Translating HIV Sequences into Quantitative Fitness Landscapes Predicts Viral Vulnerabilities for Rational Immunogen Design

Andrew L. Ferguson; Jaclyn K. Mann; Saleha Omarjee; Thumbi Ndung'u; Bruce D. Walker; Arup K. Chakraborty

doi:10.1016/j.immuni.2012.11.022

Translating HIV Sequences into Quantitative Fitness Landscapes Predicts Viral Vulnerabilities for Rational Immunogen Design

Andrew L. Ferguson
, Jaclyn K. Mann
, Saleha Omarjee
, Thumbi Ndung'u
, Bruce D. Walker
, Arup K. Chakraborty

Research output: Contribution to journal › Article › peer-review

181 Scopus citations

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.

Original language	English
Pages (from-to)	606-617
Number of pages	12
Journal	Immunity
Volume	38
Issue number	3
DOIs	https://doi.org/10.1016/j.immuni.2012.11.022
State	Published - 21 Mar 2013
Externally published	Yes

Bibliographical note

Funding Information:
We thank Todd Allen, Herman Eisen, and John Barton for fruitful discussions. Financial support was provided by the Ragon Institute (B.D.W., A.K.C., J.K.M., S.O., T.N.), a National Institutes of Health Director’s Pioneers Award (A.K.C.), NIH Award AI30914 (B.D.W.), the Howard Hughes Medical Institute (B.D.W., T.N.), the South African Department of Science and Technology/National Research Foundation Research Chair Initiative (J.K.M., S.O., T.N.), and a Ragon Postdoctoral Fellowship (A.L.F.).

Funding

We thank Todd Allen, Herman Eisen, and John Barton for fruitful discussions. Financial support was provided by the Ragon Institute (B.D.W., A.K.C., J.K.M., S.O., T.N.), a National Institutes of Health Director’s Pioneers Award (A.K.C.), NIH Award AI30914 (B.D.W.), the Howard Hughes Medical Institute (B.D.W., T.N.), the South African Department of Science and Technology/National Research Foundation Research Chair Initiative (J.K.M., S.O., T.N.), and a Ragon Postdoctoral Fellowship (A.L.F.).

Funders	Funder number
National Institutes of Health	AI30914
Howard Hughes Medical Institute
National Institute of Allergy and Infectious Diseases	UM1AI100663
Ragon Institute of MGH, MIT and Harvard
Department of Science and Technology, Republic of South Africa

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1016/j.immuni.2012.11.022

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title = "Translating HIV Sequences into Quantitative Fitness Landscapes Predicts Viral Vulnerabilities for Rational Immunogen Design",

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.",

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note = "Funding Information: We thank Todd Allen, Herman Eisen, and John Barton for fruitful discussions. Financial support was provided by the Ragon Institute (B.D.W., A.K.C., J.K.M., S.O., T.N.), a National Institutes of Health Director{\textquoteright}s Pioneers Award (A.K.C.), NIH Award AI30914 (B.D.W.), the Howard Hughes Medical Institute (B.D.W., T.N.), the South African Department of Science and Technology/National Research Foundation Research Chair Initiative (J.K.M., S.O., T.N.), and a Ragon Postdoctoral Fellowship (A.L.F.). ",

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AB - 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.

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Translating HIV Sequences into Quantitative Fitness Landscapes Predicts Viral Vulnerabilities for Rational Immunogen Design

Abstract

Bibliographical note

Funding

UN SDGs

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