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This article is part of the supplement: Selected Articles on Computational Vaccinology

Open Access Research

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

Ying-Tsang Lo1, Hsin-Wei Wang1, Tun-Wen Pai13*, Wen-Shoung Tzou23, Hui-Huang Hsu4 and Hao-Teng Chang56

Author Affiliations

1 Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan, R.O.C

2 Department of Life Sciences, National Taiwan Ocean University, Keelung, Taiwan, R.O.C

3 Center of Excellence for Marine Bioenvironment and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan, R.O.C

4 Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan, R.O.C

5 Graduate Institute of Molecular Systems Biomedicine, China Medical University, Taichung, Taiwan, R.O.C

6 China Medical University Hospital, Taichung, Taiwan, R.O.C

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BMC Bioinformatics 2013, 14(Suppl 4):S4  doi:10.1186/1471-2105-14-S4-S4

Published: 8 March 2013

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 webcite.