BSiteFinder, an Improved Protein-binding Sites Prediction Server Based on Structural Alignment: More Accurate and Less Time-consuming

Overview

Journal J Cheminform

Publisher Biomed Central

Specialty Chemistry

Date 2016 Jul 13

PMID 27403208

Citations 11

Authors

Jun Gao

Qingchen Zhang

Min Liu

Lixin Zhu

Dingfeng Wu

Zhiwei Cao

Ruixin Zhu

Affiliations

Soon will be listed here.

Abstract

Motivation: Protein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed.

Results: By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset. For 210 bound dataset, bSiteFinder achieved high accuracies up to 94.8 % (MCC 0.95). For another 48 bound/unbound dataset, bSiteFinder achieved high accuracies up to 93.8 % for bound proteins (MCC 0.95) and 85.4 % for unbound proteins (MCC 0.72). Our bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/, and the source code is provided at the methods page.

Conclusion: An online bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/. Our work lays a foundation for functional annotation of protein and structure-based drug design. With ever increasing numbers of three-dimensional protein-ligand complex structures, our server should be more accurate and less time-consuming.Graphical Abstract bSiteFinder (http://binfo.shmtu.edu.cn/bsitefinder/) as a protein-binding sites prediction server was developed based on the largest database of bound templates so far with stringent quality control. By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server have been significantly improved. What's more, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset.

Citing Articles

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

Zhao Y, He S, Xing Y, Li M, Cao Y, Wang X Int J Mol Sci. 2024; 25(17).

PMID: 39273227 PMC: 11394757. DOI: 10.3390/ijms25179280.

Protein ligand binding site prediction using graph transformer neural network.

Ishitani R, Takemoto M, Tomii K PLoS One. 2024; 19(8):e0308425.

PMID: 39106255 PMC: 11302905. DOI: 10.1371/journal.pone.0308425.

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures.

Carbery A, Buttenschoen M, Skyner R, von Delft F, Deane C J Cheminform. 2024; 16(1):32.

PMID: 38486231 PMC: 10941399. DOI: 10.1186/s13321-024-00821-4.

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.

Popov P, Kalinin R, Buslaev P, Kozlovskii I, Zaretckii M, Karlov D Brief Bioinform. 2023; 25(1).

PMID: 38113077 PMC: 10783863. DOI: 10.1093/bib/bbad459.

A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions.

Gu L, Li B, Ming D BMC Bioinformatics. 2022; 23(1):456.

PMID: 36324073 PMC: 9628359. DOI: 10.1186/s12859-022-04995-2.

References

Laurie A, Jackson R . Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. Curr Protein Pept Sci. 2006; 7(5):395-406. DOI: 10.2174/138920306778559386. View

Rausell A, Juan D, Pazos F, Valencia A . Protein interactions and ligand binding: from protein subfamilies to functional specificity. Proc Natl Acad Sci U S A. 2010; 107(5):1995-2000. PMC: 2808218. DOI: 10.1073/pnas.0908044107. View

Brylinski M, Skolnick J . A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc Natl Acad Sci U S A. 2008; 105(1):129-34. PMC: 2224172. DOI: 10.1073/pnas.0707684105. View

Wass M, Kelley L, Sternberg M . 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res. 2010; 38(Web Server issue):W469-73. PMC: 2896164. DOI: 10.1093/nar/gkq406. View

Levitt D, BANASZAK L . POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph. 1992; 10(4):229-34. DOI: 10.1016/0263-7855(92)80074-n. View

Dai T, Liu Q, Gao J, Cao Z, Zhu R . A new protein-ligand binding sites prediction method based on the integration of protein sequence conservation information. BMC Bioinformatics. 2012; 12 Suppl 14:S9. PMC: 3287474. DOI: 10.1186/1471-2105-12-S14-S9. View

Ngan C, Hall D, Zerbe B, Grove L, Kozakov D, Vajda S . FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics. 2011; 28(2):286-7. PMC: 3259439. DOI: 10.1093/bioinformatics/btr651. View

Hendlich M, Rippmann F, Barnickel G . LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model. 1998; 15(6):359-63, 389. DOI: 10.1016/s1093-3263(98)00002-3. View

Xie Z, Liu C, Hsiao F, Yao A, Hwang M . LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites. Nucleic Acids Res. 2013; 41(Web Server issue):W292-6. PMC: 3692107. DOI: 10.1093/nar/gkt300. View

10.

Laurie A, Jackson R . Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics. 2005; 21(9):1908-16. DOI: 10.1093/bioinformatics/bti315. View

11.

Ortiz A, Strauss C, Olmea O . MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. Protein Sci. 2002; 11(11):2606-21. PMC: 2373724. DOI: 10.1110/ps.0215902. View

12.

Weisel M, Proschak E, Schneider G . PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem Cent J. 2007; 1:7. PMC: 1994066. DOI: 10.1186/1752-153X-1-7. View

13.

Fox N, Brenner S, Chandonia J . SCOPe: Structural Classification of Proteins--extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Res. 2013; 42(Database issue):D304-9. PMC: 3965108. DOI: 10.1093/nar/gkt1240. View

14.

Liang S, Zhang C, Liu S, Zhou Y . Protein binding site prediction using an empirical scoring function. Nucleic Acids Res. 2006; 34(13):3698-707. PMC: 1540721. DOI: 10.1093/nar/gkl454. View

15.

Roy A, Yang J, Zhang Y . COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res. 2012; 40(Web Server issue):W471-7. PMC: 3394312. DOI: 10.1093/nar/gks372. View

16.

Prlic A, Yates A, Bliven S, Rose P, Jacobsen J, Troshin P . BioJava: an open-source framework for bioinformatics in 2012. Bioinformatics. 2012; 28(20):2693-5. PMC: 3467744. DOI: 10.1093/bioinformatics/bts494. View

17.

Greer J, Erickson J, Baldwin J, Varney M . Application of the three-dimensional structures of protein target molecules in structure-based drug design. J Med Chem. 1994; 37(8):1035-54. DOI: 10.1021/jm00034a001. View

18.

Lichtarge O, Bourne H, Cohen F . An evolutionary trace method defines binding surfaces common to protein families. J Mol Biol. 1996; 257(2):342-58. DOI: 10.1006/jmbi.1996.0167. View

19.

Xiao X, Min J, Lin W, Liu Z, Cheng X, Chou K . iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J Biomol Struct Dyn. 2014; 33(10):2221-33. DOI: 10.1080/07391102.2014.998710. View

20.

Pradeep H, Rajanikant G . Computational prediction of a putative binding site on drp1: implications for antiparkinsonian therapy. J Chem Inf Model. 2014; 54(7):2042-50. DOI: 10.1021/ci500243h. View