Domain prediction with probabilistic directional context.

TitleDomain prediction with probabilistic directional context.
Publication TypeJournal Article
Year of Publication2017
AuthorsOchoa, A, Singh, M
JournalBioinformatics
Volume33
Issue16
Pagination2471-2478
Date Published2017 Aug 15
ISSN1367-4811
KeywordsAlgorithms, Computational Biology, Humans, Models, Molecular, Models, Statistical, Protein Domains, Sequence Analysis, Protein, Software
Abstract

<p><b>Motivation: </b>Protein domain prediction is one of the most powerful approaches for sequence-based function prediction. Although domain instances are typically predicted independently of each other, newer approaches have demonstrated improved performance by rewarding domain pairs that frequently co-occur within sequences. However, most of these approaches have ignored the order in which domains preferentially co-occur and have also not modeled domain co-occurrence probabilistically.</p><p><b>Results: </b>We introduce a probabilistic approach for domain prediction that models 'directional' domain context. Our method is the first to score all domain pairs within a sequence while taking their order into account, even for non-sequential domains. We show that our approach extends a previous Markov model-based approach to additionally score all pairwise terms, and that it can be interpreted within the context of Markov random fields. We formulate our underlying combinatorial optimization problem as an integer linear program, and demonstrate that it can be solved quickly in practice. Finally, we perform extensive evaluation of domain context methods and demonstrate that incorporating context increases the number of domain predictions by ∼15%, with our approach dPUC2 (Domain Prediction Using Context) outperforming all competing approaches.</p><p><b>Availability and Implementation: </b>dPUC2 is available at http://github.com/alexviiia/dpuc2.</p><p><b>Contact: </b>mona@cs.princeton.edu.</p><p><b>Supplementary information: </b>Supplementary data are available at Bioinformatics online.</p>

DOI10.1093/bioinformatics/btx221
Alternate JournalBioinformatics
PubMed ID28407137
PubMed Central IDPMC5870623
Grant ListR01 GM076275 / GM / NIGMS NIH HHS / United States
R21 AI085415 / AI / NIAID NIH HHS / United States