National Taiwan Ocean University Institutional Repository:Item 987654321/40195
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 28611/40652
造访人次 : 772688      在线人数 : 54
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 进阶搜寻

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40195

题名: Protein-ligand Binding Region Prediction (PLB-SAVE) based on Geometric Features and GPU Acceleration
作者: Ying-Tsang Lo;Hsin-Wei Wang;Tun-Wen Pai;Wen-Shoung Tzou;Hui-Huang Hsu;Hao-Teng Chang
贡献者: 國立臺灣海洋大學:資訊工程學系
日期: 2013
上传时间: 2017-01-16T03:47:11Z
出版者: BMC Bioinformatics
摘要: 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.
關聯: 13(3)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40195
显示于类别:[資訊工程學系] 期刊論文

文件中的档案:

档案 描述 大小格式浏览次数
index.html0KbHTML103检视/开启


在NTOUR中所有的数据项都受到原著作权保护.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈