Free energy grids: a practical qualitative application of free energy perturbation to ligand design using the OWFEG method

Fragment-Based Ligand Designing

Fragment-based drug design strategies have been used in drug discovery since it was first demonstrated using experimental structural biology techniques such as nuclear magnetic resonance (NMR) and X-ray crystallography. The underlying idea is that existing or new chemical entities with known desirable properties may serve both as tool compounds and as starting points for hit-to-lead expansion. Despite the recent advancements, there remain challenges to overcome, such as assembly of the synthetically feasible structures, development of scoring functions to correlate structure and their activities, and fine tuning of the promising molecules. This chapter first covers the theoretical background needed to understand the concepts and the challenges related to the field of study, followed by the description of important protocols and related software. Case studies are presented to demonstrate practical applications.

Analysis of ligand binding to proteins using molecular dynamics simulations

This work aims to explore theoretically the molecular mechanisms of ligand binding to proteins through the use of molecular dynamics simulations. The binding of sodium dodecyl sulfate (SDS) to cobra cardio toxin A3 (CTX A3) and thiourea (TOU) to lysozyme have been chosen as the two model systems. Data acquisitions were made by Gromacs software. To begin with, the collisions of ligand molecules with every residue of CTX A3 and lysozyme were evaluated. With this information in hand, the average numbers of collisions with each residue was defined and then assessed. Next, a measure of the affinity of a residue, P(i), referred to as conformational factor, toward a ligand molecule was established. Based on the results provided, all site-making residues for CTX A3 and lysozyme were identified. The results are in good agreement with the experimental data. Finally, based on this method, all site-making residues of bovine carbonic anhydrase (BCA) toward the SDS ligand were predicted.

Evaluating the Molecular Mechanics Poisson−Boltzmann Surface Area Free Energy Method Using a Congeneric Series of Ligands to p38 MAP Kinase

The recently described molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method for calculating free energies is applied to a congeneric series of 16 ligands to p38 MAP kinase whose binding constants span approximately 2 orders of magnitude. These compounds have previously been used to test and compare other free energy calculation methods, including thermodynamic integration (TI), OWFEG, ChemScore, PLPScore, and Dock Energy Score. We find that the MM-PBSA performs relatively poorly for this set of ligands, yielding results much inferior to those from TI or OWFEG, inferior to Dock Energy Score, and not appreciably better than ChemScore or PLPScore but at an appreciably larger computational cost than any of these other methods. This suggests that one should be selective in applying the MM-PBSA method and that for systems that are amenable to other free energy approaches, these other approaches may be preferred. We also examine the single simulation approximation for MM-PBSA, whereby the required ligand and protein trajectories are extracted from a single MD simulation rather than two separate MD runs. This assumption, sometimes used to speed the MM-PBSA calculation, is found to yield significantly inferior results with only a moderate net percentage reduction in total simulation time.

Optimization and Validation of a Docking-Scoring Protocol; Application to Virtual Screening for COX-2 Inhibitors

To exploit available structural information about the cyclooxygenase enzyme for the virtual screening of large chemical libraries, a docking-scoring protocol was tuned and validated. The screening accuracy was assessed using a series of known inhibitors and a set of diverse a priori inactive compounds that was seeded with known active ligands. The major parameters of the DOCK algorithm were investigated. A large improvement of the results was obtained on tweaking some of them. The generated complexes were rescored using six scoring functions. In this way, the striking importance of this step was demonstrated, as well as the good performances of DOCK energy and SCORE for this target. The results were further improved via a consensus approach. As a first application, a subset of a large compound library was screened using this protocol. Among the compounds that were selected for biological testing, a third of them turned out to have a significant enzyme inhibition.

Raptor: Combining Dual-Shell Representation, Induced-Fit Simulation, and Hydrophobicity Scoring in Receptor Modeling: Application toward the Simulation of Structurally Diverse Ligand Sets

We present a novel receptor-modeling approach (software Raptor) based on multidimensional quantitative structure-activity relationships (QSARs). To accurately predict relative free energies of ligand binding, it is of utmost importance to simulate induced fit. In Raptor, we explicitly and anisotropically allow for this phenomenon by a dual-shell representation of the receptor surrogate. In our concept, induced fit is not limited to steric aspects but includes the variation of the physicochemical fields along with it. The underlying scoring function for evaluating ligand-receptor interactions includes directional terms for hydrogen bonding and hydrophobicity and thereby treats solvation effects implicitly. This makes the approach independent from a partial-charge model and, as a consequence, allows one to smoothly model ligand molecules binding to the receptor with different net charges. We have applied the new concept toward the estimation of ligand-binding energies associated with the chemokine receptor-3 (50 ligands: r(2) = 0.965; p(2) = 0.932), the bradykinin B(2) receptor (52 ligands: r(2) = 0.949; p(2) = 0.859), and the estrogen receptor (116 ligands: r(2) = 0.908; p(2) = 0.907), respectively.

Pattern recognition and massively distributed computing

A feature of Peter Kollman's research was his exploitation of the latest computational techniques to devise novel applications of the free energy perturbation method. He would certainly have seized upon the opportunities offered by massively distributed computing. Here we describe the use of over a million personal computers to perform virtual screening of 3.5 billion druglike molecules against protein targets by pharmacophore pattern matching, together with other applications of pattern recognition such as docking ligands without any a priori knowledge about the binding site location.

DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein

The development of a new tailor-made scoring function to predict binding affinities of protein-ligand complexes is described. Knowledge-based pair-potentials are specifically adapted to a particular protein by considering additional ligand-based information. The formalism applied to derive the new function is similar to the well-known CoMFA approach, however, the fields used in the approach originate from the protein environment (and not from the aligned ligands as in CoMFA, thus, a "reverse" CoMFA (= AFMoC) named Adaptation of Fields for Molecular Comparison is performed). A regular-spaced grid is placed into the binding site and knowledge-based pair-potentials between protein atoms and ligand atom probes are mapped onto the grid intersections resulting in "potential fields". By multiplying distance-dependent atom-type properties of actual ligands docked into the binding site with the neighboring grid values, "interaction fields" are produced from the original "potential fields". In a PLS analysis, these atom-type specific interaction fields are correlated to the actual binding affinities of the embedded ligands, resulting in individual weighting factors for each field value. As in CoMFA, the results of the analysis can be interpreted in graphical terms by contribution maps, and binding affinities of novel ligands are predicted by applying the derived 3D QSAR equation. The scope of the new method is demonstrated using thermolysin and glycogen phosphorylase b as test examples. Impressive improvements of the predictive power for affinity prediction can be achieved compared to the application of the original knowledge-based potentials by considering a sample set of only 15 known training ligands. Thus, with growing information about the drug target studied, the new method allows one to move gradually from generally valid to protein-specifically adapted pair-potentials, depending on the amount of training information available and its degree of structural diversity. In addition, convincing predictive power is also achieved for ligand poses generated by automatic docking tools.

Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system

Precise thermodynamic integration free energy simulations have been applied to a congeneric series of 16 inhibitors to the p38 MAP kinase protein for which the experimental binding data (IC(50)) is known. The relative free energy of binding for each compound has been determined. For comparison, the same series of compounds have also scored using the best rapid scoring functions used in database screening. From the results of these calculations, we find (1) that precise free energy simulations allow predictions that are reliable and in good agreement with experiment; (2) that predictions of lower reliability, but still in good qualitative agreement with experiment, can be obtained using the OWFEG free energy grid method, at a much lower computational cost; (3) and that other methods, not based on free energy simulations yield results in much poorer agreement with experiment. A new predictive index, which measures the reliability of a prediction method in the context of normal use, is defined and calculated for each scoring method. Predictive indices of 0.84, 0.56, 0.04, -0.05, and 0.25 are calculated for thermodynamic integration, OWFEG, ChemScore, PLPScore, and Dock Energy Score, respectively, where +1.0 is perfect correct prediction, -1.0 is perfect incorrect prediction, and 0.0 is random.

Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions

Computer modeling has been developed and widely applied in studying molecules of biological interest. The force field is the cornerstone of computer simulations, and many force fields have been developed and successfully applied in these simulations. Two interesting areas are (a) studying enzyme catalytic mechanisms using a combination of quantum mechanics and molecular mechanics, and (b) studying macromolecular dynamics and interactions using molecular dynamics (MD) and free energy (FE) calculation methods. Enzyme catalysis involves forming and breaking of covalent bonds and requires the use of quantum mechanics. Noncovalent interactions appear ubiquitously in biology, but here we confine ourselves to review only noncovalent interactions between protein and protein, protein and ligand, and protein and nucleic acids.

Improved scoring of ligand-protein interactions using OWFEG free energy grids

A new approach to rapidly score protein-ligand interactions is tested on several protein-ligand systems. Results using this approach - the OWFEG free energy grid - are quite promising and are generally in better agreement with experiment (in some cases much better) than those obtained employing scoring techniques currently in wide use. The OWFEG free energy grid is generated from a one-window free energy perturbation MD simulation (Pearlman, D. A. J. Med. Chem. 1999, 42, 4313-4324). The OWFEG approach is applied to three protein systems: IMPDH, MAP kinase p38, and HIV-1 aspartyl protease. OWFEG scores are compared to experimental K(i) and IC50 data in each case. Empirical scoring functions applied to the same systems for comparison include ChemScore, Piecewise Linear Potential (PLP), and Dock energy score.