IDEPI: Rapid Prediction of HIV-1 Antibody Epitopes and Other Phenotypic Features from Sequence Data Using a Flexible Machine Learning Platform: e1003842

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals - a cure and a vaccine - remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with...

Full description

Saved in:
Bibliographic Details
Published in:PLoS computational biology 2014-09, Vol.10 (9)
Main Authors: Hepler, N Lance, Scheffler, Konrad, Weaver, Steven, Murrell, Ben, Richman, Douglas D, Burton, Dennis R, Poignard, Pascal, Smith, Davey M, Pond, Sergei LKosakovsky
Format: Article
Language:English
Subjects:
Acquired immune deficiency syndrome
AIDS
Algorithms
Antibodies
Computation
Drug resistance
Genetics
Genotype & phenotype
Human immunodeficiency virus 1
Licenses
Machine learning
Mathematical models
Medical research
Mutation
Neural networks
Proteins
Software
Source code
Studies
Vaccines
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals - a cure and a vaccine - remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.
ISSN:1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003842