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

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 IC 50 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 IC 50 was predicted with an average validated R 2 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.

Predicting the broadly neutralizing antibody susceptibility of the HIV reservoir

“…Buiu et al presented a trained neural network approach to directly predict IC50 values of given antibodies against HIV-1, based on the Env sequence information (31), and Hake et al built binary classifiers with nonlinear support vector machines and string kernels to distinguish between HIV-1 resistance and susceptibility to a bNAb based on different Env amino acid (AA) sequences (20). More recently, Magaret et al used 2 machine-learning approaches, based on a set of predefined AA sequence features to predict several TZM-bl neutralization assay outcomes for the CD4bs antibody VRC01, including virus's resistance versus sensitivity status, logIC50/80 values, and estimated neutralization slope of the dose-response curves (22). Bricault et al used random forest for IC50 regression predictions that included information on AA, PNGS, clade, and variable loop characteristics, demonstrating that the model accuracy using all the information was superior compared with model accuracy including single parameters, i.e., contact-region-only signatures (21).…”

“…High-resolution imaging approaches have revealed that bNAb and glycan interactions are common across all bNAb classes, and previous analyses to map antibody binding and antibody neutralization activity depended not only on sequence, but also on glycan occupancy (18,19). Computational approaches for prediction of neutralization sensitivity based on Env sequences have been reported previously (20)(21)(22). Information of glycans and glycan occupancy, however, have not been included in these models, therefore excluding a critical component.…”

Broadly Neutralizing Antibodies for HIV-1 Prevention

“…While phenotyping the bnAb sensitivity of patient viruses can be performed using either bulk or limiting dilution T cell outgrowth cultures, these assays can be labor intensive, costly, and may fail to detect minor pre-existing resistant variants ( 65 , 66 ). An alternative strategy for future development may be to use predictive modeling based on env sequencing of the patient’s quasispecies to determine bnAb sensitivity patterns and optimize combination cocktails ( 67 – 69 ).…”

Peptide-Based Vaccination for Antibody Responses Against HIV

“…These algorithms have become more powerful in predicting epitopes and can be combined to increase the accuracy of large-scale peptide epitope predictions in HIV. For example, Bricault et al [13] recently demonstrated that bnAbs signatures can be used to engineer HIV-1 Env vaccine immunogens capable of eliciting Ab responses with greater neutralization breadth by a machine-learning-based prediction approach [13,14]. The second strategy is in vitro toward the discovery of new epitopes by a phage display technology, resulting in the expression of a peptide that mimics the structure of an epitope.…”