title: Protein structure prediction provides comparable performance to crystallographic structures in docking-based virtual screening.
abstract: Structure based virtual screening has largely been limited to protein targets for which either an experimental structure is available or a strongly homologous template exists so that a high-resolution model can be constructed. The performance of state of the art protein structure predictions in virtual screening in systems where only weakly homologous templates are available is largely untested. Using the challenging DUD database of structural decoys, we show here that even using templates with only weak sequence homology (<30% sequence identity) structural models can be constructed by I-TASSER which achieve comparable enrichment rates to using the experimental bound crystal structure in the majority of the cases studied. For 65% of the targets, the I-TASSER models, which are constructed essentially in the apo conformations, reached 70% of the virtual screening performance of using the holo-crystal structures. A correlation was observed between the success of I-TASSER in modeling the global fold and local structures in the binding pockets of the proteins versus the relative success in virtual screening. The virtual screening performance can be further improved by the recognition of chemical features of the ligand compounds. These results suggest that the combination of structure-based docking and advanced protein structure modeling methods should be a valuable approach to the large-scale drug screening and discovery studies, especially for the proteins lacking crystallographic structures.
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA; Department of Public Health, Lanzhou University, Lanzhou 730000, China.
authors.forename: Hongying
authors.initials: H
authors.lastname: Du
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Brender
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
authors.forename: Jian
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA. Electronic address: zhng@umich.edu.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D018360:Crystallography, X-Ray; D062126:Databases, Chemical; D008024:Ligands; D062105:Molecular Docking Simulation / Q000379:methods*; D011506:Proteins / Q000737:chemistry*
chemical_list: D008024:Ligands; D011506:Proteins
keywords: Enrichment rate; Ligand docking; Protein structure prediction; Virtual screening