Prediction of Co-Receptor Usage of HIV-1 from Genotype

Abstract

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937±0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine.

Random Forest for Bioinformatics

“…The type of co-receptor is crucial for the aggressiveness of the virus and the available treatment options. Hence, Dybowski et al [16] proposed to predict co-receptor usage based on the viral genome sequences. A random forest based method is developed to predict co-receptor usage for new sequences using structures and sequences of "gp120".…”

“…A random forest based method is developed to predict co-receptor usage for new sequences using structures and sequences of "gp120". The good accuracy achieved in [16] made random forest a strong candidate for computational diagnosis of viral diseases.…”

Nonparametric Variable Selection Using Machine Learning Algorithms in High Dimensional (Large P, Small N) Biomedical Applications

“…Random Forests are increasingly popular in the biomedical community and enjoy good predictive success even against other machine learning algorithms in a wide variety of applications (Lunetta et al, 2004;Segal et al, 2004;Bureau et al 2005; Diaz-Uriarte and Alvarez de Andes 2006; Qi, Bar-Joseph and Klein-Seetharaman 2006; Xu et al, 2007;Archer and Kimes 2008;Pers et al 2009;Tuv et al, 2009;Dybowski, Heider and Hoffman 2010;Geneur et al, 2010). Random Forests have been used in HIV disease to examine phenotypic properties of the virus.…”

“…Segal et al used Random Forests to examine the role of mutations in polymerase in HIV-1 to viral replication capacity . Random Forests have also been used to predict HIV-1 coreceptor usage from sequence data (Xu et al, 2007;Dybowski et al, 2010). Qi et al found that Random Forests had excellent predictive capabilities in the prediction of protein interaction compared to six other machine learning methods (Qi et al, 2006).…”

A Case-Based Reasoning System for Genotypic Prediction of Hiv-1 Co-Receptor Tropism

“…To date, many bioinformatics methods for tropism prediction have been developed and tested. These bioinformatics predictors include support vector machines (SVM) [4,5], neural networks (NN) [6], decision trees [7], random forest [8], instance based reasoning [9], position specific scoring matrices (PSSM) [10], multiple linear regression [11], and the 11/25 rule [12]. However, these methods generally are developed by fitting a model onto the respective training set, and might not perform as well in independent or unseen datasets [13].…”