cycle to predict the competitive inhibition outcomes of an evolving enzyme.bioRxiv : the preprint server for biology · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.Genome biology · 2024Article
- The origin of mutational epistasis.European biophysics journal : EBJ · 2024Article
- The genetic architecture of protein stability.Nature · 2024Article
- Emergent time scales of epistasis in protein evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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1 author.
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Abstract
Understanding the relationship between protein sequence, function, and stability is a fundamental problem in biology. The essential function of many proteins that fold into a specific structure is their ability to bind to a ligand, which can be assayed for thousands of mutated variants. However, binding assays do not distinguish whether mutations affect the stability of the binding interface or the overall fold. Here, we introduce a statistical method to infer a detailed energy landscape of how a protein folds and binds to a ligand by combining information from many mutated variants. We fit a thermodynamic model describing the bound, unbound, and unfolded states to high quality data of protein G domain B1 binding to IgG-Fc. We infer distinct folding and binding energies for each mutation providing a detailed view of how mutations affect binding and stability across the protein. We accurately infer the folding energy of each variant in physical units, validated by independent data, whereas previous high-throughput methods could only measure indirect changes in stability. While we assume an additive sequence-energy relationship, the binding fraction is epistatic due its nonlinear relation to energy. Despite having no epistasis in energy, our model explains much of the observed epistasis in binding fraction, with the remaining epistasis identifying conformationally dynamic regions.
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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.