Abstract
To carry out functional annotation of proteins, the most crucial step is to identify the ligand binding site (LBS) information. Although several algorithms have been reported to identify the LBS, most have limited accuracy and efficiency while considering the number and type of geometrical and physio-chemical features used for such predictions. In this proposed work, a fast and accurate algorithm “PROcket” has been implemented and discussed. The algorithm uses grid-based approach to cluster the local residue neighbors that are present on the solvent accessible surface of proteins. Further with inclusion of selected physio-chemical properties and phylogenetically conserved residues, the algorithm enables accurate detection of the LBS. A comparative study with well-known tools; LIGSITE, LIGSITECS, PASS and CASTptool was performed to analyze the performance of our tool. A set of 48 ligand-bound protein structures from different families were used to compare the performance of the tools. The PROcket algorithm outperformed the existing methods in terms of quality and processing speed with 91% accuracy while considering top 3 rank pockets and 98% accuracy considering top 5 rank pockets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dutta, S., et al.: Data deposition and annotation at the worldwide protein data bank. Mol. Biotechnol. 42(1), 1–13 (2009)
Craig, I.R., Pfleger, C., Gohlke, H., Essex, J.W., Spiegel, K.: Pocket-space maps to identify novel binding-site conformations in proteins. J. Chem. Inf. Model. 51(10), 2666–2679 (2011)
Katchalski-Katzir, E., Shariv, I., Eisenstein, M., Friesem, A.A., Aflalo, C., Vakser, I.A.: Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc. Natl. Acad. Sci. 89(6), 2195–2199 (1992)
Jones, S., Thornton, J.M.: Principles of protein-protein interactions. Proc. Natl. Acad. Sci. 93(1), 13–20 (1996)
Heifetz, A., Katchalski-Katzir, E., Eisenstein, M.: Electrostatics in protein–protein docking. Protein Sci. 11(3), 571–587 (2002)
Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins: Struct. Funct. Bioinf. 47(4), 409–443 (2002)
Levitt, D.G., Banaszak, L.J.: POCKET: a computer graphies method for identifying and displaying protein cavities and their surrounding amino acids. J. Mol. Graph. 10(4), 229–234 (1992)
Delaney, J.S.: Finding and filling protein cavities using cellular logic operations. J. Mol. Graph. 10(3), 174–177 (1992)
Del Carpio, C.A., Takahashi, Y., Sasaki, S.I.: A new approach to the automatic identification of candidates for ligand receptor sites in proteins: (I) search for pocket regions. J. Mol. Graph. 11(1), 23–29 (1993)
Kleywegt, G.J., Jones, T.A.: Detection, delineation, measurement and display of cavities in macromolecular structures. Acta Crystallogr. Sect. D: Biol. Crystallogr. 50(2), 178–185 (1994)
Masuya, M., Doi, J.: Detection and geometric modeling of molecular surfaces and cavities using digital mathematical morphological operations. J. Mol. Graph. 13(6), 331–336 (1995)
Peters, K.P., Fauck, J., Frömmel, C.: The automatic search for ligand binding sites in proteins of known three-dimensional structure using only geometric criteria. J. Mol. Biol. 256(1), 201–213 (1996)
Hendlich, M., Rippmann, F., Barnickel, G.: LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J. Mol. Graph. Model. 15(6), 359–363 (1997)
Huang, B., Schroeder, M.: LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct. Biol. 6(1), 19 (2006)
Ruppert, J., Welch, W., Jain, A.N.: Automatic identification and representation of protein binding sites for molecular docking. Protein Sci. 6(3), 524–533 (1997)
Liang, J., Woodward, C., Edelsbrunner, H.: Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci. 7(9), 1884–1897 (1998)
Dundas, J., Ouyang, Z., Tseng, J., Binkowski, A., Turpaz, Y., Liang, J.: CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res. 34(Suppl_2), W116–W118 (2006)
Brady, G.P., Stouten, P.F.: Fast prediction and visualization of protein binding pockets with PASS. J. Comput. Aided Mol. Des. 14(4), 383–401 (2000)
Venkatachalam, C.M., Jiang, X., Oldfield, T., Waldman, M.: LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graph. Model. 21(4), 289–307 (2003)
An, J., Totrov, M., Abagyan, R.: Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol. Cell. Proteomics 4(6), 752–761 (2005)
Nayal, M., Honig, B.: On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins: Struct. Funct. Bioinf. 63(4), 892–906 (2006)
Glaser, F., Morris, R.J., Najmanovich, R.J., Laskowski, R.A., Thornton, J.M.: A method for localizing ligand binding pockets in protein structures. PROTEINS: Struct. Funct. Bioinf. 62(2), 479–488 (2006)
Kawabata, T., Go, N.: Detection of pockets on protein surfaces using small and large probe spheres to find putative ligand binding sites. Proteins: Struct. Funct. Bioinf. 68(2), 516–529 (2007)
Kim, D., Cho, C.H., Cho, Y., Ryu, J., Bhak, J., Kim, D.S.: Pocket extraction on proteins via the Voronoi diagram of spheres. J. Mol. Graph. Model. 26(7), 1104–1112 (2008)
McGovern, S.L., Shoichet, B.K.: Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J. Med. Chem. 46(14), 2895–2907 (2003)
Bhinge, A., Chakrabarti, P., Uthanumallian, K., Bajaj, K., Chakraborty, K., Varadarajan, R.: Accurate detection of protein: ligand binding sites using molecular dynamics simulations. Structure 12(11), 1989–1999 (2004)
Yang, A.Y.C., Källblad, P., Mancera, R.L.: Molecular modelling prediction of ligand binding site flexibility. J. Comput. Aided Mol. Des. 18(4), 235–250 (2004)
Murga, L.F., Ondrechen, M.J., Ringe, D.: Prediction of interaction sites from apo 3D structures when the holo conformation is different. Proteins: Struct. Funct. Bioinf. 72(3), 980–992 (2008)
Foote, J., Raman, A.: A relation between the principal axes of inertia and ligand binding. Proc. Natl. Acad. Sci. 97(3), 978–983 (2000)
Berman, H.M., et al.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)
Pettersen, E.F., et al.: UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25(13), 1605–1612 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Semwal, R., Aier, I., Varadwaj, P.K., Antsiperov, S. (2019). PROcket, an Efficient Algorithm to Predict Protein Ligand Binding Site. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11465. Springer, Cham. https://doi.org/10.1007/978-3-030-17938-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-17938-0_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17937-3
Online ISBN: 978-3-030-17938-0
eBook Packages: Computer ScienceComputer Science (R0)