Abstract
MotivationProtein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods.ResultsWe extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.
Highlights
Operational definition of physical Protein-Protein Interactions (PPIs)We define PPIs as interactions that bring two different proteins A and B directly into ‘physical contact’
Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less wellstudied organisms
Given all PPIs in an organism, the interactome comprises all PPIs in the entire proteome; this network contains all non-temporal aspects of associations on the network level
Summary
We define PPIs as interactions that bring two different proteins A and B directly into ‘physical contact’. This ‘molecular’ perspective on PPIs differs from the most frequent view of both associations and permanent complexes. For us the crucial aspect of a PPI is that it brings two proteins into direct physical contact (usually transiently, i.e. for a limited time). Statistical models of PPIs can amend the coverage of networks formed from binary PPIs (A binds B) cost-effectively by enriching protein association networks [2,3,4] or by combining heterogeneous data sources in Bayesian networks [5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
