Ribonucleic acid (RNA) molecules play central roles in a variety of biological processes and, hence, are attractive targets for therapeutic intervention. In recent years, molecular docking techniques have become one of the most popular and successful approaches in drug discovery; however, almost all docking programs are protein based. The adaptability of popular docking programs in RNA world ha...

Developing chemicals that inhibit checkpoint kinase 1 (Chk1) is a promising adjuvant therapeutic to improve the efficacy and selectivity of DNA-targeting agents. Reliable prediction of binding-free energy and binding affinity of Chk1 inhibitors can provide a guide for rational drug design. In this study, multiple docking strategies and Prime/Molecular Mechanics Generalized Born Surface Area (Pr...

Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations ...

Treating flexibility in molecular docking is a major challenge in cell biology research. Here we describe the background and the principles of existing flexible protein-protein docking methods, focusing on the algorithms and their rational. We describe how protein flexibility is treated in different stages of the docking process: in the preprocessing stage, rigid and flexible parts are identifi...

In order to improve docking score correction, we developed several structure-based quantitative structure activity relationship (QSAR) models by protein-drug docking simulations and applied these models to public affinity data. The prediction models used descriptor-based regression, and the compound descriptor was a set of docking scores against multiple (∼600) proteins including nontargets. Th...

In this study, a new molecular docking method is presented to improve the docking accuracy. We introduce to docking design a concept of residue groups based on induced-fit and use K Score (a kind of PMF scoring function) to score the docking position. Genetic algorithm with the multi-population evolution and entropy-based searching technique with narrowing down space is used to solve the optimi...

Physical interactions between proteins are involved in many important cell functions and are key for understanding the mechanisms of biological processes. Protein-protein docking programs provide a means to computationally construct three-dimensional (3D) models of a protein complex structure from its component protein units. A protein docking program takes two or more individual 3D protein str...

MOTIVATION The limited success rate of protein-protein docking procedures is generally attributed to structure differences between the bound and unbound states of the molecules. Herein we analyze a large dataset of protein-protein docking results and identify additional parameters that affect the performance of docking procedures. RESULTS We find that the distinction between nearly correct mo...

Based on the results of several groups using different docking methods, the key properties that determine the expected success rate in protein-protein docking calculations are measures of conformational change, interface area, and hydrophobicity. A classification of protein complexes in terms of these measures provides a prediction of docking difficulty. This classification is used to study the...