Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

• DOI: 10.1371/journal.pone.0160098
• Publication Date: 2016-07-28T13:59:23Z
• Authors: Matthew J. O’Meara; Sara Ballouz; Brian K. Shoichet; Jesse Gillis
• Keywords: 0301 basic medicine; 570; General Science & Technology; 1.1 Normal biological development and functioning; Science; anzsrc-for: 46 Information and Computing Sciences; Bioinformatics and Computational Biology; 612; 3101 Biochemistry and Cell Biology; anzsrc-for: 4613 Theory Of Computation; Ligands; anzsrc-for: 34 Chemical Sciences; 4613 Theory Of Computation; 3102 Bioinformatics and Computational Biology; Medicinal and Biomolecular Chemistry; Databases; 03 medical and health sciences; 46 Information and Computing Sciences; Underpinning research; Information and Computing Sciences; Genetics; Gene Regulatory Networks; ontology; 3404 Medicinal and Biomolecular Chemistry; anzsrc-for: 31 Biological Sciences; genes; Databases, Protein; genomics and proteomics; 34 Chemical Sciences; Protein; anzsrc-for: 3101 Biochemistry and Cell Biology; Q; R; bioinformatics; Biological Sciences; 540; Theory Of Computation; anzsrc-for: 3404 Medicinal and Biomolecular Chemistry; Chemical Sciences; Medicine; structure and function; Biochemistry and Cell Biology; Generic health relevance; anzsrc-for: 3102 Bioinformatics and Computational Biology; 31 Biological Sciences; Biotechnology; Research Article

The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63-0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited.