title: An Evolutionary View on Disulfide Bond Connectivities Prediction Using Phylogenetic Trees and a Simple Cysteine Mutation Model.
abstract: Disulfide bonds are crucial for many structural and functional aspects of proteins. They have a stabilizing role during folding, can regulate enzymatic activity and can trigger allosteric changes in the protein structure. Moreover, knowledge of the topology of the disulfide connectivity can be relevant in genomic annotation tasks and can provide long range constraints for ab-initio protein structure predictors. In this paper we describe PhyloCys, a novel unsupervised predictor of disulfide bond connectivity from known cysteine oxidation states. For each query protein, PhyloCys retrieves and aligns homologs with HHblits and builds a phylogenetic tree using ClustalW. A simplified model of cysteine co-evolution is then applied to the tree in order to hypothesize the presence of oxidized cysteines in the inner nodes of the tree, which represent ancestral protein sequences. The tree is then traversed from the leaves to the root and the putative disulfide connectivity is inferred by observing repeated patterns of tandem mutations between a sequence and its ancestors. A final correction is applied using the Edmonds-Gabow maximum weight perfect matching algorithm. The evolutionary approach applied in PhyloCys results in disulfide bond predictions equivalent to Sephiroth, another approach that takes whole sequence information into account, and is 26-29% better than state of the art methods based on cysteine covariance patterns in multiple sequence alignments, while requiring one order of magnitude fewer homologous sequences (10(3) instead of 10(4)), thus extending its range of applicability. The software described in this article and the datasets used are available at http://ibsquare.be/phylocys.
authors.affiliation: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium; Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium; Department of Structural Biology, VIB, Brussels, Belgium; Machine Learning Group, ULB, Brussels, Belgium.
authors.forename: Daniele
authors.initials: D
authors.lastname: Raimondi
authors.affiliation: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium; Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium; Department of Structural Biology, VIB, Brussels, Belgium; Machine Learning Group, ULB, Brussels, Belgium.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Orlando
authors.affiliation: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium; Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium; Department of Structural Biology, VIB, Brussels, Belgium.
authors.forename: Wim F
authors.initials: WF
authors.lastname: Vranken
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003545:Cysteine / Q000737:chemistry / Q000145:classification / Q000235:genetics*; D004220:Disulfides / Q000737:chemistry*; D019143:Evolution, Molecular; D020407:Internet; D008957:Models, Genetic; D009154:Mutation; D010084:Oxidation-Reduction; D010802:Phylogeny; D015203:Reproducibility of Results; D012984:Software
chemical_list: D004220:Disulfides; D003545:Cysteine