ArticlePLoS computational biology2013
Computational protein design quantifies structural constraints on amino acid covariation.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Ensemble-conditioned protein sequence design with Caliby.bioRxiv : the preprint server for biology · 2025Article
- PDBench: evaluating computational methods for protein-sequence design.Bioinformatics (Oxford, England) · 2023Article
- State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold.Physical review letters · 2022Article
- DE-STRESS: a user-friendly web application for the evaluation of protein designs.Protein engineering, design & selection : PEDS · 2021Article
- Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions.Proteins · 2020Article
- Beyond Thermodynamic Constraints: Evolutionary Sampling Generates Realistic Protein Sequence Variation.Genetics · 2018Article
- Biophysical Models of Protein Evolution: Understanding the Patterns of Evolutionary Sequence Divergence.Annual review of biophysics · 2017Review
- An Evolution-Based Approach to De Novo Protein Design.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Computational Redesign of Thioredoxin Is Hypersensitive toward Minor Conformational Changes in the Backbone Template.Journal of molecular biology · 2016Article
- The Gearbox of the Bacterial Flagellar Motor Switch.Structure (London, England : 1993) · 2016Article
- Intermediate divergence levels maximize the strength of structure-sequence correlations in enzymes and viral proteins.Protein science : a publication of the Protein Society · 2016Article
- Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics.PLoS computational biology · 2016Review
- Causes of evolutionary rate variation among protein sites.Nature reviews. Genetics · 2016Review
- Liquid-theory analogy of direct-coupling analysis of multiple-sequence alignment and its implications for protein structure prediction.Biophysics and physicobiology · 2015Article
- Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity.PLoS computational biology · 2015Article
- A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design.PloS one · 2015Article
- Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design.Journal of molecular evolution · 2014Article
- Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Multidimensional mutual information methods for the analysis of covariation in multiple sequence alignments.BMC bioinformatics · 2014Article
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2 authors.
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Abstract
Amino acid covariation, where the identities of amino acids at different sequence positions are correlated, is a hallmark of naturally occurring proteins. This covariation can arise from multiple factors, including selective pressures for maintaining protein structure, requirements imposed by a specific function, or from phylogenetic sampling bias. Here we employed flexible backbone computational protein design to quantify the extent to which protein structure has constrained amino acid covariation for 40 diverse protein domains. We find significant similarities between the amino acid covariation in alignments of natural protein sequences and sequences optimized for their structures by computational protein design methods. These results indicate that the structural constraints imposed by protein architecture play a dominant role in shaping amino acid covariation and that computational protein design methods can capture these effects. We also find that the similarity between natural and designed covariation is sensitive to the magnitude and mechanism of backbone flexibility used in computational protein design. Our results thus highlight the necessity of including backbone flexibility to correctly model precise details of correlated amino acid changes and give insights into the pressures underlying these correlations.
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