ArticlePLoS computational biology2012
Prediction of mutational tolerance in HIV-1 protease and reverse transcriptase using flexible backbone protein design.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- Different Mutation Tolerance of Lentiviral (HIV-1) and Deltaretroviral (BLV and HTLV) Protease Precursors.Viruses · 2022Article
- Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.PloS one · 2021Article
- Multi-state design of flexible proteins predicts sequences optimal for conformational change.PLoS computational biology · 2020Article
- Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions.Proteins · 2020Article
- Molecular Determinants of Epistasis in HIV-1 Protease: Elucidating the Interdependence of L89V and L90M Mutations in Resistance.Biochemistry · 2019Article
- Constrained Mutational Sampling of Amino Acids in HIV-1 Protease Evolution.Molecular biology and evolution · 2019Article
- Elucidating the Interdependence of Drug Resistance from Combinations of Mutations.Journal of chemical theory and computation · 2017Article
- Automated prediction of HIV drug resistance from genotype data.BMC bioinformatics · 2016Article
- Algorithms for protein design.Current opinion in structural biology · 2016Review
- Article
- Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations.PLoS computational biology · 2015Article
- Article
- New insights into the interdependence between amino acid stereochemistry and protein structure.Biophysical journal · 2013Article
- Computational protein design quantifies structural constraints on amino acid covariation.PLoS computational biology · 2013Article
- Serverification of molecular modeling applications: the Rosetta Online Server that Includes Everyone (ROSIE).PloS one · 2013Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Predicting which mutations proteins tolerate while maintaining their structure and function has important applications for modeling fundamental properties of proteins and their evolution; it also drives progress in protein design. Here we develop a computational model to predict the tolerated sequence space of HIV-1 protease reachable by single mutations. We assess the model by comparison to the observed variability in more than 50,000 HIV-1 protease sequences, one of the most comprehensive datasets on tolerated sequence space. We then extend the model to a second protein, reverse transcriptase. The model integrates multiple structural and functional constraints acting on a protein and uses ensembles of protein conformations. We find the model correctly captures a considerable fraction of protease and reverse-transcriptase mutational tolerance and shows comparable accuracy using either experimentally determined or computationally generated structural ensembles. Predictions of tolerated sequence space afforded by the model provide insights into stability-function tradeoffs in the emergence of resistance mutations and into strengths and limitations of the computational model.
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