Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration

doi:10.1186/1471-2105-14-S4-S4

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/93416

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/93416

Title:	Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration
Authors:	Lo, Ying-Tsang;Wang, Hsin-Wei;Pai, Tun-Wen;Tzou, Wen-Shoung;Hsu, Hui-Huang;Chang, Hao-Teng
Contributors:	淡江大學資訊工程學系
Date:	2013-03
Issue Date:	2014-01-09 15:38:21 (UTC+8)
Publisher:	London: BioMed Central Ltd.
Abstract:	Background Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. Results Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. Conclusions In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance for protein-ligand binding region prediction. Based on the same approach and rationale, this method can also be applied to predict carbohydrate-antibody interactions for further design and development of carbohydrate-based vaccines. PLB-SAVE is available at http://save.cs.ntou.edu.tw.
Relation:	BMC Bioinformatics 14 Suppl.4, pp.1-11
DOI:	10.1186/1471-2105-14-S4-S4
Appears in Collections:	[資訊工程學系暨研究所] 期刊論文

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