We present a binding free energy function that consists of force field terms supplemented by solvation terms. We used this function to calibrate the solvation model along with the binding interaction terms in a self-consistent manner. The motivation for this approach was that the solute dielectric-constant dependence of calculated hydration gas-to-water transfer free energies is markedly different from that of binding free energies (J. Comput. Chem. 2003, 24, 954). Hence, we sought to calibrate directly the solvation terms in the context of a binding calculation. The five parameters of the model were systematically scanned to best reproduce the absolute binding free energies for a set of 99 protein-ligand complexes. We obtained a mean unsigned error of 1.29 kcal/mol for the predicted absolute binding affinity in a parameter space that was fairly shallow near the optimum. The lowest errors were obtained with solute dielectric values of Din = 20 or higher and scaling of the intermolecular van der Waals interaction energy by factors ranging from 0.03 to 0.15. The high apparent Din and strong van der Waals scaling may reflect the anticorrelation of the change in solvated potential energy and configurational entropy, that is, enthalpy-entropy compensation in ligand binding (Biophys. J. 2004, 87, 3035-3049). Five variations of preparing the protein-ligand data set were explored in order to examine the effect of energy refinement and the presence of bound water on the calculated results. We find that retaining water in the final protein structure used for calculating the binding free energy is not necessary to obtain good results; that is the continuum solvation model is sufficient. Virtual screening enrichment studies on estrogen receptor and thymidine kinase showed a good ability of the binding free energy function to recover true hits in a collection of decoys.

Computational mapping places molecular probes-small molecules or functional groups-on a protein surface to identify the most favorable binding positions. Although x-ray crystallography and NMR show that organic solvents bind to a limited number of sites on a protein, current mapping methods result in hundreds of energy minima and do not reveal why some sites bind molecules with different sizes and polarities. We describe a mapping algorithm that explains the origin of this phenomenon. The algorithm has been applied to hen egg-white lysozyme and to thermolysin, interacting with eight and four different ligands, respectively. In both cases the search finds the consensus site to which all molecules bind, whereas other positions that bind only certain ligands are not necessarily found. The consensus sites are pockets of the active site, lined with partially exposed hydrophobic residues and with a number of polar residues toward the edge. These sites can accommodate each ligand in a number of rotational states, some with a hydrogen bond to one of the nearby donor͞acceptor groups. Specific substrates and͞or inhibitors of hen egg-white lysozyme and thermolysin interact with the same side chains identified by the mapping, but form several hydrogen bonds and bind in unique orientations.T he mapping of a protein by experimental or computational tools involves placing molecular probes-small organic molecules or functional groups-around the protein surface to determine the most favorable binding positions. Larger molecules that are candidates for high affinity ligands can be constructed by combining the probes at (or near) their optimal binding sites. This site-mapping and fragment-assembly strategy provides an important approach to drug design (1-6). Experimental approaches to mapping include x-ray crystallography (7-10) and NMR techniques (11, l2). In the multiple solvent crystal structure method (7-10), the x-ray structure of a protein is repeatedly solved in a variety of organic solvents, each representing a different functional group. In NMR methods, the binding of small molecules in aqueous solution is detected by chemical shifts of the protein and by the observation of intermolecular nuclear Overhauser effects (NOEs) between protons of the protein and the ligand (11).Because the probes are generally unrelated to any natural substrate of the protein, one would expect largely nonspecific binding. However, both x-ray crystallography (7-10) and NMR (11) reveal only a limited number of bound ligand positions, and a pocket of the active site tends to form a consensus site that binds many ligands, irrespective of their sizes and polarities. An NMR study by Liepinsh and Otting (11) shows that methanol, methylene chloride, acetonitrile (CCN), acetone (ACN), DMSO, isopropanol (IPA), t-butanol, and urea all bind to the specificity-determining site (site C) of the hen egg-white lysozyme (HEWL). Recent multiple solvent crystal structure studies of thermolysin (TLN) (9, 10) show that IPA, ACN, CCN, and phenol (IPH) bind preferenti...

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The SAMPL-1 hydration free energy blind prediction challenge data set includes 63 compounds that are more chemically diverse, polyfunctional, drug-like, and with examples of transfer free energies and molecular weights larger than ever before seen in previously tabulated data sets of neutral compounds. For the prospective SAMPL-1 study, we employed a continuum model including a boundary element solution of the Poisson equation to describe electrostatic solvation, a molecular surface area-based cost of cavity formation in water, and a continuum Lennard-Jones potential to account for dispersion-repulsion solute-solvent effects. For the latter contribution, continuum van der Waals atom-type coefficients were calibrated and validated on previously available hydration data sets. In the prospective study, this continuum hydration model yielded SAMPL-1 predictions highly correlated with experimental data, albeit with a slope of slightly above 0.5, suggesting a valid model but with a systematic error. Analysis of the major outliers, all overestimating the experimental hydration data, highlights a common structural theme as a possible cause of the prediction errors: densely polar and hydrogen-bond-capable structures, featuring primarily substituted (sulfon)amide groups, often in conjugated systems. By examining analog pairs within the SAMPL-1 data set, it was also noted that certain solvation trends are captured neither by chemical sense nor by our hydration model, which seem too additive. A retrospective analysis of model transferability between hydration data sets as a function of its parameters and complexity indicates that the electrostatic component of the model is fairly transferrable across data sets, but the nonelectrostatic terms are less so. For the chemical space covered in SAMPL-1, absolute prediction errors indicate that the simpler transferrable electrostatics-only model outperforms the more complex model including cavity and continuum dispersion terms. Possible directions to further improve this continuum hydration model are proposed.

Free energy potentials, combining molecular mechanics with empirical solvation and entropic terms, are used to discriminate native and near‐native protein conformations from slightly misfolded decoys. Since the functional forms of these potentials vary within the field, it is of interest to determine the contributions of individual free energy terms and their combinations to the discriminative power of the potential. This is achieved in terms of quantitative measures of discrimination that include the correlation coefficient between RMSD and free energy, and a new measure labeled the minimum discriminatory slope (MDS). In terms of these criteria, the internal energy is shown to be a good discriminator on its own, which implies that even well‐constructed decoys are substantially more strained than the native protein structure. The discrimination improves if, in addition to the internal energy, the free energy expression includes the electrostatic energy, calculated by assuming non‐ionized side chains, and an empirical solvation term, with the classical atomic solvation parameter model providing slightly better discrimination than a structure‐based atomic contact potential. Finally, the inclusion of a term representing the side chain entropy change, and calculated by an established empirical scale, is so inaccurate that it makes the discrimination worse. It is shown that both the correlation coefficient and the MDS value (or its dimensionless form) are needed for an objective assessment of a potential, and that together they provide much more information on the origins of discrimination than simple inspection of the RMSD‐free energy plots. Proteins 2000;41:518–534. © 2000 Wiley‐Liss, Inc.

Computational mapping methods place molecular probes (small molecules or functional groups) on a protein surface to identify the most favorable binding positions by calculating an interaction potential. We have developed a novel computational mapping program called CS-Map (computational solvent mapping of proteins), which differs from earlier mapping methods in three respects: (i) it initially moves the ligands on the protein surface toward regions with favorable electrostatics and desolvation, (ii) the final scoring potential accounts for desolvation, and (iii) the docked ligand positions are clustered, and the clusters are ranked on the basis of their average free energies. To understand the relative importance of these factors, we developed alternative algorithms that use the DOCK and GRAMM programs for the initial search. Because of the availability of experimental solvent mapping data, lysozyme and thermolysin are considered as test proteins. Both DOCK and GRAMM speed up the initial search, and the combined algorithms yield acceptable mapping results. However, the DOCK-based approaches place the consensus site farther from its experimentally determined position than CS-Map, primarily because of the lack of a solvation term in the initial search. The GRAMM-based program also finds the correct consensus site for thermolysin. We conclude that good sampling is the most important requirement for successful mapping, but accounting for desolvation and clustering of ligand positions also help to reduce the number of false positives.

Structural studies of the ligand-binding domain (LBD) of several steroid receptors have revealed that the dynamic properties of the C-terminal helix 12 (H12) are the major determinant of the activation mode of these receptors. H12 exhibits high mobility and different conformations in the absence of ligand. Upon ligand binding, H12 is stabilized in a precise position to seal the ligand-binding pocket and finalize the assembly of the activation function (AF-2) domain. In this study, we investigated the role of the conserved proline 892 of the androgen receptor (AR) in directing the dynamic location and orientation of the AR-H12. We used a combined approach including kinetic and biochemical assays with molecular dynamic simulations to analyze two substitutions (P892A and P892L) identified in individuals with complete androgen insensitivity syndrome. Our analyses revealed distinct mechanisms by which these substitutions impair H12 function resulting in severely defective receptors. The AR-P892A receptor exhibited reduced ligand binding and transactivational potential because of an increased flexibility in H12. The AR-P892L substitution renders the receptor inactive due to a distorted, unstructured and misplaced H12. To confirm the mutants' inability to stabilize H12 in an active position, we have developed a novel in vivo assay to evaluate the accessibility of the H12-docking site on the AR-LBD surface. An extrinsic AR-H12 peptide was able to interact with wild-type and mutant LBDs in the absence of ligand. Ligand-induced proper positioning of the intrinsic H12 of wild-type AR prevented these interactions, whereas the misplacement of the mutants' H12 did not. Proline at this position may be critical for H12 dynamics not only in the AR, but also in other nuclear receptors where this proline is conserved.

To understand water-protein interactions in solution, the electrostatic field is calculated by solving the Poisson-Boltzmann equation, and the free energy surface of water is mapped by translating and rotating an explicit water molecule around the protein. The calculation is applied to T4 lysozyme with data available on the conservation of solvent binding sites in 18 crystallographically independent molecules. The free energy maps around the ordered water sites provide information on the relationship between water positions in crystal structure and in solution. Results show that almost all conserved sites and the majority of nonconserved sites are within 1.3 A of local free energy minima. This finding is in sharp contrast to the behavior of randomly placed water molecules in the boundary layer, which, on the average, must travel more than 3 A to the nearest free energy minimum. Thus, the solvation sites are at least partially determined by protein-water interactions rather than by crystal packing alone. The characteristic water residence times, obtained from the free energies at the local minima, are in good agreement with nuclear magnetic resonance experiments. Only about half of the potential sites show up as ordered water in the 1.7 A resolution X-ray structure. Crystal packing interactions can stabilize weak or mobile potential sites (in fact, some ordered water positions are not close to free energy minima) or can prevent water from occupying certain sites. Apart from a few buried water molecules that are strong binders, the free energies are not very different for conserved and nonconserved sites. We show that conservation of a water site between two crystals occurs if the positions of protein atoms, primarily contributing to the free energy at the local minimum, do not substantially change from one structure to the other. This requirement can be correlated with the nature of the side chain contacting the water molecule in the site.