Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free-Energy Calculations Using Lambda Dynamics.

Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small-molecule lead optimization. Relative free-energy perturbation (FEP) approaches are one of the most widely utilized for this goal but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to set up, execute, and analyze multisite lambda dynamics (MSLD) calculations run on GPUs with CHARMM implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse data set of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free-energy landscape of any MSLD system is developed, which enhances sampling and allows for efficient estimation of free-energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than 100 ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150 ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multisite systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore the chemical space around a lead compound and thus are of utility in lead optimization.

Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands.

Physics-based prediction of protein-ligand binding affinities for a congeneric series of ligands in lead optimization requires their geometries as a first step. In this paper, we report a method that uses the 3D conformation of a lead compound in complex with a protein as a template to generate conformations of a series of related analog compounds. The method uses the Maximal Common Substructure (MCS) computed between lead and analog ligands to assign coordinates for the atoms shared between the ligands. For the differing atoms, a conformation generation procedure is implemented that results in a diversity of conformations. The generated conformations are sorted using a score based on the Molecular Mechanics and Generalized Born with Solvent Accessible Surface Area contribution (MM-GBSA) method. The accuracy of the generated conformations is tested retrospectively using a cross-validation approach applied to four data sets obtained from the Drug Design Data Resource (D3R) by measuring the RMSD of the top scored conformation with respect to the crystallographic pose. The scoring ability of the method is independently assessed using data for the same protein targets to test the rank ordering ability and separating active and inactive ligands. We tested the effect of protein flexibility during structural optimization and scoring approaches with and without strain energies. Retrospective validation on data sets comprising 4 targets shows that the method outperforms random selection for all targets and outperforms a molecular weight-based null model in 3 out of 4 targets in separating active and inactive compounds. Therefore, the presented method is expected to be of utility in lead optimization for rapidly screening analog ligands and generating initial conformations for use in more detailed physics-based binding affinity prediction methods.

Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches.

Accurate and rapid estimation of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estimated for one or two non-hydrogen atom changes in compounds targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include standard FEP, single-step free energy perturbation (SSFEP) and the site-identification by ligand competitive saturation (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with experimental results. This is supported by additional comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000-fold computational savings over FEP for calculating the relative affinities of ligand modifications once those pre-computations are complete. An illustrative example of the potential application of these methods in the context of screening large numbers of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.

Novel protein-inhibitor interactions in site 3 of Ca(2+)-bound S100B as discovered by X-ray crystallography.

Structure-based drug discovery is under way to identify and develop small-molecule S100B inhibitors (SBiXs). Such inhibitors have therapeutic potential for treating malignant melanoma, since high levels of S100B downregulate wild-type p53 tumor suppressor function in this cancer. Computational and X-ray crystallographic studies of two S100B-SBiX complexes are described, and both compounds (apomorphine hydrochloride and ethidium bromide) occupy an area of the S100B hydrophobic cleft which is termed site 3. These data also reveal novel protein-inhibitor interactions which can be used in future drug-design studies to improve SBiX affinity and specificity. Of particular interest, apomorphine hydrochloride showed S100B-dependent killing in melanoma cell assays, although the efficacy exceeds its affinity for S100B and implicates possible off-target contributions. Because there are no structural data available for compounds occupying site 3 alone, these studies contribute towards the structure-based approach to targeting S100B by including interactions with residues in site 3 of S100B.

Small Molecule Inhibitors of Ca(2+)-S100B Reveal Two Protein Conformations.

The drug pentamidine inhibits calcium-dependent complex formation with p53 ((Ca)S100B·p53) in malignant melanoma (MM) and restores p53 tumor suppressor activity in vivo. However, off-target effects associated with this drug were problematic in MM patients. Structure-activity relationship (SAR) studies were therefore completed here with 23 pentamidine analogues, and X-ray structures of (Ca)S100B·inhibitor complexes revealed that the C-terminus of S100B adopts two different conformations, with location of Phe87 and Phe88 being the distinguishing feature and termed the "FF-gate". For symmetric pentamidine analogues ((Ca)S100B·5a, (Ca)S100B·6b) a channel between sites 1 and 2 on S100B was occluded by residue Phe88, but for an asymmetric pentamidine analogue ((Ca)S100B·17), this same channel was open. The (Ca)S100B·17 structure illustrates, for the first time, a pentamidine analog capable of binding the "open" form of the "FF-gate" and provides a means to block all three "hot spots" on (Ca)S100B, which will impact next generation (Ca)S100B·p53 inhibitor design.

Mapping functional group free energy patterns at protein occluded sites: nuclear receptors and G-protein coupled receptors.

Occluded ligand-binding pockets (LBP) such as those found in nuclear receptors (NR) and G-protein coupled receptors (GPCR) represent a significant opportunity and challenge for computer-aided drug design. To determine free energies maps of functional groups of these LBPs, a Grand-Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) strategy is combined with the Site Identification by Ligand Competitive Saturation (SILCS) methodology. SILCS-GCMC/MD is shown to map functional group affinity patterns that recapitulate locations of functional groups across diverse classes of ligands in the LBPs of the androgen (AR) and peroxisome proliferator-activated-γ (PPARγ) NRs and the metabotropic glutamate (mGluR) and ß2-adreneric (ß2AR) GPCRs. Inclusion of protein flexibility identifies regions of the binding pockets not accessible in crystal conformations and allows for better quantitative estimates of relative ligand binding affinities in all the proteins tested. Differences in functional group requirements of the active and inactive states of the ß2AR LBP were used in virtual screening to identify high efficacy agonists targeting ß2AR in Airway Smooth Muscle (ASM) cells. Seven of the 15 selected ligands were found to effect ASM relaxation representing a 46% hit rate. Hence, the method will be of use for the rational design of ligands in the context of chemical biology and the development of therapeutic agents.

Small-molecule inhibitors of ERK-mediated immediate early gene expression and proliferation of melanoma cells expressing mutated BRaf.

Constitutive activation of the extracellular-signal-regulated kinases 1 and 2 (ERK1/2) are central to regulating the proliferation and survival of many cancer cells. The current inhibitors of ERK1/2 target ATP binding or the catalytic site and are therefore limited in their utility for elucidating the complex biological roles of ERK1/2 through its phosphorylation and regulation of over 100 substrate proteins. To overcome this limitation, a combination of computational and experimental methods was used to identify low-molecular-mass inhibitors that are intended to target ERK1/2 substrate-docking domains and selectively interfere with ERK1/2 regulation of substrate proteins. In the present study, we report the identification and characterization of compounds with a thienyl benzenesulfonate scaffold that were designed to inhibit ERK1/2 substrates containing an F-site or DEF (docking site for ERK, FXF) motif. Experimental evidence shows the compounds inhibit the expression of F-site containing immediate early genes (IEGs) of the Fos family, including c-Fos and Fra1, and transcriptional regulation of the activator protein-1 (AP-1) complex. Moreover, this class of compounds selectively induces apoptosis in melanoma cells containing mutated BRaf and constitutively active ERK1/2 signalling, including melanoma cells that are inherently resistant to clinically relevant kinase inhibitors. These findings represent the identification and initial characterization of a novel class of compounds that inhibit ERK1/2 signalling functions and their potential utility for elucidating ERK1/2 and other signalling events that control the growth and survival of cancer cells containing elevated ERK1/2 activity.

Site Identification by Ligand Competitive Saturation (SILCS) simulations for fragment-based drug design.

Fragment-based drug design (FBDD) involves screening low molecular weight molecules ("fragments") that correspond to functional groups found in larger drug-like molecules to determine their binding to target proteins or nucleic acids. Based on the principle of thermodynamic additivity, two fragments that bind nonoverlapping nearby sites on the target can be combined to yield a new molecule whose binding free energy is the sum of those of the fragments. Experimental FBDD approaches, like NMR and X-ray crystallography, have proven very useful but can be expensive in terms of time, materials, and labor. Accordingly, a variety of computational FBDD approaches have been developed that provide different levels of detail and accuracy.The Site Identification by Ligand Competitive Saturation (SILCS) method of computational FBDD uses all-atom explicit-solvent molecular dynamics (MD) simulations to identify fragment binding. The target is "soaked" in an aqueous solution with multiple fragments having different identities. The resulting computational competition assay reveals what small molecule types are most likely to bind which regions of the target. From SILCS simulations, 3D probability maps of fragment binding called "FragMaps" can be produced. Based on the probabilities relative to bulk, SILCS FragMaps can be used to determine "Grid Free Energies (GFEs)," which provide per-atom contributions to fragment binding affinities. For essentially no additional computational overhead relative to the production of the FragMaps, GFEs can be used to compute Ligand Grid Free Energies (LGFEs) for arbitrarily complex molecules, and these LGFEs can be used to rank-order the molecules in accordance with binding affinities.

Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules.

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.

Spatial analysis and quantification of the thermodynamic driving forces in protein-ligand binding: binding site variability.

The thermodynamic driving forces behind small molecule-protein binding are still not well-understood, including the variability of those forces associated with different types of ligands in different binding pockets. To better understand these phenomena we calculate spatially resolved thermodynamic contributions of the different molecular degrees of freedom for the binding of propane and methanol to multiple pockets on the proteins Factor Xa and p38 MAP kinase. Binding thermodynamics are computed using a statistical thermodynamics based end-point method applied on a canonical ensemble comprising the protein-ligand complexes and the corresponding free states in an explicit solvent environment. Energetic and entropic contributions of water and ligand degrees of freedom computed from the configurational ensemble provide an unprecedented level of detail into the mechanisms of binding. Direct protein-ligand interaction energies play a significant role in both nonpolar and polar binding, which is comparable to water reorganization energy. Loss of interactions with water upon binding strongly compensates these contributions leading to relatively small binding enthalpies. For both solutes, the entropy of water reorganization is found to favor binding in agreement with the classical view of the "hydrophobic effect". Depending on the specifics of the binding pocket, both energy-entropy compensation and reinforcement mechanisms are observed. It is notable to have the ability to visualize the spatial distribution of the thermodynamic contributions to binding at atomic resolution showing significant differences in the thermodynamic contributions of water to the binding of propane versus methanol.

Sampling of Organic Solutes in Aqueous and Heterogeneous Environments Using Oscillating Excess Chemical Potentials in Grand Canonical-like Monte Carlo-Molecular Dynamics Simulations.

Solute sampling of explicit bulk-phase aqueous environments in grand canonical (GC) ensemble simulations suffer from poor convergence due to low insertion probabilities of the solutes. To address this, we developed an iterative procedure involving Grand Canonical-like Monte Carlo (GCMC) and molecular dynamics (MD) simulations. Each iteration involves GCMC of both the solutes and water followed by MD, with the excess chemical potential (µex) of both the solute and the water oscillated to attain their target concentrations in the simulation system. By periodically varying the µex of the water and solutes over the GCMC-MD iterations, solute exchange probabilities and the spatial distributions of the solutes improved. The utility of the oscillating-µex GCMC-MD method is indicated by its ability to approximate the hydration free energy (HFE) of the individual solutes in aqueous solution as well as in dilute aqueous mixtures of multiple solutes. For seven organic solutes: benzene, propane, acetaldehyde, methanol, formamide, acetate, and methylammonium, the average µex of the solutes and the water converged close to their respective HFEs in both 1 M standard state and dilute aqueous mixture systems. The oscillating-µex GCMC methodology is also able to drive solute sampling in proteins in aqueous environments as shown using the occluded binding pocket of the T4 lysozyme L99A mutant as a model system. The approach was shown to satisfactorily reproduce the free energy of binding of benzene as well as sample the functional group requirements of the occluded pocket consistent with the crystal structures of known ligands bound to the L99A mutant as well as their relative binding affinities.

Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling.

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.

Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach.

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Rapid estimation of hydration thermodynamics of macromolecular regions.

This work describes a novel protocol to efficiently calculate the local free energy of hydration of specific regions in macromolecules. The method employs Monte Carlo simulations in the grand canonical ensemble to generate water configurations in a selected spherical region in the macromolecule. Excess energy and entropy of hydration are calculated by analyzing the water configurational distributions following the recently published grid inhomogeneous solvation theory method [C. N. Nguyen, T. K. Young, and M. K. Gilson, J. Chem. Phys. 137, 044101 (2012)]. Our method involves the approximations of treating the macromolecule and distant solvent as rigid and performing calculations on multiple such conformations to account for conformational diversity. These approximations are tested against water configurations obtained from a molecular dynamics simulation. The method is validated by predicting the number and location of water molecules in 5 pockets in the protein Interleukin-1ß for which experimental water occupancy data are available. Free energy values are validated against decoupling free energy perturbation calculations. The results indicate that the approximations used in the method enable efficient prediction of free energies of water displacement.

Structure-Based Discovery of a Novel Pentamidine-Related Inhibitor of the Calcium-Binding Protein S100B.

Molecular Dynamics simulations of the pentamidine-S100B complex, where two molecules of pentamidine bind per monomer of S100B, were performed in an effort to determine what properties would be desirable in a pentamidine-derived compound as an inhibitor for S100B. These simulations predicted that increasing the linker length of the compound would allow a single molecule to span both pentamidine binding sites on the protein. The resulting compound, SBi4211 (also known as heptamidine), was synthesized and experiments to study its inhibition of S100B were performed. The 1.65 Å X-ray crystal structure was determined for Ca(2+)-S100B-heptamdine and gives high-resolution information about key contacts that facilitate the interaction between heptamidine and S100B. Additionally, NMR HSQC experiments with both compounds show that heptamidine interacts with the same region of S100B as pentamidine. Heptamidine is able to selectively kill melanoma cells with S100B over those without S100B, indicating that its binding to S100B has an inhibitory effect and that this compound may be useful in designing higher-affinity S100B inhibitors as a treatment for melanoma and other S100B-related cancers.

Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges.

Molecular mechanics force fields are widely used in computer-aided drug design for the study of drug candidates interacting with biological systems. In these simulations, the biological part is typically represented by a specialized biomolecular force field, while the drug is represented by a matching general (organic) force field. In order to apply these general force fields to an arbitrary drug-like molecule, functionality for assignment of atom types, parameters, and partial atomic charges is required. In the present article, algorithms for the assignment of parameters and charges for the CHARMM General Force Field (CGenFF) are presented. These algorithms rely on the existing parameters and charges that were determined as part of the parametrization of the force field. Bonded parameters are assigned based on the similarity between the atom types that define said parameters, while charges are determined using an extended bond-charge increment scheme. Charge increments were optimized to reproduce the charges on model compounds that were part of the parametrization of the force field. A "penalty score" is returned for every bonded parameter and charge, allowing the user to quickly and conveniently assess the quality of the force field representation of different parts of the compound of interest. Case studies are presented to clarify the functioning of the algorithms and the significance of their output data.

Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations.

The in-silico Site Identification by Ligand Competitive Saturation (SILCS) approach identifies the binding sites of representative chemical entities on the entire protein surface, information that can be applied for computational fragment-based drug design. In this study, we report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single step free energy perturbation (SSFEP) calculations to identify site-specific favorable chemical modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the experimental trends in relative hydration free energies of benzene analogues and for two datasets of experimental relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins is first analyzed to identify favorable benzene binding sites following which an ensemble of benzene-protein conformations for that site is obtained. The SSFEP protocol applied to that ensemble results in good reproduction of experimental free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and analysis of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.

CHARMM additive all-atom force field for carbohydrate derivatives and its utility in polysaccharide and carbohydrate-protein modeling.

Monosaccharide derivatives such as xylose, fucose, N-acetylglucosamine (GlcNAc), N-acetylgalactosamine (GlaNAc), glucuronic acid, iduronic acid, and N-acetylneuraminic acid (Neu5Ac) are important components of eukaryotic glycans. The present work details development of force-field parameters for these monosaccharides and their covalent connections to proteins via O-linkages to serine or threonine sidechains and via N-linkages to asparagine sidechains. The force field development protocol was designed to explicitly yield parameters that are compatible with the existing CHARMM additive force field for proteins, nucleic acids, lipids, carbohydrates, and small molecules. Therefore, when combined with previously developed parameters for pyranose and furanose monosaccharides, for glycosidic linkages between monosaccharides, and for proteins, the present set of parameters enables the molecular simulation of a wide variety of biologically-important molecules such as complex carbohydrates and glycoproteins. Parametrization included fitting to quantum mechanical (QM) geometries and conformational energies of model compounds, as well as to QM pair interaction energies and distances of model compounds with water. Parameters were validated in the context of crystals of relevant monosaccharides, as well NMR and/or x-ray crystallographic data on larger systems including oligomeric hyaluronan, sialyl Lewis X, O- and N-linked glycopeptides, and a lectin:sucrose complex. As the validated parameters are an extension of the CHARMM all-atom additive biomolecular force field, they further broaden the types of heterogeneous systems accessible with a consistently-developed force-field model.

Reproducing crystal binding modes of ligand functional groups using Site-Identification by Ligand Competitive Saturation (SILCS) simulations.

The applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS), to identify regions on a protein surface with which different types of functional groups on low-molecular weight inhibitors interact is demonstrated. The method involves molecular dynamics (MD) simulations of a protein in an aqueous solution of chemically diverse small molecules from which probability distributions of fragments types, termed FragMaps, are obtained. In the present application, SILCS simulations are performed with an aqueous solution of 1 M benzene and propane to map the affinity pattern of the protein for aromatic and aliphatic functional groups. In addition, water hydrogen and oxygen atoms serve as probes for hydrogen-bond donor and acceptor affinity, respectively. The method is tested using a set of 7 proteins for which crystal structures of complexes with several high affinity inhibitors are known. Good agreement is obtained between FragMaps and the positions of chemically similar functional groups in inhibitors as observed in the X-ray crystallographic structures. Quantitative capabilities of the SILCS approach are demonstrated by converting FragMaps to free energies, termed Grid Free Energies (GFE), and showing correlation between the GFE values and experimental binding affinities. For proteins for which ligand decoy sets are available, GFE values are shown to typically score the crystal conformation and conformations similar to it more favorable than decoys. Additionally, SILCS is tested for its ability to capture the subtle differences in ligand affinity across homologous proteins, information which may be of utility toward specificity-guided drug design. Taken together, our results show that SILCS can recapitulate the known location of functional groups of bound inhibitors for a number of proteins, suggesting that the method may be of utility for rational drug design.

Molecular simulations of dodecyl-ß-maltoside micelles in water: influence of the headgroup conformation and force field parameters.

This paper deals with the development and validation of new potential parameter sets, based on the CHARMM36 and GLYCAM06 force fields, to simulate micelles of the two anomeric forms (α and ß) of N-dodecyl-ß-maltoside (C(12)G(2)), a surfactant widely used in the extraction and purification of membrane proteins. In this context, properties such as size, shape, internal structure, and hydration of the C(12)G(2) anomer micelles were thoroughly investigated by molecular dynamics simulations and the results compared with experiments. Additional simulations were also performed with the older CHARMM22 force field for carbohydrates (Kuttel, M.; et al. J. Comput. Chem. 2002, 23, 1236-1243). We find that our CHARMM and GLYCAM parameter sets yield similar results in the case of properties related to the micelle structure but differ for other properties such as the headgroup conformation or the micelle hydration. In agreement with experiments, our results show that for all model potentials the ß-C(12)G(2) micelles have a more pronounced ellipsoidal shape than those containing α anomers. The computed radius of gyration is 20.2 and 25.4 Å for the α- and ß-anomer micelles, respectively. Finally, we show that depending on the potential the water translational diffusion of the interfacial water is 7-11.5 times slower than that of bulk water due to the entrapment of the water in the micelle crevices. This retardation is independent of the headgroup in α- or ß-anomers.