Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration
學年 101
學期 2
出版（發表）日期 2013-03-01
作品名稱 Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration
作品名稱（其他語言）
著者 Lo, Ying-Tsang; Wang, Hsin-Wei; Pai, Tun-Wen; Tzou, Wen-Shoung; Hsu, Hui-Huang; Chang, Hao-Teng
單位 淡江大學資訊工程學系
出版者 London: BioMed Central Ltd.
著錄名稱、卷期、頁數 BMC Bioinformatics 14 Suppl.4, pp.1-11
摘要 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.
關鍵字
語言 en
ISSN 1471-2105
期刊性質 國外
收錄於
產學合作
通訊作者 Pai, Tun-Wen
審稿制度 是
國別 GBR
公開徵稿
出版型式 電子版
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