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dc.contributor.authorMann, Jaclyn K.en_US
dc.contributor.authorBarton, John P.en_US
dc.contributor.authorFerguson, Andrew L.en_US
dc.contributor.authorOmarjee, Salehaen_US
dc.contributor.authorWalker, Bruce D.en_US
dc.contributor.authorChakraborty, Arupen_US
dc.contributor.authorNdung'u, Thumbien_US
dc.date.accessioned2014-09-08T15:36:00Z
dc.date.issued2014en_US
dc.identifier.citationMann, Jaclyn K., John P. Barton, Andrew L. Ferguson, Saleha Omarjee, Bruce D. Walker, Arup Chakraborty, and Thumbi Ndung'u. 2014. “The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing.” PLoS Computational Biology 10 (8): e1003776. doi:10.1371/journal.pcbi.1003776. http://dx.doi.org/10.1371/journal.pcbi.1003776.en
dc.identifier.issn1553-734Xen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12785822
dc.description.abstractViral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model), generalizing our previous approach (Ising model) that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these) predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = −0.74, p = 3.6×10−6) are strongly correlated, and this was further strengthened in the regularized Ising model (r = −0.83, p = 3.7×10−12). Performance of the Potts model (r = −0.73, p = 9.7×10−9) was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion, and harnessing this knowledge for immunogen design.en
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofdoi:10.1371/journal.pcbi.1003776en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125067/pdf/en
dash.licenseLAAen_US
dc.subjectBiology and Life Sciencesen
dc.subjectComputational Biologyen
dc.subjectGeneticsen
dc.subjectImmunologyen
dc.subjectMicrobiologyen
dc.subjectMedical Microbiologyen
dc.subjectMicrobial Pathogensen
dc.subjectViral Pathogensen
dc.subjectImmunodeficiency Virusesen
dc.subjectHIVen
dc.subjectMedicine and Health Sciencesen
dc.subjectInfectious Diseasesen
dc.subjectViral Diseasesen
dc.titleThe Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testingen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPLoS Computational Biologyen
dash.depositing.authorWalker, Bruce D.en_US
dc.date.available2014-09-08T15:36:00Z
dc.identifier.doi10.1371/journal.pcbi.1003776*
dash.contributor.affiliatedWalker, Bruce


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