BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation.

Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.

A fragment-based approach to the SAMPL3 Challenge.

The success of molecular fragment-based design depends critically on the ability to make predictions of binding poses and of affinity ranking for compounds assembled by linking fragments. The SAMPL3 Challenge provides a unique opportunity to evaluate the performance of a state-of-the-art fragment-based design methodology with respect to these requirements. In this article, we present results derived from linking fragments to predict affinity and pose in the SAMPL3 Challenge. The goal is to demonstrate how incorporating different aspects of modeling protein-ligand interactions impact the accuracy of the predictions, including protein dielectric models, charged versus neutral ligands, ΔΔGs solvation energies, and induced conformational stress. The core method is based on annealing of chemical potential in a Grand Canonical Monte Carlo (GC/MC) simulation. By imposing an initially very high chemical potential and then automatically running a sequence of simulations at successively decreasing chemical potentials, the GC/MC simulation efficiently discovers statistical distributions of bound fragment locations and orientations not found reliably without the annealing. This method accounts for configurational entropy, the role of bound water molecules, and results in a prediction of all the locations on the protein that have any affinity for the fragment. Disregarding any of these factors in affinity-rank prediction leads to significantly worse correlation with experimentally-determined free energies of binding. We relate three important conclusions from this challenge as applied to GC/MC: (1) modeling neutral ligands--regardless of the charged state in the active site--produced better affinity ranking than using charged ligands, although, in both cases, the poses were almost exactly overlaid; (2) simulating explicit water molecules in the GC/MC gave better affinity and pose predictions; and (3) applying a ΔΔGs solvation correction further improved the ranking of the neutral ligands. Using the GC/MC method under a variety of parameters in the blinded SAMPL3 Challenge provided important insights to the relevant parameters and boundaries in predicting binding affinities using simulated annealing of chemical potential calculations.

Fast estimation of solvation free energies for diverse chemical species.

The free energy of solvation can play an important or even dominant role in the accurate prediction of binding affinities and various other molecular-scale interaction phenomena critical to the study of biochemical processes. Many research applications for solvation modeling, such as fragment-based drug design, require algorithms that are both accurate and computationally inexpensive. We have developed a calculation of solvation free energy which runs fast enough for interactive applications, functions for a wide range of chemical species relevant to simulating molecules for biological and pharmaceutical applications, and is readily extended when data for new species becomes available. We have also demonstrated that the incorporation of ab initio data provides necessary access to sufficient reference data for a broad range of chemical features. Our empirical model, including an electrostatic term and a different set of atom types, demonstrates improvements over a previous, solvent-accessible surface area-only model by Wang et al. when fit to identical training sets (mean absolute error of 0.513 kcal/mol versus the 0.538 kcal/mol reported by Wang). The incorporation of ab initio solvation free energies provides a significant increase in the breadth of chemical features for which the model can be applied by introducing classes of compounds for which little or no experimental data is available. The increased breadth and the speed of this solvation model allow for conformational minimization, conformational search, and ligand binding free energy calculations that economically account for the complex interplay of bonded, nonbonded, and solvation free energies as conformations with varying solvent-accessible surfaces are sampled.

N-Acetyltransferase and sulfotransferase activity in human prostate: potential for carcinogen activation.

BACKGROUND: N-Acetyltransferases (NATs) and sulfotransferases (SULTs) are key phase II metabolizing enzymes that can be involved both in the detoxification and in the activation of many human promutagens and procarcinogens. METHODS AND RESULTS: We investigated the expression of NATs and SULTs in human prostate and tested their role in the activation the N-hydroxy (N-OH) metabolite of 2-amino-3-methylimidazo[4,5-f]quinoline (IQ), a dietary carcinogen, to form DNA adducts. Western blotting showed detectable levels of NAT1, SULT1A1 and SULT1A3 with marked inter-individual variation. NAT2 and other SULT enzymes were not detectable. NAT1 was localized by immunohistochemistry to the cytoplasm of epithelial cells. The presence of acetyl Co-enzyme A (acetyl CoA) and 3'-phosphoadenosine-5'-phosphosulfate (PAPS), NAT and SULT cofactors, respectively, significantly increased the level of DNA adducts, detected by P-postlabelling analysis, in calf thymus DNA incubated with N-OH-IQ and prostate cytosolic fractions. The enhancement in the level of DNA adducts in the presence of PAPS correlated with the level of SULT1A1 protein. A single prostate cytosol with the SULT1A1*2/*2 genotype produced less DNA adducts than cytosols with the *1/*2 and *1/*1 genotypes. No significant correlation was observed between NAT1 protein level and the formation of DNA adducts, even in the presence of acetyl CoA. CONCLUSIONS: In conclusion, we demonstrated that NAT1, SULT1A1 and SULT1A3 are present in human prostate and that both enzyme classes significantly contribute to the activation of N-hydroxylated heterocyclic amines to DNA-damaging species in this tissue. Variation in expression levels, in combination with dietary and/or environmental exposure to carcinogens, could be influential in determining individual susceptibility to prostate cancer.