title: Protein meta-functional signatures from combining sequence, structure, evolution, and amino acid property information.
abstract: Protein function is mediated by different amino acid residues, both their positions and types, in a protein sequence. Some amino acids are responsible for the stability or overall shape of the protein, playing an indirect role in protein function. Others play a functionally important role as part of active or binding sites of the protein. For a given protein sequence, the residues and their degree of functional importance can be thought of as a signature representing the function of the protein. We have developed a combination of knowledge- and biophysics-based function prediction approaches to elucidate the relationships between the structural and the functional roles of individual residues and positions. Such a meta-functional signature (MFS), which is a collection of continuous values representing the functional significance of each residue in a protein, may be used to study proteins of known function in greater detail and to aid in experimental characterization of proteins of unknown function. We demonstrate the superior performance of MFS in predicting protein functional sites and also present four real-world examples to apply MFS in a wide range of settings to elucidate protein sequence-structure-function relationships. Our results indicate that the MFS approach, which can combine multiple sources of information and also give biological interpretation to each component, greatly facilitates the understanding and characterization of protein function.
authors.affiliation: Department of Microbiology, Computational Genomics Group, University of Washington, Seattle, Washington, United States of America.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Horst
authors.forename: Gong
authors.initials: G
authors.lastname: Cheng
authors.forename: David C
authors.initials: DC
authors.lastname: Nickle
authors.forename: Ram
authors.initials: R
authors.lastname: Samudrala
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001426:Bacterial Proteins / Q000737:chemistry / Q000235:genetics / Q000502:physiology; D001665:Binding Sites; D043366:Cellulose 1,4-beta-Cellobiosidase / Q000737:chemistry / Q000235:genetics / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D017124:Conserved Sequence; D030562:Databases, Protein / Q000706:statistics & numerical data; D019143:Evolution, Molecular; D020407:Internet; D008956:Models, Chemical; D008957:Models, Genetic; D008958:Models, Molecular; D015394:Molecular Structure; D016297:Mutagenesis, Site-Directed; D009955:Ornithine Decarboxylase / Q000737:chemistry / Q000235:genetics / Q000502:physiology; D054730:Protein Interaction Domains and Motifs; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics* / Q000502:physiology; D012044:Regression Analysis; D016415:Sequence Alignment / Q000706:statistics & numerical data; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D001426:Bacterial Proteins; D011506:Proteins; D043366:Cellulose 1,4-beta-Cellobiosidase; D009955:Ornithine Decarboxylase