Quantum Chemical Associations Ligand−Residue: Their Role to Predict Flavonoid Binding Sites in Proteins

• Published in: Journal of chemical information and modeling Vol. 50; no. 5; pp. 924 - 933
• Main Authors: Rolo-Naranjo, Alberto, Codorniu-Hernández, Edelsys, Ferro, Noel
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 24-05-2010
• Subjects: Amino acids; Binding Sites; Bioinformatics; Biological and medical sciences; Crystal structure; Crystallography, X-Ray; Drug Design; Entropy; Flavonoids - metabolism; Fundamental and applied biological sciences. Psychology; General aspects; General pharmacology; Humans; Interactions. Associations; Intermolecular phenomena; Ligands; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Medical sciences; Metabolites; Models, Biological; Models, Molecular; Molecular biophysics; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Protein Binding; Proteins; Proteins - chemistry; Proteins - metabolism; Quantum Theory; Ligand binding; Pharmaceutical technology; Quantum chemistry; Geometrical parameter; Molecular interaction; Binding site; Medical screening; Screening; Binding protein; Cavity; Scoring credit; Residue; Quantum mechanics; Molecular biology; Bioinformatics; Protein structure; Crystalline structure
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci900358z

A novel approach is applied for the prediction of potential binding sites in ligand−protein interactions. This methodology introduces an integral strategy based on the calculation of protein geometrical parameters and the use of a quantum mechanical descriptor, Binding Local Site (B LS). A screening of the most likely cavities in the protein crystal structure is carried out where the analysis of geometric cavities is performed, and the virtual centers for binding (VCB) are located. The VCB surrounding amino acid residues (AA) are evaluated through the calculation of the B LS by using the theoretical affinity order between the ligand and each AA. It includes a quantum scoring function based on the ligand−AA association energies and entropies. A contribution to the understanding of flavonoid−protein interactions is provided as well. The new bioinformatic strategy makes good predictions for flavonoid ligands. The calculated binding sites are quite in agreement with the crystal binding sites of 10 flavonoid binding proteins. This is a contribution of quantum mechanics in some phases of in silico drug design.