|Title||Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Ferguson AL, Mann JK, Omarjee S, Ndung'u T, Walker BD, Chakraborty AK|
|Keywords||AIDS 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|
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.
|PubMed Central ID||PMC3728823|
|Grant List||AI30914 / 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
Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design.