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In silico identification and characterization of protein-ligand binding sites

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Roche, D. B. and McGuffin, L. J. (2016) In silico identification and characterization of protein-ligand binding sites. In: Computational design of ligand binding proteins. Methods in Molecular Biology, 1414. Springer, pp. 1-21. ISBN 9781493935673

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To link to this item DOI: 10.1007/978-1-4939-3569-7_1

Abstract/Summary

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:64582
Publisher:Springer

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