title: Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening.
abstract: Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets.
authors.affiliation: BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.
authors.forename: Changsheng
authors.initials: C
authors.lastname: Zhang
authors.forename: Bo
authors.initials: B
authors.lastname: Tang
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.forename: Luhua
authors.initials: L
authors.lastname: Lai
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D002352:Carrier Proteins; D055808:Drug Discovery; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D057166:High-Throughput Screening Assays; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D008958:Models, Molecular; D062105:Molecular Docking Simulation; D011485:Protein Binding; D011487:Protein Conformation; D054730:Protein Interaction Domains and Motifs; D020349:Surface Plasmon Resonance; D014409:Tumor Necrosis Factor-alpha / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D002352:Carrier Proteins; D014409:Tumor Necrosis Factor-alpha
keywords: TNFα; binding energy landscape; protein design; protein-protein docking; protein-protein interaction prediction; virtual screening