title: Structural relatedness via flow networks in protein sequence space.
abstract: A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.
authors: Institute of Evolution, University of Haifa, Haifa 31905, Israel. zakharf@research.haifa.ac.il
authors: Zakharia M
authors: ZM
authors: Frenkel
authors: Zeev M
authors: ZM
authors: Frenkel
authors: Edward N
authors: EN
authors: Trifonov
authors: Sagi
authors: S
authors: Snir
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D004553:Electric Conductivity; D008956:Models, Chemical; D010446:Peptide Fragments / Q000737:chemistry; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D010446:Peptide Fragments; D011506:Proteins