Structure activity relationship by NMR and by computer: a comparative study

A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery

Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful tool to study structures and dynamics of biomolecules under physiological conditions. As there are numerous NMR-derived methods applicable to probe protein–ligand interactions, NMR has been widely utilized in drug discovery, especially in such steps as hit identification and lead optimization. NMR is frequently used to locate ligand-binding sites on a target protein and to determine ligand binding modes. NMR spectroscopy is also a unique tool in fragment-based drug design (FBDD), as it is able to investigate target-ligand interactions with diverse binding affinities. NMR spectroscopy is able to identify fragments that bind weakly to a target, making it valuable for identifying hits targeting undruggable sites. In this review, we summarize the roles of solution NMR spectroscopy in drug discovery. We describe some methods that are used in identifying fragments, understanding the mechanism of action for a ligand, and monitoring the conformational changes of a target induced by ligand binding. A number of studies have proven that ¹⁹F-NMR is very powerful in screening fragments and detecting protein conformational changes. In-cell NMR will also play important roles in drug discovery by elucidating protein-ligand interactions in living cells.

Theoretical improvement of the specific inhibitor of human carbonic anhydrase VII

The selectivity of a known arylsulfonamides inhibitor for two isozymes II and VII of human carbonic anhydrases (hCAs) was studied by homology modeling, molecular docking and molecular dynamics methods. The results show that the selectivity of the inhibitor for two isozymes is due to the different side chain lengths between N67 of hCA II and Q64 of hCA VII. One more methene group in the side chain of Q64 of hCA VII makes it possible to form the hydrogen bond with the bromide atom of the known inhibitor. From the point of view, the modification to the known inhibitor was performed to obtain an inhibitor with higher selectivity. The complex conformations of the new designed inhibitor and two isozymes designate the formation of the hydrogen bond between the newly added group (hydroxypropyl group) and Q64 of hCA VII but N67 of hCA II. The results of the binding free energy from the MM/PBSA approach also prove the selectivity improvement of the new inhibitor in comparison with the known inhibitor. The work will help the design of the isozyme-specific inhibitors of hCA VII.

Force field parameters for the simulation of modified histone tails

We describe the development of force field parameters for methylated lysines and arginines, and acetylated lysine for the CHARMM all-atom force field. We also describe a CHARMM united-atom force field for modified sidechains suitable for use with fragment-based docking methods. The development of these parameters is based on results of ab initio quantum mechanics calculations of model compounds with subsequent refinement and validation by molecular mechanics and molecular dynamics simulations. The united-atom parameters are tested by fragment docking to target proteins using the MCSS procedure. The all-atom force field is validated by molecular dynamics simulations of multiple experimental structures. In both sets of calculations, the computational predictions using the force field were compared to the corresponding experimental structures. We show that the parameters yield an accurate reproduction of experimental structures. Together with the existing CHARMM force field, these parameters will enable the general modeling of post-translational modifications of histone tails.

CHARMM: the biomolecular simulation program

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.

The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design

Fragment-based ligand design approaches, such as the multi-copy simultaneous search (MCSS) methodology, have proven to be useful tools in the search for novel therapeutic compounds that bind pre-specified targets of known structure. MCSS offers a variety of advantages over more traditional high-throughput screening methods, and has been applied successfully to challenging targets. The methodology is quite general and can be used to construct functionality maps for proteins, DNA, and RNA. In this review, we describe the main aspects of the MCSS method and outline the general use of the methodology as a fundamental tool to guide the design of de novo lead compounds. We focus our discussion on the evaluation of MCSS results and the incorporation of protein flexibility into the methodology. In addition, we demonstrate on several specific examples how the information arising from the MCSS functionality maps has been successfully used to predict ligand binding to protein targets and RNA.

Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.

EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization

In recent years, protein-ligand docking has become a powerful tool for drug development. Although several approaches suitable for high throughput screening are available, there is a need for methods able to identify binding modes with high accuracy. This accuracy is essential to reliably compute the binding free energy of the ligand. Such methods are needed when the binding mode of lead compounds is not determined experimentally but is needed for structure-based lead optimization. We present here a new docking software, called EADock, that aims at this goal. It uses an hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 A around the center of mass of the ligand position in the crystal structure, and on the contrary to other benchmarks, our algorithm was fed with optimized ligand positions up to 10 A root mean square deviation (RMSD) from the crystal structure, excluding the latter. This validation illustrates the efficiency of our sampling strategy, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures could be explained by the presence of crystal contacts in the experimental structure. Finally, the ability of EADock to accurately predict binding modes on a real application was illustrated by the successful docking of the RGD cyclic pentapeptide on the alphaVbeta3 integrin, starting far away from the binding pocket.

Protein/ligand binding free energies calculated with quantum mechanics/molecular mechanics

The calculation of binding affinities for flexible ligands has hitherto required the availability of reliable molecular mechanics parameters for the ligands, a restriction that can in principle be lifted by using a mixed quantum mechanics/molecular mechanics (QM/MM) representation in which the ligand is treated quantum mechanically. The feasibility of this approach is evaluated here, combining QM/MM with the Poisson-Boltzmann/surface area model of continuum solvation and testing the method on a set of 47 benzamidine derivatives binding to trypsin. The experimental range of the absolute binding energy (DeltaG = -3.9 to -7.6 kcal/mol) is reproduced well, with a root-mean-square (RMS) error of 1.2 kcal/mol. When QM/MM is applied without reoptimization to the very different ligands of FK506 binding protein the RMS error is only 0.7 kcal/mol. The results show that QM/MM is a promising new avenue for automated docking and scoring of flexible ligands. Suggestions are made for further improvements in accuracy.

Structural prediction of peptides bound to MHC class I

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Structure-based design of novel Chk1 inhibitors: insights into hydrogen bonding and protein-ligand affinity

We report the discovery, synthesis, and crystallographic binding mode of novel furanopyrimidine and pyrrolopyrimidine inhibitors of the Chk1 kinase, an oncology target. These inhibitors are synthetically tractable and inhibit Chk1 by competing for its ATP site. A chronological account allows an objective comparison of modeled compound docking modes to the subsequently obtained crystal structures. The comparison provides insights regarding the interpretation of modeling results, in relationship to the multiple reasonable docking modes which may be obtained in a kinase-ATP site. The crystal structures were used to guide medicinal chemistry efforts. This led to a thorough characterization of a pair of ligand-protein complexes which differ by a single hydrogen bond. An analysis indicates that this hydrogen bond is expected to contribute a fraction of the 10-fold change in binding affinity, adding a valuable observation to the debate about the energetic role of hydrogen bonding in molecular recognition.

Protein-ligand binding free energy estimation using molecular mechanics and continuum electrostatics. Application to HIV-1 protease inhibitors

A method is proposed for the estimation of absolute binding free energy of interaction between proteins and ligands. Conformational sampling of the protein-ligand complex is performed by molecular dynamics (MD) in vacuo and the solvent effect is calculated a posteriori by solving the Poisson or the Poisson-Boltzmann equation for selected frames of the trajectory. The binding free energy is written as a linear combination of the buried surface upon complexation, SASbur, the electrostatic interaction energy between the ligand and the protein, Eelec, and the difference of the solvation free energies of the complex and the isolated ligand and protein, deltaGsolv. The method uses the buried surface upon complexation to account for the non-polar contribution to the binding free energy because it is less sensitive to the details of the structure than the van der Waals interaction energy. The parameters of the method are developed for a training set of 16 HIV-1 protease-inhibitor complexes of known 3D structure. A correlation coefficient of 0.91 was obtained with an unsigned mean error of 0.8 kcal/mol. When applied to a set of 25 HIV-1 protease-inhibitor complexes of unknown 3D structures, the method provides a satisfactory correlation between the calculated binding free energy and the experimental pIC5o without reparametrization.

Pose scoring by NMR

Recently, we have developed a fast approach to calculate NMR chemical shifts using the divide and conquer method at the semiempirical level. To demonstrate the utility of this approach for characterizing protein-ligand interactions, we used the deviation of calculated chemical shift perturbations from experiment to determine the orientation of a ligand (GPI-1046) in the binding pocket of the FK506 binding protein (FKBP12). Moreover, we were able to select the native state of the ligand from a collection of decoy poses. A key hydrogen bond between O1 and HN in Ile56 was also identified. Our results suggest that ligand-induced chemical shift perturbations can be used to refine protein/ligand structures.

Functionality maps of the ATP binding site of DNA gyrase B: generation of a consensus model of ligand binding

The Multiple Copy Simultaneous Search method (MCSS) was used to construct consensus functionality maps for functional group binding in the ATP binding site of DNA gyrase B. To account for the conformational flexibility of the protein active site, which involves small side chain fluctuations as well as large-scale loop motions, the calculations were done for three different conformations of the 24 kDa subdomain of DNA gyrase B. A postprocessing procedure that employs a continuum dielectric model to include solvent effects was used to calculate the binding free energy for every functional group. These results were ranked according to their affinity for DNA gyrase B and clustered using a new procedure based on van der Waals contacts that is better adapted for cases where multiple conformations are being considered. A total of 23 different functional groups were tested. The results gave consensus maps that indicate those functional group binding sites that are insensitive to the specific protein conformation. The maps also demonstrate that functional groups other than those found in the known ligands may bind competitively in the binding sites of known ligands. This suggests numerous scaffolds that can be used in the development of new ligands for the ATP and coumarinic binding sites in DNA gyrase B. Finally, the calculations show the existence of alternative binding sites near the known binding sites that could be targeted in the rational design for new inhibitors.

Investigation of glucose binding sites on insulin

Possible insulin binding sites for D-glucose have been investigated theoretically by docking and molecular dynamics (MD) simulations. Two different docking programs for small molecules were used; Multiple Copy Simultaneous Search (MCSS) and Solvation Energy for Exhaustive Docking (SEED) programs. The configurations resulting from the MCSS search were evaluated with a scoring function developed to estimate the binding free energy. SEED calculations were performed using various values for the dielectric constant of the solute. It is found that scores emphasizing non-polar interactions gave a preferential binding site in agreement with that inferred from recent fluorescence and NMR NOESY experiments. The calculated binding affinity of -1.4 to -3.5 kcal/mol is within the measured range of -2.0 +/- 0.5 kcal/mol. The validity of the binding site is suggested by the dynamical stability of the bound glucose when examined with MD simulations with explicit solvent. Alternative binding sites were found in the simulations and their relative stabilities were estimated. The motions of the bound glucose during molecular dynamics simulations are correlated with the motions of the insulin side chains that are in contact with it and with larger scale insulin motions. These results raise the question of whether glucose binding to insulin could play a role in its activity. The results establish the complementarity of molecular dynamics simulations and normal mode analyses with the search for binding sites proposed with small molecule docking programs.