Domain prediction with probabilistic directional context.

Published

Journal Article

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.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.dPUC2 is available at http://github.com/alexviiia/dpuc2.mona@cs.princeton.edu.Supplementary data are available at Bioinformatics online.

Full Text

Duke Authors

Cited Authors

  • Ochoa, A; Singh, M

Published Date

  • August 2017

Published In

Volume / Issue

  • 33 / 16

Start / End Page

  • 2471 - 2478

PubMed ID

  • 28407137

Pubmed Central ID

  • 28407137

Electronic International Standard Serial Number (EISSN)

  • 1367-4811

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btx221

Language

  • eng