Abstract
HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/.
Highlights
Significant advances in the treatment of human immunodeficiency virus type 1 (HIV-1) have been made, and one class of drugs that has contributed to that success is inhibitors that target chemokine receptors CCR5 and CXCR4, collectively known as the HIV-1 coreceptors [1]
The situation is further complicated by HIV-1 tropism, or the ability of the virus to change the cell type infected, with the transition from a CCR5-specific (R5) virus to a CXCR4-specific (X4) virus often indicating a progression to advanced stages of infection for subtype B viruses [2]
Sequences with contradicting coreceptor usage in different sets/patients were removed. This resulted in a superset of non-redundant V3-loop sequences containing 235 CXCR4 tropic and 2220 CCR5 tropic sequences that were used for all training and testing of support vector machines (SVM) models, which is provided in S1 Dataset
Summary
Significant advances in the treatment of human immunodeficiency virus type 1 (HIV-1) have been made, and one class of drugs that has contributed to that success is inhibitors that target chemokine receptors CCR5 and CXCR4, collectively known as the HIV-1 coreceptors [1]. Bozek et al [24] utilized an approach similar to that used by Sander et al, but instead utilized the values of 54 amino acid indices mapped to spheres representing each V3-loop sequence None of these methods, nor to the best of our knowledge any other existing methods, utilize structural details of the specific interactions between the V3-loop and chemokine receptors CCR5/CXCR4 to predict HIV-1 coreceptor usage. GPCRs are highly structurally flexible, which implies that different V3-loop sequences could have different binding modes To this end, we have developed a multifaceted hybrid approach for investigating the interactions that drive coreceptor usage, using tools from computational biophysics, structural bioinformatics, and machine learning, as illustrated by Fig 1. A novel non-linear feature selection algorithm was used to narrow down the necessary and sufficient V3-loop:coreceptor interacting pairs
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