ArticleProtein science : a publication of the Protein Society2016
Constructing sequence-dependent protein models using coevolutionary information.
Article in Protein science : a publication of the Protein Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Disease-relevant mutations alter amino acid co-evolution networks in the second nucleotide binding domain of CFTR.PloS one · 2020Article
- Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.Biophysical journal · 2018Article
- Inferring repeat-protein energetics from evolutionary information.PLoS computational biology · 2017Article
- Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.Current opinion in structural biology · 2017Review
- Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes.Molecular biology and evolution · 2016Article
- Structural propensities of kinase family proteins from a Potts model of residue co-variation.Protein science : a publication of the Protein Society · 2016Article
- SMOG 2: A Versatile Software Package for Generating Structure-Based Models.PLoS computational biology · 2016Article
- Sequence co-evolutionary information is a natural partner to minimally-frustrated models of biomolecular dynamics.F1000Research · 2016Review
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4 authors.
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Abstract
Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid sites within the multiple sequence alignment of a protein family. Here, we use the maximum entropy-based approach called mean field Direct Coupling Analysis (mfDCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family. We use the inferred pairwise statistical couplings to generate the sequence-dependent heterogeneous interaction energies of a structure-based model (SBM) where only native contacts are considered. Considering the ribosomal S6 protein and its circular permutants as well as the SH3 protein, we demonstrate that these models quantitatively agree with experimental data on folding mechanisms. This work serves as a new framework for generating coevolutionary data-enriched models that can potentially be used to engineer key functional motions and novel interactions in protein systems.
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