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Prediction of mutational tolerance in HIV-1 protease and reverse transcriptase using flexible backbone protein design.


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.

SUBMITTER: Humphris-Narayanan E 

PROVIDER: S-EPMC3426558 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Prediction of mutational tolerance in HIV-1 protease and reverse transcriptase using flexible backbone protein design.

Humphris-Narayanan Elisabeth E   Akiva Eyal E   Varela Rocco R   Ó Conchúir Shane S   Kortemme Tanja T  

PLoS computational biology 20120823 8


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 data  ...[more]

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