Direct calculations of the absolute free energies of binding for eight ligands to FKBP protein were performed using the Fujitsu BioServer massively parallel computer. Using the latest version of the general assisted model building with energy refinement (AMBER) force field for ligand model parameters and the Bennett acceptance ratio for computing free-energy differences, we obtained an excellent linear fit between the calculated and experimental binding free energies. The rms error from a linear fit is 0.4 kcal/mol for eight ligand complexes. In comparison with a previous study of the binding energies of these same eight ligand complexes, these results suggest that the use of improved model parameters can lead to more predictive binding estimates, and that these estimates can be obtained with significantly less computer time than previously thought. These findings make such direct methods more attractive for use in rational drug design.

There are many unresolved questions regarding the role of water in protein folding. Does water merely induce hydrophobic forces, or does the discrete nature of water play a structural role in folding? Are the nonadditive aspects of water important in determining the folding mechanism? To help to address these questions, we have performed simulations of the folding of a model protein (BBA5) in explicit solvent. Starting 10,000 independent trajectories from a fully unfolded conformation, we have observed numerous folding events, making this work a comprehensive study of the kinetics of protein folding starting from the unfolded state and reaching the folded state and with an explicit solvation model and experimentally validated rates. Indeed, both the raw TIP3P folding rate (4.5 ؎ 2.5 s) and the diffusion-constant corrected rate (7.5 ؎ 4.2 s) are in strong agreement with the experimentally observed rate of 7.5 ؎ 3.5 s. To address the role of water in folding, the mechanism is compared with that predicted from implicit solvation simulations. An examination of solvent density near hydrophobic groups during folding suggests that in the case of BBA5, there are water-induced effects not captured by implicit solvation models, including signs of a ''concurrent mechanism'' of core collapse and desolvation.explicit solvation model ͉ distributed computing ͉ molecular dynamics W hen considering the nature of the protein folding mechanism, because of the dominance of the hydrophobic effect, one must consider the role of water. Water can be examined explicitly by studying discrete water molecules. However, it is common to consider the role of water implicitly in terms of its bulk dielectric property and interaction with hydrophobic groups of the protein. Implicit solvation methods have been widely adopted in the computational study of folding dynamics, where such dielectric and hydrophobic properties are accounted for with continuum models. In addition, it is interesting to consider that experiments assessing the folding mechanism (e.g., ⌽-value analysis) are typically interpreted in terms of an implicit role of water. For example, the stabilization or destabilization of protein-protein interactions is accounted for based on physical forces mediated by water, rather than an accounting of the role of specific, explicit water molecules.However, there are important properties of water which are not considered in typical implicit solvation models. In particular, the nonadditive nature of hydrophobicity leads to the so-called ''drying effect,'' in which a layer of vacuum surrounds hydrophobic surfaces and makes hydrophobic collapse cooperative (1). In addition, continuum models of water do not account for the discrete nature of water molecules, which may lead to differences in protein folding dynamics, such as a cooperative expulsion of water upon folding (2). It is also known that structured water plays a role in the folded state of many proteins (3). Accordingly, one can imagine that water may play a ''structural role'' in some o...

We report on the use of large-scale distributed computing simulation and novel analysis techniques for examining the dynamics of a small protein. Matters addressed include folding rate, very long time scale kinetics, ensemble properties, and interaction with water. The target system for the study, the villin headpiece, has been of great interest to experimentalists and theorists both. Sampling totaled nearly 500 mus-the most extensive published to date for a system of villin's size in explicit solvent with all atom detail-and was in the form of tens of thousands of independent molecular dynamics trajectories, each several tens of nanoseconds in length. We report on kinetics sensitivity analyses that, using a set of short simulations, probed the role of water in villin's folding and sensitivity to the simulation's electrostatics treatment. By constructing Markovian state models (MSMs) from the collected data, we were able to propagate dynamics to times far beyond those directly simulated and to rapidly compute mean first passage times, long time kinetics (tens of microseconds), and evolution of ensemble property distributions over long times, otherwise currently impossible. We also tested our MSM by using it to predict the structure of villin de novo.

We present a technique for biomolecular free energy calculations that exploits highly parallelized sampling to significantly reduce the time to results. The technique combines free energies for multiple, nonoverlapping configurational macrostates and is naturally suited to distributed computing. We describe a methodology that uses this technique with docking, molecular dynamics, and free energy perturbation to compute absolute free energies of binding quickly compared to previous methods. The method does not require a priori knowledge of the binding pose as long as the docking technique used can generate reasonable binding modes. We demonstrate the method on the protein FKBP12 and eight of its inhibitors.

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