Combination of a Modified Scoring Function with Two-Dimensional Descriptors for Calculation of Binding Affinities of Bulky, Flexible Ligands to Proteins

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Abstract

Bulky, flexible molecules such as peptides and peptidomimetics are often used as lead compounds during the drug discovery process. Pathophysiological events, e.g., the formation of amyloid fibrils in Alzheimer's disease, the conformational changes of prion proteins, or beta-secretase activity, may be successfully hindered by the use of rationally designed peptide sequences. A key step in the molecular engineering of such potent lead compounds is the prediction of the energetics of their binding to the macromolecular targets. Although sophisticated experimental and in silico methods are available to help this issue, the structure-based calculation of the binding free energies of large, flexible ligands to proteins is problematic. In this study, a fast and accurate calculation strategy is presented, following modification of the scoring function of the popular docking program package AutoDock and the involvement of ligand-based two-dimensional descriptors. Quantitative structure-activity relationships with good predictive power were developed. Thorough cross-validation tests and verifications were performed on the basis of experimental binding data of biologically important systems. The capabilities and limitations of the ligand-based descriptors were analyzed. Application of these results in the early phase of lead design will contribute to precise predictions, correct selections, and consequently a higher success rate of rational drug discovery.

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