title: A comparative analysis of computational approaches and algorithms for protein subcomplex identification.
abstract: High-throughput AP-MS methods have allowed the identification of many protein complexes. However, most post-processing methods of this type of data have been focused on detection of protein complexes and not its subcomplexes. Here, we review the results of some existing methods that may allow subcomplex detection and propose alternative methods in order to detect subcomplexes from AP-MS data. We assessed and drew comparisons between the use of overlapping clustering methods, methods based in the core-attachment model and our own prediction strategy (TRIBAL). The hypothesis behind TRIBAL is that subcomplex-building information may be concealed in the multiple edges generated by an interaction repeated in different contexts in raw data. The CACHET method offered the best results when the evaluation of the predicted subcomplexes was carried out using both the hypergeometric and geometric scores. TRIBAL offered the best performance when using a strict meet-min score.
authors.affiliation: College of Information Technology, United Arab Emirates University, Al AinP.O. Box 17551, United Arab Emirates.
authors.forename: Nazar
authors.initials: N
authors.lastname: Zaki
authors.affiliation: 1] College of Information Technology, United Arab Emirates University, Al AinP.O. Box 17551, United Arab Emirates [2] Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology (IFC), CNR, Pisa, Italy.
authors.forename: Antonio
authors.initials: A
authors.lastname: Mora
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D057166:High-Throughput Screening Assays / Q000379:methods*; D008954:Models, Biological; D008969:Molecular Sequence Data; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D020543:Proteome