title: Proteins comparison through probabilistic optimal structure local alignment.
abstract: Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.
authors.affiliation: Department of Computer Science, University of Pisa Pisa, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Micale
authors.affiliation: Department of Clinical and Molecular Biomedicine, University of Catania Catania, Italy.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Pulvirenti
authors.affiliation: Department of Clinical and Molecular Biomedicine, University of Catania Catania, Italy.
authors.forename: Rosalba
authors.initials: R
authors.lastname: Giugno
authors.affiliation: Department of Clinical and Molecular Biomedicine, University of Catania Catania, Italy.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Ferro
keywords: binding sites identification; local alignment; motifs identification; protein comparison; protein families; structure comparison