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

By Andrew L. Ferguson, Jaclyn K. Mann, Saleha Omarjee, Thumbi Ndung'u, Bruce D. Walker, Arup K. Chakraborty. 2013

240 citations recorded by the source corpus.

Summary A prophylactic or therapeutic vaccine offers the best hope to curb the HIV-AIDS epidemic gripping sub-Saharan Africa, but 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.

Subfields: Genetics, Molecular Biology, Virology

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “One key issue in the description of a mutational landscape is to understand how much the effect of a mutation depends on the genetic background in which it appears [3, 8, 9].”
The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing (2014, methodology): “To fit the Potts model, we implemented a generalization of the semi-analytical extension of the iterative gradient descent implemented by Mora and Bialek [11,17].”
Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes (2013, unknown): “Answering these questions requires knowledge of the complex, multidimensional structure of HIV’s fitness landscape [8, 9] - a measure of the virus’ replicative ∗ Corresponding author; kardar@mit.edu † Corresponding author; arupc@mit.edu capacity as a function of the amino acid sequence of its consti”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “In a series of papers, Chakraborty and co-workers proposed a ﬁtness model for proteins in the Gag family of the HIV virus [55, 70, 122, 187].”
Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, background): “Two separate studies, which focused on antibiotic drug resistance in E. coli (Figliuzzi et al. 2016) and viral fitness of HIV-1 proteins (Ferguson et al. 2013), respectively, inferred a Potts model (i.e., additive fitness landscape), H , and calculated mutational changes as D H .”