title: Prediction of interacting protein residues using sequence and structure data.
abstract: Identifying hotspots responsible for protein interactions with other macromolecules or drugs provides insight into functional aspects of the protein network, and is a pivotal task in systems biology and drug discovery. Here, we present the protocol for the application of a machine-learning method - Random Forest - to prediction of interacting residues in proteins, based on either the structural parameters or the primary sequence alone.
authors.affiliation: Department of Molecular Biology, University of Zagreb, Zagreb, Croatia. vfranke@bioinfo.hr
authors.forename: Vedran
authors.initials: V
authors.lastname: Franke
authors.forename: Mile
authors.initials: M
authors.lastname: Sikić
authors.forename: Kristian
authors.initials: K
authors.lastname: Vlahoviček
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000378:metabolism*; D030562:Databases, Protein; D008958:Models, Molecular; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D012984:Software; D013223:Statistics as Topic
chemical_list: D000596:Amino Acids; D011506:Proteins