title: A compound-based computational approach for the accurate determination of hot spots.
abstract: A plethora of both experimental and computational methods have been proposed in the past 20 years for the identification of hot spots at a protein-protein interface. The experimental determination of a protein-protein complex followed by alanine scanning mutagenesis, though able to determine hot spots with much precision, is expensive and has no guarantee of success while the accuracy of the current computational methods for hot-spot identification remains low. Here, we present a novel structure-based computational approach that accurately determines hot spots through docking into a set of proteins homologous to only one of the two interacting partners of a compound capable of disrupting the protein-protein interaction (PPI). This approach has been applied to identify the hot spots of human activin receptor type II (ActRII) critical for its binding toward Cripto-I. The subsequent experimental confirmation of the computationally identified hot spots portends a potentially accurate method for hot-spot determination in silico given a compound capable of disrupting the PPI in question. The hot spots of human ActRII first reported here may well become the focal points for the design of small molecule drugs that target the PPI. The determination of their interface may have significant biological implications in that it suggests that Cripto-I plays an important role in both activin and nodal signal pathways.
authors.affiliation: The College of Computer Science and Technology, Jilin University, Changchun, Jilin, China. wlincong@gmail.com
authors.forename: Lincong
authors.initials: L
authors.lastname: Wang
authors.forename: Yaqin
authors.initials: Y
authors.lastname: Hou
authors.forename: Haihua
authors.initials: H
authors.lastname: Quan
authors.forename: Weiwei
authors.initials: W
authors.lastname: Xu
authors.forename: Yongli
authors.initials: Y
authors.lastname: Bao
authors.forename: Yuxin
authors.initials: Y
authors.lastname: Li
authors.forename: Yuan
authors.initials: Y
authors.lastname: Fu
authors.forename: Shuxue
authors.initials: S
authors.lastname: Zou
mesh_terms: D030301:Activin Receptors, Type II / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D028341:Activins / Q000235:genetics / Q000378:metabolism; D000595:Amino Acid Sequence; D001665:Binding Sites; D030562:Databases, Protein; D058851:GPI-Linked Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D057809:HEK293 Cells; D006801:Humans; D036341:Intercellular Signaling Peptides and Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D008958:Models, Molecular; D062105:Molecular Docking Simulation / Q000379:methods*; D008969:Molecular Sequence Data; D009363:Neoplasm Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D016415:Sequence Alignment; D015398:Signal Transduction
chemical_list: D058851:GPI-Linked Proteins; D036341:Intercellular Signaling Peptides and Proteins; D009363:Neoplasm Proteins; C070636:TDGF1 protein, human; D028341:Activins; D030301:Activin Receptors, Type II; C425585:activin receptor type II-A
keywords: Cripto-I; activin receptor; activin signal pathway and nodal signal pathway; hot spot; hot-spot prediction; protein-ligand docking; protein-protein docking; protein-protein interaction; scoring function