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PLoS Comput Biol. 2019 Apr 1;15(4):e1006952. doi: 10.1371/journal.pcbi.1006952. eCollection 2019 Apr.

Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features.

Author information

1
Vaccine and Infectious Disease Division and Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
2
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America.
3
Department of Biostatistics, University of Washington, Seattle, Washington, United States of America.
4
Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
5
Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
6
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.
7
Program in Chemical & Physical Biology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
8
Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
9
Division of Human Biology and Epidemiology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
10
Molecular and Cellular Biology PhD Program, University of Washington, Seattle, Washington, United States of America.
11
Duke University School of Medicine, Duke University, Durham, North Carolina, United States of America.
12
Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America.
13
University of Zimbabwe College of Health Sciences Clinical Trials Research Centre, Harare, Zimbabwe.
14
Department of Medicine, Division of Infectious Diseases, Emory University, Atlanta, Georgia, United States of America.

Abstract

The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.

PMID:
30933973
PMCID:
PMC6459550
DOI:
10.1371/journal.pcbi.1006952
[Indexed for MEDLINE]
Free PMC Article

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