The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure

Abstract

A new simple empirical function has been developed that estimates the free energy of binding for a given protein-ligand complex of known 3D structure. The function takes into account hydrogen bonds, ionic interactions, the lipophilic protein-ligand contact surface and the number of rotatable bonds in the ligand. The dataset for the calibration of the function consists of 45 protein-ligand complexes. The new energy function reproduces the binding constants (ranging from 2.5.10(-2) to 4.10(-14) M, corresponding to binding energies between -9 and -76 kJ/mol) of the dataset with a standard deviation of 7.9 kJ/mol, corresponding to 1.4 orders of magnitude in binding affinity. The individual contributions to protein-ligand binding obtained from the scoring function are: ideal neutral hydrogen bond: -4.7 kJ/mol; ideal ionic interaction: -8.3 kJ/mol; lipophilic contact: -0.17 kJ/mol A2; one rotatable bond in the ligand: +1.4 kJ/mol. The function also contains a constant contribution (+5.4 kJ/mol) which may be rationalized as loss of translational and rotational entropy. The function can be evaluated very fast and is therefore also suitable for application in a 3D database search or de novo ligand design program such as LUDI.

On the calculation of binding free energies using continuum methods: Application to MHC class I protein‐peptide interactions

“…Molecular dynamics simulations including the explicit treatment of solvent molecules have encountered a number of striking successes (see, e.g., Bash et al, 1987;McCammon, 1987;Beveridge & DiCapua, 1989;Straatsma & McCammon, 1991;Miyamoto & Kollman, 1993;Kollman, 1993Kollman, , 1994; however, the need to sample a large ensemble of conformational states places severe computational demands on this approach. At the other extreme, a variety of empirical methods have also been developed to estimate binding free energies (see, e.g., Andrews et al, 1984;Williams et al, 1991Williams et al, , 1993Bohm, 1994). These methods, which, for example, assign a free energy value to each hydrogen bond, salt bridge, and buried nonpolar area, provide extremely useful qualitative tools.…”

Complex Structure Modeling of p38 MAP Kinase and Pyridyl-pyrrole Compounds

“…Recently empirical and knowledge-based scoring functions have been developed. Böhm et al reported that the score value calculated by the Ludi program well approximated the binding affinity [8,9]. In one of their studies, a correlation with a standard deviation of 1.7 kcal/mol (number of structures = 82, correlation coefficient = 0.89) was obtained between the experimental and calculated binding free energies derived from K i values.…”

“…The score value was reported to correspond to the dissociation constant K i by Böhm et al [8] [9]. They derived the empirical parameters of the score function from the protein-ligand complexes of known 3D structure in the Protein Data Bank [10].…”

“…In this study, two kinds of score functions were used, i.e., score1 and score2, to calculate the score values of inhibitors complexed with p38 MAP kinase. Score1 and score2 were derived from 45 and 82 complexes, respectively [8] [9]. …”

Virtual Screening Strategies: A State of Art to Combat with Multiple Drug Resistance Strains

“…Three types of scoring functions are currently applied in currently available docking programs. These scoring functions are based on: force field [41,42], empirical approaches [43,44] and knowledge based [45,46]. Discriminating or predictive power of these functions have high influence on an outcome of any docking protocol.…”