Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design.

TitleTranslating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design.
Publication TypeJournal Article
Year of Publication2013
AuthorsFerguson AL, Mann JK, Omarjee S, Ndung'u T, Walker BD, Chakraborty AK
JournalImmunity
Volume38
Issue3
Pagination606-17
Date Published03/21/2013
ISSN1097-4180
KeywordsAIDS Vaccines, Algorithms, Amino Acid Sequence, Binding Sites, Computational Biology, Drug Design, Epitopes, Gene Products, gag, HIV Infections, HIV-1, HLA-B Antigens, Humans, Models, Genetic, Models, Immunological, Mutation, Reproducibility of Results, Sequence Homology, Amino Acid, T-Lymphocytes, Cytotoxic
Abstract

A prophylactic or therapeutic vaccine offers the best hope to curb the HIV-AIDS epidemic gripping sub-Saharan Africa, but it remains elusive. A major challenge is the extreme viral sequence variability among strains. Systematic means to guide immunogen design for highly variable pathogens like HIV are not available. Using computational models, we have developed an approach to translate available viral sequence data into quantitative landscapes of viral fitness as a function of the amino acid sequences of its constituent proteins. Predictions emerging from our computationally defined landscapes for the proteins of HIV-1 clade B Gag were positively tested against new in vitro fitness measurements and were consistent with previously defined in vitro measurements and clinical observations. These landscapes chart the peaks and valleys of viral fitness as protein sequences change and inform the design of immunogens and therapies that can target regions of the virus most vulnerable to selection pressure.

DOI10.1016/j.immuni.2012.11.022
Alternate JournalImmunity
PubMed ID23521886
PubMed Central IDPMC3728823
Grant ListAI30914 / AI / NIAID NIH HHS / United States
P30 AI060354 / AI / NIAID NIH HHS / United States
R37 AI067073 / AI / NIAID NIH HHS / United States
UM1 AI100663 / AI / NIAID NIH HHS / United States
/ / Howard Hughes Medical Institute / United States
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