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The thermodynamics of molecular recognition is investigated by a statistical energy landscape approach, where the temperature profile of the ligand–protein binding process is determined using the weighted histogram analysis method. The analysis reveals differences in the binding energy landscapes of two molecular fragments with the FKBP12 protein, which are reflected in their characteristic transition temperatures. The approach provides insight into the nature of transitions between unbound and bound phases of ligand–protein complexes. One molecular fragment proceeds from the unbound phase to the native complex via a short-lived intermediate at relatively high temperature. The second fragment has a significantly more rugged binding energy landscape and goes from unbound to a long-lived nonspecific bound species consisting of isoenergetic yet structurally different binding modes, and later via a second-order-like transition to the native complex. Emerging universalities in molecular recognition and protein folding mechanisms are highlighted in the context of the kinetic partitioning mechanism. ©1999 John Wiley & Sons, Inc. Int J Quant Chem 73: 113–121, 1999

We present the results of molecular docking simulations with HIV-1 protease for the sb203386 and skf107457 inhibitors by Monte Carlo simulated annealing. A simplified piecewise linear energy function, the standard AMBER force field, and the AMBER force field with solvation and a soft-core smoothing component are employed in simulations with a single-protein conformation to determine the relationship between docking simulations with a simple energy function and more realistic force fields. The temperature-dependent binding free energy profiles of the inhibitors interacting with a single protein conformation provide a detailed picture of relative thermodynamic stability and a distribution of ligand binding modes in agreement with experimental crystallographic data. Using the simplified piecewise linear energy function, we also performed Monte Carlo docking simulations with an ensemble of protein conformations employing preferential biased sampling of low-energy protein conformations, and the results are analyzed in connection with the free energy profiles. ©1999 John Wiley & Sons, Inc. Int J Quant Chem 72: 73–84, 1999