| Literature DB >> 32483825 |
Rui Duan1, Liming Qiu1, Xianjin Xu1, Zhiwei Ma1,2, Benjamin Ryan Merideth1,3, Chi-Ren Shyu3,4, Xiaoqin Zou1,2,3,5.
Abstract
CAPRI challenges offer a variety of blind tests for protein-protein interaction prediction. In CAPRI Rounds 38-45, we generated a set of putative binding modes for each target with an FFT-based docking algorithm, and then scored and ranked these binding modes with a proprietary scoring function, ITScorePP. We have also developed a novel web server, Rebipp. The algorithm utilizes information retrieval to identify relevant biological information to significantly reduce the search space for a particular protein. In parallel, we have also constructed a GPU-based docking server, MDockPP, for protein-protein complex structure prediction. Here, the performance of our protocol in CAPRI rounds 38-45 is reported, which include 16 docking and scoring targets. Among them, three targets contain multiple interfaces: Targets 124, 125, and 136 have 2, 4, and 3 interfaces, respectively. In the predictor experiments, we predicted correct binding modes for nine targets, including one high-accuracy interface, six medium-accuracy binding modes, and six acceptable-accuracy binding modes. For the docking server prediction experiments, we predicted correct binding modes for eight targets, including one high-accuracy, three medium-accuracy, and five acceptable-accuracy binding modes.Entities:
Keywords: information retrieval; molecular docking; protein-peptide interactions; protein-protein interactions; protein-sugar interactions; scoring function; text mining
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Year: 2020 PMID: 32483825 PMCID: PMC7423244 DOI: 10.1002/prot.25956
Source DB: PubMed Journal: Proteins ISSN: 0887-3585