Potent Antimalarials with Development Potential Identified by Structure-Guided Computational Optimization of a Pyrrole-Based Dihydroorotate Dehydrogenase Inhibitor Series.

Dihydroorotate dehydrogenase (DHODH) has been clinically validated as a target for the development of new antimalarials. Experience with clinical candidate triazolopyrimidine DSM265 (1) suggested that DHODH inhibitors have great potential for use in prophylaxis, which represents an unmet need in the malaria drug discovery portfolio for endemic countries, particularly in areas of high transmission in Africa. We describe a structure-based computationally driven lead optimization program of a pyrrole-based series of DHODH inhibitors, leading to the discovery of two candidates for potential advancement to preclinical development. These compounds have improved physicochemical properties over prior series frontrunners and they show no time-dependent CYP inhibition, characteristic of earlier compounds. Frontrunners have potent antimalarial activity in vitro against blood and liver schizont stages and show good efficacy in Plasmodium falciparum SCID mouse models. They are equally active against P. falciparum and Plasmodium vivax field isolates and are selective for Plasmodium DHODHs versus mammalian enzymes.

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.

The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

Accurate Binding Free Energy Predictions in Fragment Optimization.

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Probing the origin of structural stability of single and double stapled p53 peptide analogs bound to MDM2.

The stabilization of secondary structure is believed to play an important role in the peptide-protein binding interaction. In this study, the α-helical conformation and structural stability of single and double stapled all-hydrocarbon cross-linked p53 peptides when bound and unbound to MDM2 are investigated. We determined the effects of the peptide sequence, the stereochemistry of the cross-linker, the conformation of the double bond in the alkene bridge, and the length of the bridge, to the relative stability of the α-helix structure. The binding affinity calculations by WaterMap provided over one hundred hydration sites in the MDM2 binding pocket where water density is greater than twice that of the bulk, and the relative value of free energy released by displacing these hydration sites. In agreement with the experimental data, potentials of mean force obtained by weighted histogram analysis methods indicated the order of peptides from lowest to highest binding affinity. Our study provides a comprehensive rationalization of the relationship between peptide stapling strategy, the secondary structural stability, and the binding affinity of p53/MDM2 complex. We hope our efforts can help to further the development of a new generation p53/MDM2 inhibitors that can reactivate the function of p53 as tumor suppressor gene.

Predicting GPCR promiscuity using binding site features.

G protein-coupled receptors (GPCRs) represent a large family of signaling proteins that includes many therapeutic targets. GPCR ligands include odorants, tastants, and neurotransmitters and vary in size and properties. Dramatic chemical diversity may occur even among ligands of the same receptor. Our goal is to unravel the structural and chemical features that determine GPCRs' promiscuity toward their ligands. We perform statistical analysis using more than 30 descriptors related to the sequence, physicochemical, structural, and energetic properties of the GPCR binding sites-we find that the chemical variability of antagonists significantly correlates with the binding site hydrophobicity and anticorrelates with the number of hydrogen bond donors in the binding site. The number of disulfide bridges in the extracellular region of a receptor anticorrelates with the range of molecular weights of its antagonists, highlighting the role of the entrance pathway in determining the size selectivity for GPCR antagonists. The predictive capability of the model is successfully validated using a separate set of GPCRs, using either X-ray structures or homology models.

Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods.

Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a "best" single receptor identified from known compounds within the congeneric series.

Water networks contribute to enthalpy/entropy compensation in protein-ligand binding.

The mechanism (or mechanisms) of enthalpy-entropy (H/S) compensation in protein-ligand binding remains controversial, and there are still no predictive models (theoretical or experimental) in which hypotheses of ligand binding can be readily tested. Here we describe a particularly well-defined system of protein and ligands--human carbonic anhydrase (HCA) and a series of benzothiazole sulfonamide ligands with different patterns of fluorination--that we use to define enthalpy/entropy (H/S) compensation in this system thermodynamically and structurally. The binding affinities of these ligands (with the exception of one ligand, in which the deviation is understood) to HCA are, despite differences in fluorination pattern, indistinguishable; they nonetheless reflect significant and compensating changes in enthalpy and entropy of binding. Analysis reveals that differences in the structure and thermodynamic properties of the waters surrounding the bound ligands are an important contributor to the observed H/S compensation. These results support the hypothesis that the molecules of water filling the active site of a protein, and surrounding the ligand, are as important as the contact interactions between the protein and the ligand for biomolecular recognition, and in determining the thermodynamics of binding.

A computational approach to design and evaluate enzymatic reaction pathways: application to 1-butanol production from pyruvate.

We present a new computational strategy for the design and evaluation of novel enzymatic pathways for the biosynthesis of fuels and chemicals. The approach combines the use of the Biochemical Network Integrated Computational Explorer (BNICE) framework and a structure-based screening method for rapid generation and evaluation of novel enzymatic reactions and pathways. The strategy is applied to a case study of 1-butanol production from pyruvate, which yielded nine novel biosynthetic pathways. Using screening criteria based on pathway length, thermodynamic feasibility, and metabolic flux analysis, all nine novel pathways were deemed to be attractive candidates. To further assess their feasibility of implementation, we introduced a new screening criterion based on structural complementarity using molecular docking methods. We show that this approach correctly reproduces the native binding poses for a wide range of enzymes in key classes related to 1-butanol production and provides qualitative agreement with experimental measures of catalytic activity for different substrates. In addition, we show that the structure-based methods can be used to select specific proteins that may be promising candidates to catalyze novel reactions.

Understanding the stereospecific interactions of 3-deoxyphosphatidylinositol derivatives with the PTEN phosphatase domain.

PTEN is an important control element of PI3K/AKT signaling involved in controlling the processes of embryonic development, cell migration and apoptosis. While its dysfunction is implicated in a large fraction of cancers, PTEN activity in the same pathway may also contribute to metabolic syndromes such as diabetes. In those cases, selective inhibitors of PTEN may be useful. A new class of chiral PTEN inhibitors based on the 3-deoxy-phosphatidylinositol derivatives was recently identified (Wang et al. [17]). However, lack of detailed understanding of protein-ligand interactions has hampered efforts to develop effective agonists or antagonists of PTEN. Here, we use computational modeling to characterize the interactions of the diverse 3-deoxyphosphatidylinositol inhibitors with the PTEN protein. We show that, while each of the compounds binds with the inositol headgroup inserting into the proposed active site of the PTEN phosphatase domain, hydrogen bonding restrictions lead to distinct binding geometries for ligand pairs of opposite chirality. We furthermore demonstrate that the binding modes differ primarily in the orientation of acyl tails of the ligands and that the activity of the compounds is primarily controlled by the effectiveness of tail-protein contacts. These findings are confirmed by binding affinity calculations which are in good agreement with experiment. Finally, we show that while more potent d-series ligands bind in a manner similar to that of the native substrate, an alternate hydrophobic pocket suitable for binding the opposite chirality l-series inhibitors exists, offering the possibility of designing highly selective PTEN-targeting compounds.

Probing the alpha-helical structural stability of stapled p53 peptides: molecular dynamics simulations and analysis.

Reactivation of the p53 cell apoptosis pathway through inhibition of the p53-hDM2 interaction is a viable approach to suppress tumor growth in many human cancers and stabilization of the helical structure of synthetic p53 analogs via a hydrocarbon cross-link (staple) has been found to lead to increased potency and inhibition of protein-protein binding (J. Am. Chem. Soc. 129: 5298). However, details of the structure and dynamic stability of the stapled peptides are not well understood. Here, we use extensive all-atom molecular dynamics simulations to study a series of stapled alpha-helical peptides over a range of temperatures in solution. The peptides are found to exhibit substantial variations in predicted alpha-helical propensities that are in good agreement with the experimental observations. In addition, we find significant variation in local structural flexibility of the peptides with the position of the linker, which appears to be more closely related to the observed differences in activity than the absolute alpha-helical stability. These simulations provide new insights into the design of alpha-helical stapled peptides and the development of potent inhibitors of alpha-helical protein-protein interfaces.

Role of protein flexibility in the design of Bcl-X(L) targeting agents: insight from molecular dynamics.

Detailed understanding of protein-ligand interactions is crucial to the design of more effective drugs. This is particularly true when targets are protein interfaces which have flexible, shallow binding sites that exhibit substantial structural rearrangement upon ligand binding. In this study, we use molecular dynamics simulations and free energy calculations to explore the role of ligand-induced conformational changes in modulating the activity of three generations of Bcl-X(L) inhibitors. We show that the improvement in the binding affinity of each successive ligand design is directly related to a unique and measurable reduction in local flexibility of specific regions of the binding groove, accompanied by the corresponding changes in the secondary structure of the protein. Dynamic analysis of ligand-protein interactions reveals that the latter evolve with each new design consistent with the observed increase in protein stability, and correlate well with the measured binding affinities. Moreover, our free energy calculations predict binding affinities which are in qualitative agreement with experiment, and indicate that hydrogen bonding to Asn100 could play a prominent role in stabilizing the bound conformations of latter generation ligands, which has not been recognized previously. Overall our results suggest that molecular dynamics simulations provide important information on the dynamics of ligand-protein interactions that can be useful in guiding the design of small-molecule inhibitors of protein interfaces.

Mechanism of thermal decomposition of carbamoyl phosphate and its stabilization by aspartate and ornithine transcarbamoylases.

Carbamoyl phosphate (CP) has a half-life for thermal decomposition of <2 s at 100 degrees C, yet this critical metabolic intermediate is found even in organisms that grow at 95-100 degrees C. We show here that the binding of CP to the enzymes aspartate and ornithine transcarbamoylase reduces the rate of thermal decomposition of CP by a factor of >5,000. Both of these transcarbamoylases use an ordered-binding mechanism in which CP binds first, allowing the formation of an enzyme.CP complex. To understand how the enzyme.CP complex is able to stabilize CP we investigated the mechanism of the thermal decomposition of CP in aqueous solution in the absence and presence of enzyme. By quantum mechanics/molecular mechanics calculations we show that the critical step in the thermal decomposition of CP in aqueous solution, in the absence of enzyme, involves the breaking of the C O bond facilitated by intramolecular proton transfer from the amine to the phosphate. Furthermore, we demonstrate that the binding of CP to the active sites of these enzymes significantly inhibits this process by restricting the accessible conformations of the bound ligand to those disfavoring the reactive geometry. These results not only provide insight into the reaction pathways for the thermal decomposition of free CP in an aqueous solution but also show why these reaction pathways are not accessible when the metabolite is bound to the active sites of these transcarbamoylases.

Simulation study of noncovalent hybridization of carbon nanotubes by single-stranded DNA in water.

Recent discovery that single-stranded DNA (ssDNA) binds to carbon nanotubes with high affinity to form soluble hybrids has received great attention as a promising approach to solving the long-standing problem of nanotube solubilization and separation. The mechanism of this process, including the nature of the DNA-nanotube interactions and the molecular structure of the hybrids is still not well understood. Here, we use all-atom replica-exchange molecular dynamics simulations to study the association of several ssDNA decamers with single-walled carbon nanotubes of different chirality in an aqueous environment. The oligonucleotides are found to readily adsorb onto the nanotube surface, after which they undergo a slow structural rearrangement. Cluster analysis of bound DNA conformations as well as population distribution maps computed as a function of several local and global order parameters show that the hybrids exhibit a complex morphology with DNA strands assuming a number of distinct backbone geometries, which depend on both DNA sequence and nanotube diameter. In contrast, the nucleotide bases are found to align parallel to the nanotube surface with a high degree of orientational order. While the binding appears to be primarily driven by energetically favorable pi-stacking of DNA bases onto the nanotube surface, equilibrium distribution of hybrid conformations is modulated by a complex interplay of forces, including the DNA conformational strain and solvent interactions. As a result, the hybrid free-energy landscapes are found to be rugged, with multiple low-lying minima separated by high barriers, several of which are significantly populated at room temperature. Qualitative differences are observed in free energy profiles of purine- and pyrimidine-based oligonucleotide sequences and are attributed to the difference in self-stacking propensity of the bases.

Solvation of transmembrane proteins by isotropic membrane mimetics: a molecular dynamics study.

Mixtures of organic solvents are often used as membrane mimetics in structure determination of transmembrane proteins by solution NMR; however, the mechanism through which these isotropic solvents mimic the anisotropic environment of cell membranes is not known. Here, we use molecular dynamics simulations to study the solvation thermodynamics of the c-subunit of Escherichia coli F1F0 ATP synthase in membrane mimetic mixtures of methanol, chloroform, and water with varying fractions of components as well as in lipid bilayers. We show that the protein induces a local phase separation of the solvent components into hydrophobic and hydrophilic layers, which provides the anisotropic solvation environment to stabilize the amphiphilic peptide. The extent of this effect varies with solvent composition and is most pronounced in the ternary methanol-chloroform-water mixtures. Analysis of the solvent structure, including the local mole fraction, density profiles, and pair distribution functions, reveals considerable variation among solvent mixtures in the solvation environment surrounding the hydrophobic transmembrane region of the protein. Hydrogen bond analysis indicates that this is primarily driven by the hydrogen-bonding propensity of the essential Asp(61) residue. The impact of the latter on the conformational stability of the solvated protein is discussed. Comparison with the simulations in explicit all-atom models of lipid bilayer indicates a higher flexibility and reduced structural integrity of the membrane mimetic solvated c-subunit. This was particularly true for the deprotonated form of the protein and found to be linked to solvent stabilization of the charged Asp(61).

Elastic bag model for molecular dynamics simulations of solvated systems: application to liquid water and solvated peptides.

The fluctuating elastic boundary (FEB) model for molecular dynamics has recently been developed and validated through simulations of liquid argon. In the FEB model, a flexible boundary which consists of particles connected by springs is used to confine the solvated system, thereby eliminating the need for periodic boundary conditions. In this study, we extend this model to the simulation of bulk water and solvated alanine dipeptide. Both the confining potential and boundary particle interaction functions are modified to preserve the structural integrity of the boundary and prevent the leakage of the solute-solvent system through the boundary. A broad spectrum of structural and dynamic properties of liquid water are computed and compared with those obtained from conventional periodic boundary condition simulations. The applicability of the model to biomolecular simulations is investigated through the analysis of conformational population distribution of solvated alanine dipeptide. In most cases we find remarkable agreement between the two simulation approaches.

Transport properties of normal liquid helium: comparison of various methodologies.

We revisit the problem of self-diffusion in normal liquid helium above the lambda transition. Several different methods are applied to compute the velocity autocorrelation function. Since it is still impossible to determine the exact result for the velocity autocorrelation function from simulation, we appeal to the computation of short-time moments to determine the accuracy of the different approaches at short times. The main conclusion reached from our study is that both the quantum mode-coupling theory and the numerical analytic continuation approach must be regarded as a viable and competitive methods for the computation of dynamical properties of quantum systems.

Elastic bag model for molecular dynamics simulations of solvated systems: application to liquid argon.

A new approach is developed to study the dynamics of the localized process in solutions and other condensed phase systems. The approach employs a fluctuating elastic boundary (FEB) model which encloses the simulated system in an elastic bag that mimics the effects of the bulk solvent. This alleviates the need for periodic boundary conditions and allows for a reduction in the number of solvent molecules that need to be included in the simulation. The boundary bag is modeled as a mesh of quasi-particles connected by elastic bonds. The FEB model allows for volume and density fluctuations characteristic of the bulk system, and the shape of the boundary fluctuates during the course of the simulation to adapt to the configuration fluctuations of the explicit solute-solvent system inside. The method is applied to the simulation of a Lennard-Jones model of liquid argon. Various structural and dynamical quantities are computed and compared with those obtained from conventional periodic boundary simulations. The agreement between the two is excellent in most cases, thus validating the viability of the FEB method.

The calculation of transport properties in quantum liquids using the maximum entropy numerical analytic continuation method: application to liquid para-hydrogen.

We present a method based on augmenting an exact relation between a frequency-dependent diffusion constant and the imaginary time velocity autocorrelation function, combined with the maximum entropy numerical analytic continuation approach to study transport properties in quantum liquids. The method is applied to the case of liquid para-hydrogen at two thermodynamic state points: a liquid near the triple point and a high-temperature liquid. Good agreement for the self-diffusion constant and for the real-time velocity autocorrelation function is obtained in comparison to experimental measurements and other theoretical predictions. Improvement of the methodology and future applications are discussed.