Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time.

Anna Hake,Nico Pfeifer,Alice Carolyn McHardy

doi:10.1371/journal.pcbi.1005789

Anna Hake, Nico Pfeifer + Show 1 more

Open Access

https://doi.org/10.1371/journal.pcbi.1005789

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Abstract

Treatment with broadly neutralizing antibodies (bNAbs) has proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. Due to the high mutation rate of HIV-1, resistance testing of the patient’s viral strains to the bNAbs is still inevitable. So far, bNAb resistance can only be tested in expensive and time-consuming neutralization experiments. Here, we introduce well-performing computational models that predict the neutralization response of HIV-1 to bNAbs given only the envelope sequence of the virus. Using non-linear support vector machines based on a string kernel, the models learnt even the important binding sites of bNAbs with more complex epitopes, i.e., the CD4 binding site targeting bNAbs, proving thereby the biological relevance of the models. To increase the interpretability of the models, we additionally provide a new kind of motif logo for each query sequence, visualizing those residues of the test sequence that influenced the prediction outcome the most. Moreover, we predicted the neutralization sensitivity of around 34,000 HIV-1 samples from different time points to a broad range of bNAbs, enabling the first analysis of HIV resistance to bNAbs on a global scale. The analysis showed for many of the bNAbs a trend towards antibody resistance over time, which had previously only been discovered for a small non-representative subset of the global HIV-1 population.

Highlights

With around 36.7 million people living with HIV in 2015 and an incidence rate of around 2.1 million each year [1], infections with HIV continue to be a major global health issue
Upon performance comparison between different kernels, the oligo kernel was selected for all broadly neutralizing antibodies (bNAbs) to predict the neutralization susceptibility to each bNAb for new viral strains
To improve the interpretability of the classification decision, we show how to produce for each classification of a test envelope sequence a motif logo—a representation of the test sequence—that displays those residues in the test sequence that contributed the most to the classification result

Summary

Introduction

With around 36.7 million people living with HIV in 2015 and an incidence rate of around 2.1 million each year [1], infections with HIV continue to be a major global health issue. Despite more than three decades of research, there is neither a vaccine against nor a cure available for infection with HIV-1. HIV-1 infected patients are usually treated with a highly active antiretroviral therapy (ART). ART suppresses the replication of the active virus, but it is not capable of eliminating viral reservoirs and clearing the infection. To reduce the emergence of drug-resistant viruses, ART usually consists of a combination of three or more drugs from at least two different drug classes. There are six different drug classes, which differ in their mode of interference with the HIV-1 life cycle, resulting in more than 20 available antiretroviral drugs. A change of the drug regimen is still often required, due to emerging drug resistances or side-effects. Since lifelong treatment is inevitable, for some patients no efficient drug regimens might be left eventually. There is still a high demand for drugs with new targets [2]

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