title: IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.
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.
authors.affiliation: Interdisciplinary Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, United States of America.
authors.forename: N Lance
authors.initials: NL
authors.lastname: Hepler
authors.affiliation: Department of Medicine, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Konrad
authors.initials: K
authors.lastname: Scheffler
authors.affiliation: Department of Medicine, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Steven
authors.initials: S
authors.lastname: Weaver
authors.affiliation: Department of Medicine, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Ben
authors.initials: B
authors.lastname: Murrell
authors.affiliation: Department of Pathology, University of California San Diego, La Jolla, California, United States of America; San Diego Veterans Affairs Healthcare System, San Diego, California, United States of America.
authors.forename: Douglas D
authors.initials: DD
authors.lastname: Richman
authors.affiliation: The Scripps Research Institute, La Jolla, California, United States of America; Ragon Institute of MGH, MIT, and Harvard, Boston, Massachusetts, United States of America.
authors.forename: Dennis R
authors.initials: DR
authors.lastname: Burton
authors.affiliation: The Scripps Research Institute, La Jolla, California, United States of America.
authors.forename: Pascal
authors.initials: P
authors.lastname: Poignard
authors.affiliation: Department of Medicine, University of California San Diego, La Jolla, California, United States of America; San Diego Veterans Affairs Healthcare System, San Diego, California, United States of America.
authors.forename: Davey M
authors.initials: DM
authors.lastname: Smith
authors.affiliation: Department of Medicine, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Sergei L
authors.initials: SL
authors.lastname: Kosakovsky Pond
mesh_terms: D015526:AIDS Dementia Complex; D000465:Algorithms; D057134:Antibodies, Neutralizing / Q000276:immunology; D019295:Computational Biology / Q000379:methods; D024882:Drug Resistance, Viral; D000939:Epitopes / Q000737:chemistry / Q000276:immunology; D015483:HIV Antibodies / Q000276:immunology*; D015658:HIV Infections / Q000276:immunology / Q000821:virology; D015497:HIV-1 / Q000737:chemistry / Q000276:immunology; D054298:Human Immunodeficiency Virus Proteins / Q000737:chemistry / Q000276:immunology; D006801:Humans; D000069550:Machine Learning; D010641:Phenotype; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D057134:Antibodies, Neutralizing; D000939:Epitopes; D015483:HIV Antibodies; D054298:Human Immunodeficiency Virus Proteins