Abstract
Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.
Highlights
Kiel and coworkers describe a new conceptual analysis pipeline that combines experimental proteomic data, literature mining, computational analyses, and structural information to generate a multiscale signal transduction network based on tissue-specific gene expression and domain-domain interaction data for the study of rhodopsin and its interactions [169]
The systems outlined provide examples of contemporary PPI inference methods to which the filtering of false-positive interactions from AP-MS experiments lends a dual purpose. This dual purpose frames the present state of the field, because it appears that successful computational approaches to the postreduction of FPR in AP-MS experiments may require a balance to be struck between the specificity of direct analysis on experimental output and the strong phenotypic relationships inferred from abundant annotation and large databases of generic curated PPIs
Summary and Future Prospects—The approaches described in this review may all be employed to assist the interpretation of empirical AP-MS-derived protein interaction data, by filtering false-positive interactions in particular
Summary
Van Haagen et al [162] combine text mining (Peregrine [163]), Gene Ontology (GO) over-representation analysis, microarray data (COXPRESdb [164]), tissue specific gene expression data (TiGER) [165], and domain-domain interaction (DOMINE) [166] information into a single PPI prediction system and demonstrate their approach by inferring potential interaction partners for dysferlin and huntingtin [162].
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