title: Remote homolog detection using local sequence-structure correlations.
abstract: Remote homology detection refers to the detection of structural homology in proteins when there is little or no sequence similarity. In this article, we present a remote homolog detection method called SVM-HMMSTR that overcomes the reliance on detectable sequence similarity by transforming the sequences into strings of hidden Markov states that represent local folding motif patterns. These state strings are transformed into fixed-dimension feature vectors for input to a support vector machine. Two sets of features are defined: an order-independent feature set that captures the amino acid and local structure composition; and an order-dependent feature set that captures the sequential ordering of the local structures. Tests using the Structural Classification of Proteins (SCOP) 1.53 data set show that the SVM-HMMSTR gives a significant improvement over several current methods.
authors.affiliation: School of Computing, National University of Singapore, Singapore. houyuna@comp.nus.edu.sg <houyuna@comp.nus.edu.sg>
authors.forename: Yuna
authors.initials: Y
authors.lastname: Hou
authors.forename: Wynne
authors.initials: W
authors.lastname: Hsu
authors.forename: Mong Li
authors.initials: ML
authors.lastname: Lee
authors.forename: Christopher
authors.initials: C
authors.lastname: Bystroff
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D008390:Markov Chains; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry*; D040681:Structural Homology, Protein
chemical_list: D011506:Proteins