title: Deriving the phylogenetic information from some physicochemical properties of protein sequences computed.
abstract: The evolutionary relationships of organisms are traditionally delineated by the alignment-based methods using some DNA or protein sequences. In the post-genome era, the phylogenetics of life could be inferred from many sources such as genomic features, not just from comparison of one or several genes. To investigate the possibility that the physicochemical properties of protein sequences might reflect the phylogenetic ones, an alignment-free method using a support vector machine (SVM) classifier is implemented to establish the phylogenetic relationships between some protein sequences. There are two types of datasets, namely, the "Enzymatic" (assigned by an EC accession) and "Proteins" used to train the SVM classifiers. By computing the F-score for feature selection, we find that the classification accuracies of trained SVM classifiers could be significantly enhanced to 84% and 80%, respectively, for the enzymatic and "proteins" datasets classified if the protein sequences are represented with some top 255 features selected. These show that some physicochemical features of amino acid sequences selected are sufficient for inferring the phylogenetic properties of the protein sequences. Moreover, we find that the selected physicochemical features appear to correlate with the physiological characteristic of the taxonomic classes classified.
authors.affiliation: Bioresource Collection and Research Center, Food Industry Research and Development Institute, HsinChu, Taiwan.
authors.forename: Shih-Hau
authors.initials: SH
authors.lastname: Chiu
authors.forename: Chien-Chi
authors.initials: CC
authors.lastname: Chen
authors.forename: Gwo-Fang
authors.initials: GF
authors.lastname: Yuan
authors.forename: Thy-Hou
authors.initials: TH
authors.lastname: Lin
mesh_terms: D055598:Chemical Phenomena; D003198:Computer Simulation; D010802:Phylogeny; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein