Prediction of Drug Binding Affinities by Comparative Binding Energy Analysis

• Published in: Journal of medicinal chemistry Vol. 38; no. 14; pp. 2681 - 2691
• Main Authors: Ortiz, Angel R, Pisabarro, M. Teresa, Gago, Federico, Wade, Rebecca C
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01-07-1995
• Subjects: Binding Sites; Binding, Competitive; Biological and medical sciences; Electricity; General pharmacology; Humans; Medical sciences; Models, Molecular; Pharmaceutical Preparations - chemistry; Pharmaceutical Preparations - metabolism; Pharmacology. Drug treatments; Phospholipases A - antagonists & inhibitors; Phospholipases A - chemistry; Phospholipases A2; Physicochemical properties. Structure-activity relationships; Structure-Activity Relationship; Synovial Fluid - enzymology; Thermodynamics; Human; Statistical analysis; Enzyme; Prediction; Enzyme inhibitor; Esterases; Phospholipase A; Carboxylic ester hydrolases; Synovial fluid; Biological fixation; Structure activity relation; Hydrolases; Affinity; Computer aid; Interaction energy; Comparative study
• ISSN: 0022-2623; 1520-4804
• DOI: 10.1021/jm00014a020

A new computational method for deducing quantitative structure-activity relationships (QSARs) using structural data from ligand-macromolecule complexes is presented. First, the ligand-macromolecule interaction energy is computed for a set of ligands using molecular mechanics calculations. Then, by selecting and scaling components of the ligand-macromolecule interaction energy that show good predictive ability, a regression equation is obtained in which activity is correlated with the interaction energies of parts of the ligands and key regions of the macromolecule. Application to the interaction of the human synovial fluid phospholipase A2 with 26 inhibitors indicates that the derived QSAR has good predictive ability and provides insight into the mechanism of enzyme inhibition. The method, which we term comparative binding energy (COMBINE) analysis, is expected to be applicable to ligand-receptor interactions in a range of contexts including rational drug design, host-guest systems, and protein engineering.