Popular computational methods to assess multi-protein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments
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 (PPI) 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 in order to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (A) the type of data utilised: methods based on experiment-specific measurements (e.g. spectral counts or identification scores) vs. methods that extract knowledge encoded in external annotations (e.g. public interaction and functional categorisation databases); (B) the type of algorithm applied: the statistical description and estimation of physical protein properties vs. predictive supervised machine learning or text-mining algorithms; (C) the type of protein relation evaluated: direct (binary) interaction of two proteins in a co-complex vs. probability of any functional relationship between two proteins (e.g. co-occurrence in a pathway, sub cellular compartment); and (D) motivation: elucidation of experimental data by evaluation vs. prediction of novel PPI, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for PPI evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility and availability. The scoring methods and platforms described include: SAINT, CompPass, Decontaminator, FunCoup, STRING and MINT. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing inter-disciplinary field of investigation.