title: Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction.
abstract: Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
authors.affiliation: Laboratory of Computational Intelligence and Biotechnology, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia.
authors.forename: Rosfuzah
authors.initials: R
authors.lastname: Roslan
authors.forename: Razib M
authors.initials: RM
authors.lastname: Othman
authors.forename: Zuraini A
authors.initials: ZA
authors.lastname: Shah
authors.forename: Shahreen
authors.initials: S
authors.lastname: Kasim
authors.forename: Hishammuddin
authors.initials: H
authors.lastname: Asmuni
authors.forename: Jumail
authors.initials: J
authors.lastname: Taliba
authors.forename: Rohayanti
authors.initials: R
authors.lastname: Hassan
authors.forename: Zalmiyah
authors.initials: Z
authors.lastname: Zakaria
mesh_terms: D000465:Algorithms; D000818:Animals; D029742:Caenorhabditis elegans Proteins; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D029721:Drosophila Proteins; D006801:Humans; D015233:Models, Statistical; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins; D009626:Terminology as Topic
chemical_list: D029742:Caenorhabditis elegans Proteins; D029721:Drosophila Proteins; D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins