Unlike other methods for docking ligands to the rigid 3D structure of a known protein receptor, Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand. In this search, an initial rough positioning and scoring phase that dramatically narrows the search space is followed by torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses. The very best candidates are further refined via a Monte Carlo sampling of pose conformation; in some cases, this is crucial to obtaining an accurate docked pose. Selection of the best docked pose uses a model energy function that combines empirical and force-field-based terms. Docking accuracy is assessed by redocking ligands from 282 cocrystallized PDB complexes starting from conformationally optimized ligand geometries that bear no memory of the correctly docked pose. Errors in geometry for the top-ranked pose are less than 1 A in nearly half of the cases and are greater than 2 A in only about one-third of them. Comparisons to published data on rms deviations show that Glide is nearly twice as accurate as GOLD and more than twice as accurate as FlexX for ligands having up to 20 rotatable bonds. Glide is also found to be more accurate than the recently described Surflex method.

Glide's ability to identify active compounds in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compound collection of a pharmaceutical or biotechnology company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that average measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of physical chemistry, particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.

We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high-throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function.

We performed studies of fluctuating charge, fluctuating dipole, and combined models for substituted benzenes and concluded that dipoles are necessary to avoid errors in important cases. Force fields incorporating fluctuating dipoles for alanine, serine, and phenylalanine were developed that accurately reproduce both relative conformational energies and cooperative many-body energies as given by ab initio quantum chemical calculations. The polarization model was fit to quantum chemical calculations of changes in the electrostatic potential (ESP) induced by applied perturbations. The electrostatic model was completed by adding fixed charges fit to the zero-field quantum chemical ESP. All intramolecular and Lennard−Jones parameters, and some torsional parameters, were taken from the OPLS-AA force field of Jorgensen and co-workers. Key torsional parameters were refit to quantum chemical structures and energies.

We have developed a polarizable force field for peptides, using all-atom OPLS (OPLS-AA) nonelectrostatic terms and electrostatics based on a fluctuating charge model and fit to ab initio calculations of polarization responses. We discuss the fitting procedure, and specific techniques we have developed that are necessary in order to obtain an accurate, stable model. Our model is comparable to the best existing molecular mechanics force fields in reproducing quantum-chemical peptide energetics. It also accurately reproduces many-body effects in many cases. We believe that straightforward extensions of our linear-response electrostatic model will significantly improve the accuracy for those cases that the present model does not adequately address.

Glide SP mode enrichment results for two preparations of the DUD dataset and native ligand docking RMSDs for two preparations of the Astex dataset are presented. Following a best-practices preparation scheme, an average RMSD of 1.140 Å for native ligand docking with Glide SP is computed. Following the same best-practices preparation scheme for the DUD dataset an average area under the ROC curve (AUC) of 0.80 and average early enrichment via the ROC (0.1 %) metric of 0.12 were observed. 74 and 56 % of the 39 best-practices prepared targets showed AUC over 0.7 and 0.8, respectively. Average AUC was greater than 0.7 for all best-practices protein families demonstrating consistent enrichment performance across a broad range of proteins and ligand chemotypes. In both Astex and DUD datasets, docking performance is significantly improved employing a best-practices preparation scheme over using minimally-prepared structures from the PDB. Enrichment results for WScore, a new scoring function and sampling methodology integrating WaterMap and Glide, are presented for four DUD targets, hivrt, hsp90, cdk2, and fxa. WScore performance in early enrichment is consistently strong and all systems examined show AUC > 0.9 and superior early enrichment to DUD best-practices Glide SP results.

What is the current state of the art in protein design? This question was approached in a recent two-week protein design workshop sponsored by EMBO and held at the EMBL in Heidelberg. The goals were to test available design tools and to explore new design strategies. Five novel proteins were designed: Shpilka, a sandwich of two four-stranded beta-sheets, a scaffold on which to explore variations in loop topology; Grendel, a four-helical membrane anchor, ready for fusion to water-soluble functional domains; Finger-clasp, a dimer of interdigitating beta-beta-alpha units, the simplest variant of the "handshake" structural class; Aida, an antibody binding surface intended to be specific for flavodoxin; Leather--a minimal NAD binding domain, extracted from a larger protein. Each design is available as a set of three-dimensional coordinates, the corresponding amino acid sequence and a set of analytical results. The designs are placed in the public domain for scrutiny, improvement, and possible experimental verification.

We present an effective theory for water. Our goal is to formulate an accurate model for the effects of solvation on protein dynamics, without incurring the huge computational cost and the slow temporal evolution typical of molecular dynamics simulations of liquids. We replace the individual water molecules in an all-atom potential with a local dielectric density field, with self-interactions given by the Landau-Ginzburg free energy and external interactions by Lennard-Jones forces at the surface of the protein atoms. We explore conformational space with finite temperature Monte Carlo dynamics, using parallel Langevin and Fourier acceleration algorithms well suited to data-parallel computer architectures such as the Connection Machine. To establish the validity of our approximations, we compare our electrostatic contribution to the solvation energy with the results of Lim, Bashford, and Karplus using a conventional static continuum dielectric cavity model, and the nonelectrostatic contributions with estimates of hydrophobic surface free energy. Our model can also accommodate ionic charges and temperature fluctuations. We propose future investigations extending our effective theory of solvation to include explicit orientational entropy and hydrogen-bonding terms.