title: Reduced amino acid alphabet is sufficient to accurately recognize intrinsically disordered protein.
abstract: Intrinsically disordered proteins are an important class of proteins with unique functions and properties. Here, we have applied a support vector machine (SVM) trained on naturally occurring disordered and ordered proteins to examine the contribution of various parameters (vectors) to recognizing proteins that contain disordered regions. We find that a SVM that incorporates only amino acid composition has a recognition accuracy of 87+/-2%. This result suggests that composition alone is sufficient to accurately recognize disorder. Interestingly, SVMs using reduced sets of amino acids based on chemical similarity preserve high recognition accuracy. A set as small as four retains an accuracy of 84+/-2%; this suggests that general physicochemical properties rather than specific amino acids are important factors contributing to protein disorder.
authors.affiliation: Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 221 Maryland Hall, 3400 North Charles Street, Baltimore, MD 21218, USA.
authors.forename: Edward A
authors.initials: EA
authors.lastname: Weathers
authors.forename: Michael E
authors.initials: ME
authors.lastname: Paulaitis
authors.forename: Thomas B
authors.initials: TB
authors.lastname: Woolf
authors.forename: Jan H
authors.initials: JH
authors.lastname: Hoh
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry* / Q000235:genetics; D003627:Data Interpretation, Statistical; D008962:Models, Theoretical; D011506:Proteins / Q000138:chemical synthesis / Q000737:chemistry* / Q000145:classification / Q000235:genetics; D015203:Reproducibility of Results; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins