title: Joint evolutionary trees: a large-scale method to predict protein interfaces based on sequence sampling.
abstract: The Joint Evolutionary Trees (JET) method detects protein interfaces, the core residues involved in the folding process, and residues susceptible to site-directed mutagenesis and relevant to molecular recognition. The approach, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. Families of homologous sequences are analyzed through a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple alignment and impacts of weakly homologous sequences on distance tree construction. The sampling method makes sequence analysis more sensitive to functional and structural importance of individual residues by avoiding effects of the overrepresentation of highly homologous sequences and improves computational efficiency. A carefully designed clustering method is parametrized on the target structure to detect and extend patches on protein surfaces into predicted interaction sites. Clustering takes into account residues' physical-chemical properties as well as conservation. Large-scale application of JET requires the system to be adjustable for different datasets and to guarantee predictions even if the signal is low. Flexibility was achieved by a careful treatment of the number of retrieved sequences, the amino acid distance between sequences, and the selective thresholds for cluster identification. An iterative version of JET (iJET) that guarantees finding the most likely interface residues is proposed as the appropriate tool for large-scale predictions. Tests are carried out on the Huang database of 62 heterodimer, homodimer, and transient complexes and on 265 interfaces belonging to signal transduction proteins, enzymes, inhibitors, antibodies, antigens, and others. A specific set of proteins chosen for their special functional and structural properties illustrate JET behavior on a large variety of interactions covering proteins, ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf, Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant improvement in performance and computational efficiency is shown.
authors.affiliation: Génomique Analytique, Université Pierre et Marie Curie-Paris 6, UMR S511, Paris, France.
authors.forename: Stefan
authors.initials: S
authors.lastname: Engelen
authors.forename: Ladislas A
authors.initials: LA
authors.lastname: Trojan
authors.forename: Sophie
authors.initials: S
authors.lastname: Sacquin-Mora
authors.forename: Richard
authors.initials: R
authors.lastname: Lavery
authors.forename: Alessandra
authors.initials: A
authors.lastname: Carbone
mesh_terms: D001665:Binding Sites / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence / Q000235:genetics; D030562:Databases, Protein; D019143:Evolution, Molecular; D008956:Models, Chemical; D008958:Models, Molecular; D016571:Neural Networks, Computer; D010802:Phylogeny; D011485:Protein Binding / Q000235:genetics; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000235:genetics* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins