Improving the Prediction of Absolute Solvation Free Energies Using the Next Generation OPLS Force Field

New Octadecanoid Enantiomers from the Whole Plants of Plantago depressa

In this study, 19 octadecanoid derivatives-four pairs of enantiomers (1⁻8), two racemic/scalemic mixtures (9⁻10), and nine biosynthetically related analogues-were obtained from the ethanolic extract of a Chinese medicinal plant, Plantago depressa Willd. Their structures were elucidated on the basis of detailed spectroscopic analyses, with the absolute configurations of the new compounds assigned by time-dependent density functional theory (TD-DFT)-based electronic circular dichroism (ECD) calculations. Six of them (1, 3⁻6, and 9) were reported for the first time, while 2, 7, and 8 have been previously described as derivatives and are currently obtained as natural products. Our bioassays have established that selective compounds show in vitro anti-inflammatory activity by inhibiting lipopolysaccharide-induced nitric oxide (NO) production in mouse macrophage RAW 264.7 cells.

Optimized Lennard-Jones Parameters for Druglike Small Molecules

Meaningful efforts in computer-aided drug design (CADD) require accurate molecular mechanical force fields to quantitatively characterize protein-ligand interactions, ligand hydration free energies, and other ligand physical properties. Atomic models of new compounds are commonly generated by analogy from the predefined tabulated parameters of a given force field. Two widely used approaches following this strategy are the General Amber Force Field (GAFF) and the CHARMM General Force Field (CGenFF). An important limitation of using pretabulated parameter values is that they may be inadequate in the context of a specific molecule. To resolve this issue, we previously introduced the General Automated Atomic Model Parameterization (GAAMP) for automatically generating the parameters of atomic models of small molecules, using the results from ab initio quantum mechanical (QM) calculations as target data. The GAAMP protocol uses QM data to optimize the bond, valence angle, and dihedral angle internal parameters, and atomic partial charges. However, since the treatment of van der Waals interactions based on QM is challenging and may often be unreliable, the Lennard-Jones 6-12 parameters are kept unchanged from the initial atom types assignments (GAFF or CGenFF), which limits the accuracy that can be achieved by these models. To address this issue, a new set of Lennard-Jones 6-12 parameters was systematically optimized to reproduce experimental neat liquid densities and enthalpies of vaporization for a large set of 430 compounds, covering a wide range of chemical functionalities. Calculations of the hydration free energy indicate that optimal accuracy for these models is achieved when the molecule-water van der Waals dispersion is rescaled by a factor of 1.115. The final optimized model yields an average unsigned error of 0.79 kcal/mol in the hydration free energies.

Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water

Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent on three-dimensional structures or even conformer ensembles, the majority of models are based on descriptors derived from two-dimensional structures. Here we present results from a thorough benchmark study of force field, semiempirical, and density functional methods for the calculation of conformer energies in the gas phase and water solvation as a foundation for the correct identification of relevant low-energy conformers. We find that the tight-binding ansatz GFN-xTB shows the lowest error metrics and highest correlation to the benchmark PBE0-D3(BJ)/def2-TZVP in the gas phase for the computationally fast methods and that in solvent OPLS3 becomes comparable in performance. MMFF94, AM1, and DFTB+ perform worse, whereas the performance-optimized but far more expensive functional PBEh-3c yields energies almost perfectly correlated to the benchmark and should be used whenever affordable. On the basis of our findings, we have implemented a reliable and fast protocol for the identification of low-energy conformers of drug-like molecules in water that can be used for the quantification of strain energy and entropy contributions to target binding as well as for the derivation of conformer-ensemble-dependent molecular descriptors.

Unveiling the Potential of Unfused Bichromophoric Naphthalimide To Induce Cytotoxicity by Binding to Tubulin: Breaks Monotony of Naphthalimides as Conventional Intercalators

In the development of small-molecule drug candidates, naphthalimide-based compounds hold a very important position as potent anticancer agents with considerable safety in drug discoveries. Being synthetically and readily accessible, naphthalimide compounds with planar architecture have been developed mostly as DNA-targeting intercalators. However, in this article, it is demonstrated, for the first time, that an unfused naphthalimide-benzothiazole bichromophoric compound 2-(6-chlorobenzo[ d] thiazol-2-yl)-1 H-benzo[ de] isoquinoline-1,3(2 H)-dione (CBIQD), seems to expand the bioactivity of naphthalimide as anti-mitotic agent also. Preliminary studies demonstrate that CBIQD interferes with human lung cancer (A549) cell proliferation and growth and causes cellular morphological changes. However, the underlying mechanism of its antitumor action and primary cellular target in A549 cells remained skeptical. Confocal microscopy in A549 cells revealed disruption of interphase microtubule (MT) network and formation of aberrant multipolar spindle. Consistent with microscopy results, UV-vis, steady-state fluorescence, and time-resolved fluorescence (TRF) studies demonstrate that CBIQD efficiently binds to tubulin ( K_b = 2.03 × 10⁵ M^-1 ± 1.88%), inhibits its polymerization, and depolymerizes preformed microtubules (MTs). Low doses of CBIQD have also shown specificity toward tubulin protein in the presence of a nonspecific protein like bovine serum albumin as well as other cytoskeleton component, actin. The in vitro determination of binding site coupled with in silico studies suggests that CBIQD may prefer to occupy the colchicine binding site. Further, CBIQD perturbed tubulin conformation to some extent and protected ∼1.4 cysteine residues toward chemical modification by 5,5'-dithiobis-2-nitrobenzoic acid. We also suggest the possible mechanism underlying CBIQD-induced cancer cell cytotoxicity: CBIQD, when bound to tubulin, may prevent it to maintain a straight conformation; consequently, the α- and β-heterodimers might be no longer available for MT growth. Thus, the consolidated spectroscopic research described herein explores the potential of CBIQD as a new paradigm in the design and development of novel unfused or nonring-fused naphthalimide-based antimitotic cancer therapeutics in medicinal chemistry research.

Elucidating an Amorphous Form Stabilization Mechanism for Tenapanor Hydrochloride: Crystal Structure Analysis Using X-ray Diffraction, NMR Crystallography, and Molecular Modeling

By the combined use of powder and single-crystal X-ray diffraction, solid-state NMR, and molecular modeling, the crystal structures of two systems containing the unusually large tenapanor drug molecule have been determined: the free form, ANHY, and a dihydrochloride salt form, 2HCl. Dynamic nuclear polarization (DNP) assisted solid-state NMR (SSNMR) crystallography investigations were found essential for the final assignment and were used to validate the crystal structure of ANHY. From a structural informatics analysis of ANHY and 2HCl, conformational ring differences in one part of the molecule were observed which influence the relative orientation of a methyl group on a ring nitrogen and thereby impact the crystallizability of the dihydrochloride salt. From quantum chemistry calculations, the dynamics between different ring conformations in tenapanor is predicted to be fast. Addition of HCl to tenapanor results in general in a mixture of protonated ring conformers and hence a statistical mix of diastereoisomers which builds up the amorphous form, a-2HCl. This was qualitatively verified by ¹³C CP/MAS NMR investigations of the amorphous form. Thus, to form any significant amount of the crystalline material 2HCl, which originates from the minor (i.e., energetically less stable) ring conformations, one needs to involve nitrogen deprotonation to allow exchange between the minor and major conformations of ANHY in solution. Thus, by controlling the solution pH value to well below the p K_a of ANHY, the equilibrium between ANHY and 2HCl can be controlled and by this mechanism the crystallization of 2HCl can be avoided and the amorphous form of the dichloride salt can therefore be stabilized.

Discovery and Identification of an Endogenous Metabolite of Tramiprosate and Its Prodrug ALZ-801 that Inhibits Beta Amyloid Oligomer Formation in the Human Brain

BACKGROUND

ALZ-801 is an oral, small-molecule inhibitor of beta amyloid (Aβ) oligomer formation in clinical development for Alzheimer's disease (AD). ALZ-801 is a prodrug of tramiprosate with improved pharmacokinetic properties and gastrointestinal tolerability. During clinical studies, we discovered that the primary metabolite of tramiprosate and its prodrug ALZ-801, 3-sulfopropanoic acid (3-SPA), is an endogenous molecule in the human brain and present in the cerebrospinal fluid (CSF) of patients with AD and other neurodegenerative brain diseases.

OBJECTIVE

The objectives of this research were to (1) identify and confirm the presence of 3-SPA in CSF samples from elderly, drug-naïve patients with memory deficits; (2) quantify the levels of 3-SPA in the CSF of patients with AD from tramiprosate phase III North American (NA) trial; (3) evaluate the in vitro anti-Aβ42 oligomer activity of 3-SPA; and (4) characterize the pharmacokinetics and brain-penetration properties of 3-SPA.

METHODS

Lumbar CSF samples from 64 drug-naïve patients with cognitive deficits (Mini-Mental State Examination [MMSE] score range 15-30) and six patients with AD treated with tramiprosate 150 mg twice daily in the phase III trial, at week 78, were analyzed. We used liquid chromatography-tandem mass spectrometry to confirm the structural molecular identity of endogenous 3-SPA with a 3-SPA reference standard and ion-mobility spectrometry-mass spectrometry with molecular dynamics to characterize interactions of 3-SPA with Aβ42 monomers, and the resultant conformational alterations. Rat studies using oral (30 mg/kg) and intravenous (10 mg/kg) doses were conducted to characterize the pharmacokinetic properties and brain penetration of 3-SPA.

RESULTS

We confirmed the presence of 3-SPA in the CSF of drug-naïve patients with cognitive deficits (mean concentration 11.7 ± 4.3 nM). The mean concentration of 3-SPA in patients with AD treated with tramiprosate was 135 ± 51 nM. In vitro studies revealed a multi-ligand interaction of 3-SPA with monomeric Aβ42 that inhibits the aggregation of Aβ42 into small oligomers. Comparisons of the molecular interactions of tramiprosate and 3-SPA with Aβ42 are also presented. Furthermore, in rat preclinical studies, 3-SPA displayed 100% oral bioavailability and 25% brain penetration, indicating that the metabolite is well absorbed and crosses the blood-brain barrier.

CONCLUSIONS

We confirmed the endogenous presence of 3-SPA, the major metabolite of tramiprosate, in the CSF of drug-naïve elderly patients with memory deficits due to AD and a variety of other neurodegenerative disorders. The levels of 3-SPA were up to 12.6-fold greater in patients with AD receiving tramiprosate than in drug-naïve patients. In addition, we showed that 3-SPA has potent anti-Aβ oligomer activity, inhibiting aggregation of Aβ42 into small oligomers with efficacy comparable to that of tramiprosate. 3-SPA displays excellent oral availability and brain penetration in rats, suggesting that the higher CSF concentrations of 3-SPA in the human brain after oral administration of ALZ-801 or tramiprosate (and subsequent conversion to 3-SPA) result from the penetration of the metabolite into the central nervous system. These data suggest that 3-SPA is an endogenous agent with potential activity stabilizing the conformational flexibility of Aβ monomers that, in turn, inhibit Aβ misfolding and formation of soluble toxic Aβ oligomers in humans, thereby preventing the initial pathogenic step in the progression of AD. Clinical improvements observed in patients with AD carrying the ε4 allele of the apolipoprotein E gene in tramiprosate phase III studies may in part be explained by the therapeutic effects of excess levels of the metabolite in the brains of these patients. The potential protective role of 3-SPA in AD pathogenesis, as well as its therapeutic role in AD and other neurodegenerative disorders, warrants further investigation.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Design and Structure-Guided Development of Novel Inhibitors of the Xeroderma Pigmentosum Group A (XPA) Protein-DNA Interaction

XPA is a unique and essential protein required for the nucleotide excision DNA repair pathway and represents a therapeutic target in oncology. Herein, we are the first to develop novel inhibitors of the XPA-DNA interaction through structure-guided drug design efforts. Ester derivatives of the compounds 1 (X80), 22, and 24 displayed excellent inhibitory activity (IC₅₀ of 0.82 ± 0.18 μM and 1.3 ± 0.22 μM, respectively) but poor solubility. We have synthesized novel amide derivatives that retain potency and have much improved solubility. Furthermore, compound 1 analogs exhibited good specificity for XPA over RPA (replication protein A), another DNA-binding protein that participates in the nucleotide excision repair (NER) pathway. Importantly, there were no significant interactions observed by the X80 class of compounds directly with DNA. Molecular docking studies revealed a mechanistic model for the interaction, and these studies could serve as the basis for continued analysis of structure-activity relationships and drug development efforts of this novel target.

Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models

We present the estimation of solvation free energies of small solutes in water, n-octanol and hexane using molecular dynamics simulations with two MARTINI models at different resolutions, viz. the coarse-grained (CG) and the hybrid all-atom/coarse-grained (AA/CG) models. From these estimates, we also calculate the water/hexane and water/octanol partition coefficients. More than 150 small, organic molecules were selected from the Minnesota solvation database and parameterized in a semi-automatic fashion. Using either the CG or hybrid AA/CG models, we find considerable deviations between the estimated and experimental solvation free energies in all solvents with mean absolute deviations larger than 10 kJ/mol, although the correlation coefficient is between 0.55 and 0.75 and significant. There is also no difference between the results when using the non-polarizable and polarizable water model, although we identify some improvements when using the polarizable model with the AA/CG solutes. In contrast to the estimated solvation energies, the estimated partition coefficients are generally excellent with both the CG and hybrid AA/CG models, giving mean absolute deviations between 0.67 and 0.90 log units and correlation coefficients larger than 0.85. We analyze the error distribution further and suggest avenues for improvements.

Advancing Drug Discovery through Enhanced Free Energy Calculations

A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.

Antiplasmodial Mode of Action of Pantothenamides: Pantothenate Kinase Serves as a Metabolic Activator Not as a Target

N-Substituted pantothenamides (PanAms) are pantothenate analogues with up to nanomolar potency against blood-stage Plasmodium falciparum (the most virulent species responsible for malaria). Although these compounds are known to target coenzyme A (CoA) biosynthesis and/or utilization, their exact mode of action (MoA) is still unknown. Importantly, PanAms that retain the natural β-alanine moiety are more potent than other variants, consistent with the involvement of processes that are selective for pantothenate (the precursor of CoA) or its derivatives. The transport of pantothenate and its phosphorylation by P. falciparum pantothenate kinase (PfPanK, the first enzyme of CoA biosynthesis) are two such processes previously highlighted as potential targets for the PanAms' antiplasmodial action. In this study, we investigated the effect of PanAms on these processes using their radiolabeled versions (synthesized here for the first time), which made possible the direct measurement of PanAm uptake by isolated blood-stage parasites and PanAm phosphorylation by PfPanK present in parasite lysates. We found that the MoA of PanAms does not involve interference with pantothenate transport and that inhibition of PfPanK-mediated pantothenate phosphorylation does not correlate with PanAm antiplasmodial activity. Instead, PanAms that retain the β-alanine moiety were found to be metabolically activated by PfPanK in a selective manner, forming phosphorylated products that likely inhibit other steps in CoA biosynthesis or are transformed into CoA antimetabolites that can interfere with CoA utilization. These findings provide direction for the ongoing development of CoA-targeted inhibitors as antiplasmodial agents with clinical potential.

Sublimation Entropy and Dissociation Constants Prediction by Quantitative Evaluation of Molecular Mobility in Crystals

Prediction of binding free energies (or dissociation constants) is a crucial challenge for computational biochemistry. One of the main problems here consists in fast and accurate evaluation of binding entropy, which is far more time-consuming than evaluation of binding enthalpy. Here, we offer a fast and rather accurate approach to evaluate the sublimation entropy (i.e., entropy of binding of a vapor molecule to a crystal, taken with the opposite sign) from the average range of molecular movements in the solid state. To estimate this range (and the corresponding amplitude), we considered equilibrium sublimation of small organic molecules from molecular crystals. The calculations were based on experimental data on the sublimation enthalpy, pressure of saturated vapor, and structural characteristics of the molecule in question. The resulting average amplitude (0.17 ± 0.01 Å) of molecular movements in crystals was used to predict sublimation entropies and dissociation constants for sublimation of 28 molecular crystals. The results of these predictions are in close agreement with experimental values.

Synthesis and chemical characterization of several perfluorinated sialic acid glycals and evaluation of their in vitro antiviral activity against Newcastle disease virus

Newcastle Disease Virus (NDV), belonging to the Paramyxoviridae family, causes a serious infectious disease in birds, resulting in severe losses in the poultry industry every year. Haemagglutinin neuraminidase glycoprotein (HN) has been recognized as a key protein in the viral infection mechanism, and its inhibition represents an attractive target for the development of new drugs based on sialic acid glycals, with the 2-deoxy-2,3-didehydro-d-N-acetylneuraminic acid (Neu5Ac2en) as their backbone. Herein we report the synthesis of several Neu5Ac2en glycals and of their perfluorinated C-5 modified derivatives, including their respective stereoisomers at C-4, together with evaluation of their in vitro antiviral activity. While all synthesized compounds were found to be active HN inhibitors in the micromolar range, we found that their potency was influenced by the chain-length of the C-5 perfluorinated acetamido functionality. Thus, the binding modes of the inhibitors were also investigated by performing a docking study. Moreover, the perfluorinated glycals were found to be more active than the corresponding normal C-5 acylic derivatives. Finally, cell-cell fusion assays on NDV infected cells revealed that the addition of a newly synthesized C-4α heptafluorobutyryl derivative almost completely inhibited NDV-induced syncytium formation.

Assessing the performance of docking scoring function, FEP, MM-GBSA, and QM/MM-GBSA approaches on a series of PLK1 inhibitors

Over-expressed polo-like kinases 1, a key regulator of cell mitosis, is associated with carcinogenesis and poor prognosis. It is very necessary to develop a reliable computational affinity prediction protocol targeting PLK1. In this study, the performance of different docking scoring function, free energy perturbation, MM-GBSA and QM/MM-GBSA were evaluated. The ranking capability of FEP is the best with r_s = 0.854. However, the r_s obtained from MM-GBSA can reach 0.767, which requires only about one-eighth of the simulation time of FEP. As for the sampling method, single long molecular dynamics (SLMD) surpass the multiple short molecular dynamics (MSMD) in ranking of the 20 congeneric compounds by about 0.1 in r_s. In addition, ligands treated by QM can significantly improve the ranking performance. As for the docking scoring functions, a force field-based scoring function is more suitable for ranking congeneric compounds.

1.14*CM1A-LBCC: Localized Bond-Charge Corrected CM1A Charges for Condensed-Phase Simulations

The quality of the 1.14*CM1A and 1.20*CM5 charge models was evaluated for calculations of free energies of hydration. For a set of 426 neutral molecules, 1.14*CM1A and 1.20*CM5 yield MADs of 1.26 and 1.21 kcal/mol, respectively. The 1.14*CM1A charges, which can be readily obtained for large systems, exhibit large deviations only for a subset of functional groups. The results for these cases were systematically improved using localized bond-charge corrections (LBCC) by which offsetting adjustments are made to the partial charges for atoms in specified bond types. Only 19 LBCCs were needed to yield 1.14*CM1A-LBCC charges that reduce the errors for the 426 ΔG_hyd values to only 0.61 kcal/mol. The modified charge method was also tested in computation of heats of vaporization and densities for pure organic liquids, yielding average errors of 1.40 kcal/mol and 0.024 g/cm³, similar to those for 1.14*CM1A.

Natural Prenylchalconaringenins and Prenylnaringenins as Antidiabetic Agents: α-Glucosidase and α-Amylase Inhibition and in Vivo Antihyperglycemic and Antihyperlipidemic Effects

Inhibition of α-glucosidase and α-amylase decreases postprandial blood glucose levels and delays glucose absorption, making it a treatment strategy for type 2 diabetes. This study examined in vivo and in vitro antidiabetic activities of natural prenylchalconaringenins 1 and 2 and prenylnaringenins 3 and 4, found in hops and beer. 3'-Geranylchalconaringenin (2) competitively and irreversibly inhibited α-glucosidase (IC₅₀ = 1.08 μM) with activity 50-fold higher than that of acarbose (IC₅₀ = 51.30 μM) and showed moderate inhibitory activity against α-amylase (IC₅₀ = 20.46 μM). Docking analysis substantiated these findings. In addition, compound 2 suppressed the increase in postprandial blood glucose levels and serum levels of total cholesterol and triglycerides in streptozotocin-induced diabetic mice. Taken together, these results suggest that 2 has dual inhibitory activity against α-glucosidase and α-amylase and alleviates diabetic hyperglycemia and hyperlipidemia, making it a potential functional food ingredient and drug candidate for management of type 2 diabetes.

Elucidating the Aβ42 Anti-Aggregation Mechanism of Action of Tramiprosate in Alzheimer's Disease: Integrating Molecular Analytical Methods, Pharmacokinetic and Clinical Data

BACKGROUND

Amyloid beta (Aβ) oligomers play a critical role in the pathogenesis of Alzheimer's disease (AD) and represent a promising target for drug development. Tramiprosate is a small-molecule Aβ anti-aggregation agent that was evaluated in phase III clinical trials for AD but did not meet the primary efficacy endpoints; however, a pre-specified subgroup analysis revealed robust, sustained, and clinically meaningful cognitive and functional effects in patients with AD homozygous for the ε4 allele of apolipoprotein E4 (APOE4/4 homozygotes), who carry an increased risk for the disease. Therefore, to build on this important efficacy attribute and to further improve its pharmaceutical properties, we have developed a prodrug of tramiprosate ALZ-801 that is in advanced stages of clinical development. To elucidate how tramiprosate works, we investigated its molecular mechanism of action (MOA) and the translation to observed clinical outcomes.

OBJECTIVE

The two main objectives of this research were to (1) elucidate and characterize the MOA of tramiprosate via an integrated application of three independent molecular methodologies and (2) present an integrated translational analysis that links the MOA, conformation of the target, stoichiometry, and pharmacokinetic dose exposure to the observed clinical outcome in APOE4/4 homozygote subjects.

METHOD

We used three molecular analytical methods-ion mobility spectrometry-mass spectrometry (IMS-MS), nuclear magnetic resonance (NMR), and molecular dynamics-to characterize the concentration-related interactions of tramiprosate versus Aβ42 monomers and the resultant conformational alterations affecting aggregation into oligomers. The molecular stoichiometry of the tramiprosate versus Aβ42 interaction was further analyzed in the context of clinical pharmacokinetic dose exposure and central nervous system Aβ42 levels (i.e., pharmacokinetic-pharmacodynamic translation in humans).

RESULTS

We observed a multi-ligand interaction of tramiprosate with monomeric Aβ42, which differs from the traditional 1:1 binding. This resulted in the stabilization of Aβ42 monomers and inhibition of oligomer formation and elongation, as demonstrated by IMS-MS and molecular dynamics. Using NMR spectroscopy and molecular dynamics, we also showed that tramiprosate bound to Lys16, Lys28, and Asp23, the key amino acid side chains of Aβ42 that are responsible for both conformational seed formation and neuronal toxicity. The projected molar excess of tramiprosate versus Aβ42 in humans using the dose effective in patients with AD aligned with the molecular stoichiometry of the interaction, providing a clear clinical translation of the MOA. A consistent alignment of these preclinical-to-clinical elements describes a unique example of translational medicine and supports the efficacy seen in symptomatic patients with AD. This unique "enveloping mechanism" of tramiprosate also provides a potential basis for tramiprosate dose selection for patients with homozygous AD at earlier stages of disease.

CONCLUSION

We have identified the molecular mechanism that may account for the observed clinical efficacy of tramiprosate in patients with APOE4/4 homozygous AD. In addition, the integrated application of the molecular methodologies (i.e., IMS-MS, NMR, and thermodynamics analysis) indicates that it is feasible to modulate and control the Aβ42 conformational dynamics landscape by a small molecule, resulting in a favorable Aβ42 conformational change that leads to a clinically relevant amyloid anti-aggregation effect and inhibition of oligomer formation. This novel enveloping MOA of tramiprosate has potential utility in the development of disease-modifying therapies for AD and other neurodegenerative diseases caused by misfolded proteins.

Relative Binding Free Energy Calculations Applied to Protein Homology Models

A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

The effects of electronic polarization in biomolecular interactions will differ depending on the local dielectric constant of the environment, such as in solvent, DNA, proteins, and membranes. Here the performance of the AMOEBA polarizable force field is evaluated under nonaqueous conditions by calculating the solvation free energies of small molecules in four common organic solvents. Results are compared with experimental data and equivalent simulations performed with the GAFF pairwise-additive force field. Although AMOEBA results give mean errors close to "chemical accuracy," GAFF performs surprisingly well, with statistically significantly more accurate results than AMOEBA in some solvents. However, for both models, free energies calculated in chloroform show worst agreement to experiment and individual solutes are consistently poor performers, suggesting non-potential-specific errors also contribute to inaccuracy. Scope for the improvement of both potentials remains limited by the lack of high quality experimental data across multiple solvents, particularly those of high dielectric constant. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Hydrocarbon stapled B chain analogues of relaxin-3 retain biological activity

Relaxin-3 or insulin-like peptide 7 (INSL7) is the most recently discovered relaxin/insulin-like family peptide. Mature relaxin-3 consists of an A chain and a B chain held by disulphide bonds. According to structure activity relationship studies, the relaxin-3 B chain is more important in binding and activating the receptor. RXFP3 (also known as Relaxin-3 receptor 1, GPCR 135, somatostatin- and angiotensin- like peptide receptor or SALPR) was identified as the cognate receptor for relaxin-3 by expression profiles and binding studies. Recent studies imply roles of this system in mediating stress and anxiety, feeding, metabolism and cognition. Stapling of peptides is a technique used to develop peptide drugs for otherwise undruggable targets. The main advantages of stapling include, increased activity due to reduced proteolysis, increased affinity to receptors and increased cell permeability. Stable agonists and antagonists of RXFP3 are crucial for understanding the physiological significance of this system. So far, agonists and antagonists of RXFP3 are peptides. In this study, for the first time, we have introduced stapling of the relaxin-3 B chain at 14th and 18th positions (14s18) and 18th and 22nd position (18s22). These stapled peptides showed greater helicity than the unstapled relaxin-3 B chain in circular dichroism analysis. Both stapled peptides bound RXFP3 and activated RXFP3 as observed in an inhibition of forskolin-induced cAMP assay and a ERK1/2 activation assay, although with different potencies. Therefore, we conclude that stapling of the relaxin3 B chain does not compromise its ability to activate RXFP3 and is a promising method for developing stable peptide agonists and antagonists of RXFP3 to aid relaxin-3/RXFP3 research.

Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation

The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A_2AAR, β₁ adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A_2A receptor (A_2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.

Application of Free Energy Perturbation for the Design of BACE1 Inhibitors

Novel spiroaminodihydropyrroles probing for optimized interactions at the P3 pocket of β-secretase 1 (BACE1) were designed with the use of free energy perturbation (FEP) calculations. The resulting molecules showed pIC50 potencies in enzymatic BACE1 inhibition assays ranging from approximately 5 to 7. Good correlation was observed between the predicted activity from the FEP calculations and experimental activity. Simulations run with a default 5 ns approach delivered a mean unsigned error (MUE) between prediction and experiment of 0.58 and 0.91 kcal/mol for retrospective and prospective applications, respectively. With longer simulations of 10 and 20 ns, the MUE was in both cases 0.57 kcal/mol for the retrospective application, and 0.69 and 0.59 kcal/mol for the prospective application. Other considerations that impact the quality of the calculations are discussed. This work provides an example of the value of FEP as a computational tool for drug discovery.

Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water

Partition coefficients describe how a solute is distributed between two immiscible solvents. They are used in drug design as a measure of a solute's hydrophobicity and a proxy for its membrane permeability. We calculate partition coefficients from transfer free energies using molecular dynamics simulations in explicit solvent. Setup is done by our new Solvation Toolkit which automates the process of creating input files for any combination of solutes and solvents for many popular molecular dynamics software packages. We calculate partition coefficients between octanol/water and cyclohexane/water with the Generalized AMBER Force Field (GAFF) and the Dielectric Corrected GAFF (GAFF-DC). With similar methods in the past we found a root-mean-squared error (RMSE) of 6.3 kJ/mol in hydration free energies which would correspond to an error of around 1.6 log units in partition coefficients if solvation free energies in both solvents were estimated with comparable accuracy. Here we find an overall RMSE of about 1.2 log units with both force fields. Results from GAFF and GAFF-DC seem to exhibit systematic biases in opposite directions for calculated cyclohexane/water partition coefficients.

All-atom/coarse-grained hybrid predictions of distribution coefficients in SAMPL5

We present blind predictions submitted to the SAMPL5 challenge on calculating distribution coefficients. The predictions were based on estimating the solvation free energies in water and cyclohexane of the 53 compounds in the challenge. These free energies were computed using alchemical free energy simulations based on a hybrid all-atom/coarse-grained model. The compounds were treated with the general Amber force field, whereas the solvent molecules were treated with the Elba coarse-grained model. Considering the simplicity of the solvent model and that we approximate the distribution coefficient with the partition coefficient of the neutral species, the predictions are of good accuracy. The correlation coefficient, R is 0.64, 82 % of the predictions have the correct sign and the mean absolute deviation is 1.8 log units. This is on a par with or better than the other simulation-based predictions in the challenge. We present an analysis of the deviations to experiments and compare the predictions to another submission that used all-atom solvent.

Lead Discovery of Type II BRAF V600E Inhibitors Targeting the Structurally Validated DFG-Out Conformation Based upon Selected Fragments

The success of the first approved kinase inhibitor imatinib has spurred great interest in the development of type II inhibitors targeting the inactive DFG-out conformation, wherein the Phe of the DFG motif at the start of the activation loop points into the ATP binding site. Nevertheless, kinase inhibitors launched so far are heavily biased toward type I inhibitors targeting the active DFG-in conformation, wherein the Phe of the DFG motif flips by approximately 180° relative to the inactive conformation, resulting in Phe and Asp swapping their positions. Data recently obtained with structurally validated type II inhibitors supported the conclusion that type II inhibitors are more selective than type I inhibitors. In our type II BRAF V600E inhibitor lead discovery effort, we identified phenylaminopyrimidine (PAP) and unsymmetrically disubstituted urea as two fragments that are frequently presented in FDA-approved protein kinase inhibitors. We therefore defined PAP and unsymmetrically disubstituted urea as privileged fragments for kinase drug discovery. A pharmacophore for type II inhibitors, 4-phenylaminopyrimidine urea (4-PAPU), was assembled based upon these privileged fragments. Lead compound SI-046 with BRAF V600E inhibitory activity comparable to the template compound sorafenib was in turn obtained through preliminary structure-activity relationship (SAR) study. Molecular docking suggested that SI-046 is a bona fide type II kinase inhibitor binding to the structurally validated "classical DFG-out" conformation of BRAF V600E. Our privileged fragments-based approach was shown to efficiently deliver a bona fide type II kinase inhibitor lead. In essence, the theme of this article is to showcase the strategy and rationale of our approach.

Biomolecular Force Field Parameterization via Atoms-in-Molecule Electron Density Partitioning

Molecular mechanics force fields, which are commonly used in biomolecular modeling and computer-aided drug design, typically treat nonbonded interactions using a limited library of empirical parameters that are developed for small molecules. This approach does not account for polarization in larger molecules or proteins, and the parametrization process is labor-intensive. Using linear-scaling density functional theory and atoms-in-molecule electron density partitioning, environment-specific charges and Lennard-Jones parameters are derived directly from quantum mechanical calculations for use in biomolecular modeling of organic and biomolecular systems. The proposed methods significantly reduce the number of empirical parameters needed to construct molecular mechanics force fields, naturally include polarization effects in charge and Lennard-Jones parameters, and scale well to systems comprised of thousands of atoms, including proteins. The feasibility and benefits of this approach are demonstrated by computing free energies of hydration, properties of pure liquids, and the relative binding free energies of indole and benzofuran to the L99A mutant of T4 lysozyme.

Development of a True Transition State Force Field from Quantum Mechanical Calculations

Transition state force fields (TSFF) treated the TS structure as an artificial minimum on the potential energy surface in the past decades. The necessary parameters were developed either manually or by the Quantum-to-molecular mechanics method (Q2MM). In contrast with these approaches, here we propose to model the TS structures as genuine saddle points at the molecular mechanics level. Different methods were tested on small model systems of general chemical reactions such as protonation, nucleophilic attack, and substitution, and the new procedure led to more accurate models than the Q2MM-type parametrization. To demonstrate the practicality of our approach, transferrable parameters have been developed for Mo-catalyzed olefin metathesis using quantum mechanical properties as reference data. Based on the proposed strategy, any force field can be extended with true transition state force field (TTSFF) parameters, and they can be readily applied in several molecular mechanics programs as well.

Towards understanding the unbound state of drug compounds: Implications for the intramolecular reorganization energy upon binding

There has been an explosion of structural information for pharmaceutical compounds bound to biological targets, but the conformations and dynamics of compounds free in solution are poorly characterized, if at all. Yet, knowledge of the unbound state is essential to understand the fundamentals of molecular recognition, including the much debated conformational intramolecular reorganization energy of a compound upon binding (ΔEReorg). Also, dependable observation of the unbound compounds is important for ligand-based drug discovery, e.g. with pharmacophore modelling. Here, these questions are addressed with long (⩾0.5μs) state-of-the-art molecular dynamics (MD) simulations of 26 compounds (including 7 approved drugs) unbound in explicit solvent. These compounds were selected to be chemically diverse, with a range of flexibility, and good quality bioactive X-ray structures. The MD-simulated free compounds are compared to their bioactive structure and conformers generated with ad hoc sampling in vacuo or with implicit generalized Born (GB) aqueous solvation models. The GB conformational models clearly depart from those obtained in explicit solvent, and suffer from conformational collapse almost as severe as in vacuo. Thus, the global energy minima in vacuo or with GB are not suitable representations of the unbound state, which can instead be extensively sampled by MD simulations. Many, but not all, MD-simulated compounds displayed some structural similarity to their bioactive structure, supporting the notion of conformational pre-organization for binding. The ligand-protein complexes were also simulated in explicit solvent, to estimate ΔEReorg as an enthalpic difference ΔHReorg between the intramolecular energies in the bound and unbound states. This fresh approach yielded ΔHReorg values⩽6kcal/mol for 18 out of 26 compounds. For three particularly polar compounds 15⩽ΔHReorg⩽20kcal/mol, supporting the notion that ΔHReorg can be substantial. Those large ΔHReorg values correspond to a redistribution of electrostatic interactions upon binding. Overall, the study illustrates how MD simulations offer a promising avenue to characterize the unbound state of medicinal compounds.

AB-Bind: Antibody binding mutational database for computational affinity predictions

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.

Prediction of Solvation Free Energies with Thermodynamic Integration Using the General Amber Force Field

Computer-aided drug design (CADD) techniques can be very effective in reducing costs and speeding up drug discovery. The determination of binding and solvation free energies is pivotal for this process and is, therefore, the subject of many studies. In this work, the solvation free energy change (ΔΔGsolv) for a total of 92 transformations in small molecules was predicted using Thermodynamic Integration (TI). It was our aim to compare experimental and calculated solvation free energies for typical and prime additions considered in drug optimizations, analyzing trends, and optimizing a TI protocol. The results showed a good agreement between experimental and predicted values, with an overestimation of the predicted values for CH3, halogens, and NH2, as well as an underestimation for CONH2, but all fall within ±1 kcal/mol. NO2 addition showed a larger and systematic underestimation of the predicted ΔΔGsolv, indicating the need for special attention in these cases. For small molecules, if no experimental data is available, using TI as a theoretical strategy thus appears to be a suitable choice in CADD. It provides a good compromise between time and accuracy.

Predicting Partition Coefficients with a Simple All-Atom/Coarse-Grained Hybrid Model

The solvation free energy is an essential quantity in force field development and in numerous applications. Here, we present the estimation of solvation free energies in polar (water, hexanol, octanol, and nonanol) and in apolar (hexane, octane, and nonane) media. The estimates are produced using molecular dynamics simulations employing a simple all-atom/coarse-grained hybrid model (AA/ELBA) and are therefore very efficient. More than 150 solutes were taken from the Minnesota solvation database and represent small, organic molecules. The mean absolute deviation for the different solvents ranges between 2.0 and 4.1 kJ/mol, and the correlation coefficient ranges between 0.78 and 0.99, indicating that the predictions are accurate. Outliers are identified, and potential avenues for improvements are discussed. Furthermore, partition coefficients between water and the organic solvents were estimated, and the percentage of the predictions that has the correct sign ranges between 74% (for octane) and 92% (for octanol and hexanol). Finally, membrane/water partition coefficients are replaced with hexane/water and octanol/water partition coefficients, and the latter is found to be as accurate as the expensive membrane calculations, indicating a wider application area.

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.

Structure-Based Virtual Screening and Biological Evaluation of a Calpain Inhibitor for Prevention of Selenite-Induced Cataractogenesis in an in Vitro System

Calpains belong to the family of calcium-dependent, structurally related intracellular cysteine proteases that exhibit significant functions in evolution of different types of cataracts in human as well as animal models. Application of calpain inhibitors generated through a virtual screening workflow may provide new avenues for the prevention of cataractogenesis. Hence, in the current study, compounds were first screened for potent calpain inhibitory activity by employing a structure-based approach, and the screening results were then validated through biological experiments in rat lenses. A hit compound, HTS08688, was obtained by structure-based virtual screening. A micromolar concentration of HTS08688 was found to prevent in vitro cataractogenesis in isolated Wistar rat lenses, while maintaining the antioxidant and calcium concentrations at near normal levels. Inhibition of superoxide anion generation, as observed through cytochemical localization studies, and maintenance of structural integrity, as demonstrated by histological analysis of lenticular tissue, also suggested that HTS08688 can ameliorate the cataractous condition induced by selenite in an in vitro rodent model. A cell proliferation assay was performed; the IC 50 value of the screened calpain inhibitor, HTS08688, against human lenticular epithelial cells-b3 was found to be 177 μM/mL. This combined theoretical and experimental approach has demonstrated a potent lead compound, HTS08688, that exhibits putative anticataractogenic activity by virtue of its potential to inhibit calpain.

Parameterization of an effective potential for protein-ligand binding from host-guest affinity data

Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.

Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid Receptor 1

Lipid biology continues to emerge as an area of significant therapeutic interest, particularly as the result of an enhanced understanding of the wealth of signaling molecules with diverse physiological properties. This growth in knowledge is epitomized by lysophosphatidic acid (LPA), which functions through interactions with at least six cognate G protein-coupled receptors. Herein, we present three crystal structures of LPA1 in complex with antagonist tool compounds selected and designed through structural and stability analyses. Structural analysis combined with molecular dynamics identified a basis for ligand access to the LPA1 binding pocket from the extracellular space contrasting with the proposed access for the sphingosine 1-phosphate receptor. Characteristics of the LPA1 binding pocket raise the possibility of promiscuous ligand recognition of phosphorylated endocannabinoids. Cell-based assays confirmed this hypothesis, linking the distinct receptor systems through metabolically related ligands with potential functional and therapeutic implications for treatment of disease.

Determination of partial molar volumes from free energy perturbation theory

Partial molar volume is an important thermodynamic property that gives insights into molecular size and intermolecular interactions in solution. Theoretical frameworks for determining the partial molar volume (V°) of a solvated molecule generally apply Scaled Particle Theory or Kirkwood-Buff theory. With the current abilities to perform long molecular dynamics and Monte Carlo simulations, more direct methods are gaining popularity, such as computing V° directly as the difference in computed volume from two simulations, one with a solute present and another without. Thermodynamically, V° can also be determined as the pressure derivative of the free energy of solvation in the limit of infinite dilution. Both approaches are considered herein with the use of free energy perturbation (FEP) calculations to compute the necessary free energies of solvation at elevated pressures. Absolute and relative partial molar volumes are computed for benzene and benzene derivatives using the OPLS-AA force field. The mean unsigned error for all molecules is 2.8 cm(3) mol(-1). The present methodology should find use in many contexts such as the development and testing of force fields for use in computer simulations of organic and biomolecular systems, as a complement to related experimental studies, and to develop a deeper understanding of solute-solvent interactions.

Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms

CONSPECTUS: This Account highlights recent advances and discusses major challenges in the field of drug-target recognition, binding, and unbinding studied using metadynamics-based approaches, with particular emphasis on their role in structure-based design. Computational chemistry has significantly contributed to drug design and optimization in an extremely broad range of areas, including prediction of target druggability and drug likeness, de novo design, fragment screening, ligand docking, estimation of binding affinity, and modulation of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. Computationally driven drug discovery must continuously adapt to keep pace with the evolving knowledge of the factors that modulate the pharmacological action of drugs. There is thus an urgent need for novel computational approaches that integrate the vast amount of complex information currently available for small (bio)organic compounds, biologically relevant targets and their complexes, while also accounting accurately for the thermodynamics and kinetics of drug-target association, the intrinsic dynamical behavior of biomolecular systems, and the complexity of protein-protein networks. Understanding the mechanism of drug binding to and unbinding from biological targets is fundamental for optimizing lead compounds and designing novel biologically active ones. One major challenge is the accurate description of the conformational complexity prior to and upon formation of drug-target complexes. Recently, enhanced sampling methods, including metadynamics and related approaches, have been successfully applied to investigate complex mechanisms of drugs binding to flexible targets. Metadynamics is a family of enhanced sampling techniques aimed at enhancing the rare events and reconstructing the underlying free energy landscape as a function of a set of order parameters, usually referred to as collective variables. Studies of drug binding mechanisms have predicted the most probable association and dissociation pathways and the related binding free energy profile. In addition, the availability of an efficient open-source implementation, running on cost-effective GPU (i.e., graphical processor unit) architectures, has considerably decreased the learning curve and the computational costs of the methods, and increased their adoption by the community. Here, we review the recent contributions of metadynamics and other enhanced sampling methods to the field of drug-target recognition and binding. We discuss how metadynamics has been used to search for transition states, to predict binding and unbinding paths, to treat conformational flexibility, and to compute free energy profiles. We highlight the importance of such predictions in drug discovery. Major challenges in the field and possible solutions will finally be discussed.