IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform

PLoS Comput Biol. 2014 Sep 25;10(9):e1003842. doi: 10.1371/journal.pcbi.1003842. eCollection 2014 Sep.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • AIDS Dementia Complex
  • Algorithms
  • Antibodies, Neutralizing* / immunology
  • Computational Biology / methods
  • Drug Resistance, Viral
  • Epitopes* / chemistry
  • Epitopes* / immunology
  • HIV Antibodies / immunology*
  • HIV Infections / immunology
  • HIV Infections / virology
  • HIV-1* / chemistry
  • HIV-1* / immunology
  • Human Immunodeficiency Virus Proteins* / chemistry
  • Human Immunodeficiency Virus Proteins* / immunology
  • Humans
  • Machine Learning
  • Phenotype
  • Sequence Analysis, Protein / methods
  • Software

Substances

  • Antibodies, Neutralizing
  • Epitopes
  • HIV Antibodies
  • Human Immunodeficiency Virus Proteins