ArticlePLoS computational biology2012
Correlated electrostatic mutations provide a reservoir of stability in HIV protease.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- In vivo functional phenotypes from a computational epistatic model of evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Contingency and Entrenchment of Drug-Resistance Mutations in HIV Viral Proteins.The journal of physical chemistry. B · 2022Article
- Article
- The generative capacity of probabilistic protein sequence models.Nature communications · 2021Article
- Mechanistic analysis of A46V, H57Y, and D129N in pyrazinamidase associated with pyrazinamide resistance.Saudi journal of biological sciences · 2020Article
- Article
- Molecular Determinants of Epistasis in HIV-1 Protease: Elucidating the Interdependence of L89V and L90M Mutations in Resistance.Biochemistry · 2019Article
- Structural Dynamics Behind Clinical Mutants of PncA-Asp12Ala, Pro54Leu, and His57Pro ofFrontiers in bioengineering and biotechnology · 2019Article
- Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.Biophysical journal · 2018Article
- Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease.Molecular biology and evolution · 2017Article
- Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.Current opinion in structural biology · 2017Review
- Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations.Current opinion in structural biology · 2017Review
- Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.Protein science : a publication of the Protein Society · 2016Article
- Constructing sequence-dependent protein models using coevolutionary information.Protein science : a publication of the Protein Society · 2016Article
- Mutations Proximal to Sites of Autoproteolysis and the α-Helix That Co-evolve under Drug Pressure Modulate the Autoprocessing and Vitality of HIV-1 Protease.Biochemistry · 2015Article
- First Passage Times, Lifetimes, and Relaxation Times of Unfolded Proteins.Physical review letters · 2015Article
- Deep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and protease.PLoS computational biology · 2015Article
- Global quantitative modeling of chromatin factor interactions.PLoS computational biology · 2014Article
- Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
HIV protease, an aspartyl protease crucial to the life cycle of HIV, is the target of many drug development programs. Though many protease inhibitors are on the market, protease eventually evades these drugs by mutating at a rapid pace and building drug resistance. The drug resistance mutations, called primary mutations, are often destabilizing to the enzyme and this loss of stability has to be compensated for. Using a coarse-grained biophysical energy model together with statistical inference methods, we observe that accessory mutations of charged residues increase protein stability, playing a key role in compensating for destabilizing primary drug resistance mutations. Increased stability is intimately related to correlations between electrostatic mutations - uncorrelated mutations would strongly destabilize the enzyme. Additionally, statistical modeling indicates that the network of correlated electrostatic mutations has a simple topology and has evolved to minimize frustrated interactions. The model's statistical coupling parameters reflect this lack of frustration and strongly distinguish like-charge electrostatic interactions from unlike-charge interactions for [Formula: see text] of the most significantly correlated double mutants. Finally, we demonstrate that our model has considerable predictive power and can be used to predict complex mutation patterns, that have not yet been observed due to finite sample size effects, and which are likely to exist within the larger patient population whose virus has not yet been sequenced.
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