Popular Computational Methods to Assess Multiprotein Complexes Derived From Label-Free Affinity Purification and Mass Spectrometry (AP-MS) Experiments

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., cooccurrence 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.

Global landscape of cell envelope protein complexes in Escherichia coli

“…The average correlation between replicates (calculated based on protein spectral counts detected by MS) was high ( r = 0.7), with low (σ = 0.1) variance (Fig. 1b), indicating the overall reproducibility, which is comparable to previous AP/MS-based analyses of soluble protein interaction networks 11,12 .…”

Protein Complex Affinity Capture from Cryomilled Mammalian Cells

“…As a result, significant effort must be devoted to optimizing the capture process. The detection of FPs remains a central challenge for which a large number of different tools have been developed, including experimental [38][39][40] and computational approaches [41][42][43] , each with different pros and cons. In our own experiments we previously noted a pattern of FP protein binding, obtained after incubation of α-FLAG magnetic medium with control cell extracts, that was distinct from the pattern obtained in the presence of 3xFLAG-tagged proteins.…”

“…Examples of effects exerted by different extractants can be seen in references 1,11,24 . Because these and other experimental parameters affect the quality of the affinity capture, making it difficult to discriminate FPs, repositories of "bead proteomes" and computational approaches to eliminate non-specific contaminants have been developed to assist in identifying FPs [40][41][42][43] . Nevertheless, such approaches only substitute for optimal sample preparation to a limited degree 59 .…”

“…This approach foregoes optimization of the conditions of capture for any given target in favor of exploring many targets; as such, the inferred complexes themselves are rarely retrieved fully intact and highly pure in any given sample. Rather, the partial overlaps in compositions observed between numerous distinct affinity-captured samples are used as a basis of the inferences 42 . Both approaches contribute valuable data to our understanding of the interactome.…”

Pre- and Post-Processing Workflow for Affinity Purification Mass Spectrometry Data

“…[3][4][5] Eliminating all proteins that were detected in the negative controls constitutes the most rigid treatment, whereas filtering for proteins with a ratio of spectral counts in the bait versus control experiments exceeding a certain threshold is often a more suitable alternative. Further, different frequency filters have been introduced, judging the reproducibility in replicates as well as the abundance of a protein in different baits within a large-scale study.…”