title: Ligand binding site similarity identification based on chemical and geometric similarity.
abstract: The similarity comparison of binding sites based on amino acid between different proteins can facilitate protein function identification. However, Binding site usually consists of several crucial amino acids which are frequently dispersed among different regions of a protein and consequently make the comparison of binding sites difficult. In this study, we introduce a new method, named as chemical and geometric similarity of binding site (CGS-BSite), to compute the ligand binding site similarity based on discrete amino acids with maximum-weight bipartite matching algorithm. The principle of computing the similarity is to find a Euclidean Transformation which makes the similar amino acids approximate to each other in a geometry space, and vice versa. CGS-BSite permits site and ligand flexibilities, provides a stable prediction performance on the flexible ligand binding sites. Binding site prediction on three test datasets with CGS-BSite method has similar performance to Patch-Surfer method but outperforms other five tested methods, reaching to 0.80, 0.71 and 0.85 based on the area under the receiver operating characteristic curve, respectively. It performs a marginally better than Patch-Surfer on the binding sites with small volume and higher hydrophobicity, and presents good robustness to the variance of the volume and hydrophobicity of ligand binding sites. Overall, our method provides an alternative approach to compute the ligand binding site similarity and predict potential special ligand binding sites from the existing ligand targets based on the target similarity.
authors.affiliation: Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
authors.forename: Haibo
authors.initials: H
authors.lastname: Tu
authors.forename: Tieliu
authors.initials: T
authors.lastname: Shi
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008024:Ligands; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D008024:Ligands; D011506:Proteins