The reorganization energy of compounds upon binding to proteins, from dynamic and solvated bound and unbound states.

The intramolecular reorganization energy (ΔEReorg) of compounds upon binding to proteins is a component of the binding free energy, which has long received particular attention, for fundamental and practical reasons. Understanding ΔEReorg would benefit the science of molecular recognition and drug design. For instance, the tolerable strain energy of compounds upon binding has been elusive. Prior studies found some large ΔEReorg values (e.g. > 10 kcal/mol), received with skepticism since they imply excessive opposition to binding. Indeed, estimating ΔEReorg is technically difficult. Typically, ΔEReorg has been approached by taking two energy-minimized conformers representing the bound and unbound states, and subtracting their conformational energy. This is a drastic oversimplification, liable to conformational collapse of the unbound conformer. Instead, the present work applies extensive molecular dynamics (MD) and the modern OPLS3 force-field to simulate compounds bound and unbound states, in explicit solvent under physically relevant conditions. The thermalized unbound compounds populate multiple conformations, not reducible to one or a few energy-minimized conformers. The intramolecular energies in the bound and unbound states were averaged over pertinent conformational ensembles, and the reorganization enthalpy upon binding (ΔHReorg) deduced by subtraction. This was applied to 76 systems, including 43 approved drugs, carefully selected for i) the quality of the bioactive X-ray structures and ii) the diversity of the chemotypes, their properties and protein targets. It yielded comparatively low ΔHReorg values (median = 1.4 kcal/mol, mean = 3.0 kcal/mol). A new finding is the observation of negative ΔHReorg values. Indeed, reorganization energies do not have to oppose binding, e.g. when intramolecular interactions stabilize preferentially the bound state. Conversely, even with competing water molecules, intramolecular interactions can occur predominantly in the unbound compound, and be replaced by intermolecular counterparts upon protein binding. Such disruption of intramolecular interactions upon binding gives rise to occasional larger ΔHReorg values. Such counterintuitive larger ΔHReorg values may be rationalized as a redistribution of interactions upon binding, qualitatively compatible with binding.

Design and Synthesis of Pyrrolo[2,3-d]pyrimidine-Derived Leucine-Rich Repeat Kinase 2 (LRRK2) Inhibitors Using a Checkpoint Kinase 1 (CHK1)-Derived Crystallographic Surrogate.

Inhibitors of leucine-rich repeat kinase 2 (LRRK2) and mutants, such as G2019S, have potential utility in Parkinson's disease treatment. Fragment hit-derived pyrrolo[2,3-d]pyrimidines underwent optimization using X-ray structures of LRRK2 kinase domain surrogates, based on checkpoint kinase 1 (CHK1) and a CHK1 10-point mutant. (2R)-2-Methylpyrrolidin-1-yl derivative 18 (LRRK2 G2019S cKi 0.7 nM, LE 0.66) was identified, with increased potency consistent with an X-ray structure of 18/CHK1 10-pt. mutant showing the 2-methyl substituent proximal to Ala147 (Ala2016 in LRRK2). Further structure-guided elaboration of 18 gave the 2-[(1,3-dimethyl-1H-pyrazol-4-yl)amino] derivative 32. Optimization of 32 afforded diastereomeric oxolan-3-yl derivatives 44 and 45, which demonstrated a favorable in vitro PK profile, although they displayed species disconnects in the in vivo PK profile, and a propensity for P-gp- and/or BCRP-mediated efflux in a mouse model. Compounds 44 and 45 demonstrated high potency and exquisite selectivity for LRRK2 and utility as chemical probes for the study of LRRK2 inhibition.

Modelling the binding mode of macrocycles: Docking and conformational sampling.

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Using Spin-Coated Silver Nanoparticles/Zinc Oxide Thin Films to Improve the Efficiency of GaInP/(In)GaAs/Ge Solar Cells.

We synthesized a silver nanoparticle/zinc oxide (Ag NP/ZnO) thin film by using spin-coating technology. The treatment solution for Ag NP/ZnO thin film deposition contained zinc acetate (Zn(CH3COO)2), sodium hydroxide (NaOH), and silver nitrate (AgNO3) aqueous solutions. The crystalline characteristics, surface morphology, content of elements, and reflectivity of the Ag NPs/ZnO thin film at various concentrations of the AgNO3 aqueous solution were investigated using X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, atomic force microscopy, and ultraviolet⁻visible⁻near infrared spectrophotometry. The results indicated that the crystalline structure, Ag content, and reflectance of Ag NP/ZnO thin films depended on the AgNO3 concentration. Hybrid antireflection coatings (ARCs) composed of SiNx and Ag NPs/ZnO thin films with various AgNO3 concentrations were deposited on GaInP/(In)GaAs/Ge solar cells. We propose that the optimal ARC consists of SiNx and Ag NP/ZnO thin films prepared using a treatment solution of 0.0008 M AgNO3, 0.007 M Zn(CH3COO)2, and 1 M NaOH, followed by post-annealing at 200 °C. GaInP/(Al)GaAs/Ge solar cells with the optimal hybrid ARC and SiNx ARC exhibit a conversion efficiency of 34.1% and 30.2% with Voc = 2.39 and 2.4 V, Jsc = 16.63 and 15.37 mA/cm², and fill factor = 86.1% and 78.8%.

Design of Leucine-Rich Repeat Kinase 2 (LRRK2) Inhibitors Using a Crystallographic Surrogate Derived from Checkpoint Kinase 1 (CHK1).

Mutations in leucine-rich repeat kinase 2 (LRRK2), such as G2019S, are associated with an increased risk of developing Parkinson's disease. Surrogates for the LRRK2 kinase domain based on checkpoint kinase 1 (CHK1) mutants were designed, expressed in insect cells infected with baculovirus, purified, and crystallized. X-ray structures of the surrogates complexed with known LRRK2 inhibitors rationalized compound potency and selectivity. The CHK1 10-point mutant was preferred, following assessment of surrogate binding affinity with LRRK2 inhibitors. Fragment hit-derived arylpyrrolo[2,3-b]pyridine LRRK2 inhibitors underwent structure-guided optimization using this crystallographic surrogate. LRRK2-pSer935 HEK293 IC50 data for 22 were consistent with binding to Ala2016 in LRRK2 (equivalent to Ala147 in CHK1 10-point mutant structure). Compound 22 was shown to be potent, moderately selective, orally available, and brain-penetrant in wild-type mice, and confirmation of target engagement was demonstrated, with LRRK2-pSer935 IC50 values for 22 in mouse brain and kidney being 1.3 and 5 nM, respectively.

The design and SAR of a novel series of 2-aminopyridine based LRRK2 inhibitors.

Leucine-rich repeat kinase 2 (LRRK2) has attracted considerable interest as a therapeutic target for the treatment of Parkinson's disease. Compounds derived from a 2-aminopyridine screening hit were optimised using a LRRK2 homology model based on mixed lineage kinase 1 (MLK1), such that a 2-aminopyridine-based lead molecule 45, with in vivo activity, was identified.

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.

Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.

Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The sampling of smaller drug-like compounds has been an active area of research. However, few studies have tested in details the sampling of larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, and inhibitors of protein-protein interactions. Here, we investigate extensively mainstream conformational sampling methods on three carefully curated compound sets, namely the 'Drug-like', larger 'Flexible', and 'Macrocycle' compounds. These test molecules are chemically diverse with reliable X-ray protein-bound bioactive structures. The compared sampling methods include Stochastic Search and the recent LowModeMD from MOE, all the low-mode based approaches from MacroModel, and MD/LLMOD recently developed for macrocycles. In addition to default settings, key parameters of the sampling protocols were explored. The performance of the computational protocols was assessed via (i) the reproduction of the X-ray bioactive structures, (ii) the size, coverage and diversity of the output conformational ensembles, (iii) the compactness/extendedness of the conformers, and (iv) the ability to locate the global energy minimum. The influence of the stochastic nature of the searches on the results was also examined. Much better results were obtained by adopting search parameters enhanced over the default settings, while maintaining computational tractability. In MOE, the recent LowModeMD emerged as the method of choice. Mixed torsional/low-mode from MacroModel performed as well as LowModeMD, and MD/LLMOD performed well for macrocycles. The low-mode based approaches yielded very encouraging results with the flexible and macrocycle sets. Thus, one can productively tackle the computational conformational search of larger flexible compounds for drug discovery, including macrocycles.

Drug-like bioactive structures and conformational coverage with the LigPrep/ConfGen suite: comparison to programs MOE and catalyst.

Computational conformational sampling underpins many aspects of small molecule modeling and design in pharmaceutical work. This work examined in detail the widely distributed LigPrep/ConfGen software suite and the conformational models it produces for drug-like compounds. We also compare LigPrep/ConfGen to MOE and Catalyst. Tests of the conformational sampling protocols included the reproduction of known bioactive structures of ligands, characterization of the size, coverage and diversity of the output conformational models, and relative computation times. The present tests will help the user to make informed choices among the predefined ConfGen protocols (Very fast, Fast, Intermediate, and Comprehensive), and the adjustable input parameters. The parameters governing the initial compound preparation (LigPrep) and the subsequent conformational sampling were explored. This analysis has led to a new protocol called "ConfGen Optimized", which improves upon the predefined protocols. ConfGen Optimized is computationally tractable and reproduced 80% of the bioactive structures within 1 A, versus 66% for the default ConfGen Fast protocol. We also addressed the issue of the reproduction of compact/folded bioactive structures by ConfGen. It involved the compilation of a new set of 50 folded diverse drug-like bioactive structures. This indicates that heuristics penalizing folded conformers hinder reproduction of some binding modes. Overall, ConfGen offers great flexibility of use and provides a valuable addition to the molecular modeling toolbox.

Conformational sampling and energetics of drug-like molecules.

The pharmacological properties of small organic molecules depend on their three-dimensional (3D) structure. That includes physico-chemical properties (e.g. solubility, partition equilibria) and molecular recognition such as binding to a therapeutic macromolecular target. At physiological temperature, the 3D structure of a flexible small molecules is expected to cover an ensemble of energetically accessible conformations. Therefore, it is of fundamental and practical importance to be able to relate the energetics of a molecule to its conformational preferences and derived properties, a discipline known as conformational analysis. The first step of conformational analysis is the generation of the conformers, referred to as conformational sampling. This is typically performed primarily using computational chemistry methods. Taking a fresh look at these methods for a broad medicinal chemistry audience is the object of the present review. Indeed, conformational sampling methods continue to be developed, improved and tested. They underpin much of the detailed analysis of structure-activity relationships on selected chemical series, but also the preparation of large conformational libraries of generic compounds and their exploitation for virtual screening. In recent years, the conformational models of active compounds have been examined to see how frequently they capture their target-bound bioactive conformation, as revealed by X-ray crystallography. This provided a context to scrutinize the intrinsic conformational energetics of these bioactive conformers, but this subject is still intensely debated. Another line of investigation concerns the conformational diversity of the 3D models, and how well they cover the conformational and pharmacophoric spaces. This review addresses in general terms: i) the basic principles of conformational analysis, including modern computational estimates of intramolecular energy and how those are mapped on the molecular potential energy surface, ii) some experimental contributions to probing of the small molecule conformations, iii) the various computational methods available to generate conformational models, iv) the conformational properties of the bioactive conformers, and v) attempts to quantify the coverage of the conformational models and the controlling parameters.

Lessons for fragment library design: analysis of output from multiple screening campaigns.

Over the past 8 years, we have developed, refined and applied a fragment based discovery approach to a range of protein targets. Here we report computational analyses of various aspects of our fragment library and the results obtained for fragment screening. We reinforce the finding of others that the experimentally observed hit rate for screening fragments can be related to a computationally defined druggability index for the target. In general, the physicochemical properties of the fragment hits display the same profile as the library, as is expected for a truly diverse library which probes the relevant chemical space. An analysis of the fragment hits against various protein classes has shown that the physicochemical properties of the fragments are complementary to the properties of the target binding site. The effectiveness of some fragments appears to be achieved by an appropriate mix of pharmacophore features and enhanced aromaticity, with hydrophobic interactions playing an important role. The analysis emphasizes that it is possible to identify small fragments that are specific for different binding sites. To conclude, we discuss how the results could inform further development and improvement of our fragment library.

Conformational sampling of druglike molecules with MOE and catalyst: implications for pharmacophore modeling and virtual screening.

Computational conformational sampling is integral to small molecule pharmaceutical research, for detailed conformational analysis and high-throughput 3D library enumeration. These two regimes were tested in details for the general-purpose modeling program MOE, using its three conformational sampling methods, i.e. systematic search, stochastic search, and Conformation Import. The tests include i) identification of the global energy minimum, ii) reproduction of the bioactive conformation, iii) measures of conformational coverage with 3D descriptors, and iv) compute times. The bioactive conformers are from a new set of 256 diverse, druglike, protein-bound ligands compiled and analyzed with particular care. The MOE results are compared to those obtained from the established program Catalyst. Key parameters controlling the conformational coverage were varied systematically. Coverage and diversity of the conformational space were characterized with unique pharmacophore triplets or quadruplets. Overall, the protocols in both MOE and Catalyst performed well for their intended tasks. MOE performed at least as well as Catalyst for high-throughput library generation and detailed conformational modeling. This work provides a guide and specific recommendations regarding the usage of conformational sampling tools in MOE.

Chemical substituent effect on pyridine permeability and mechanistic insight from computational molecular descriptors.

The objective was (1) to evaluate the chemical substituent effect on Caco-2 permeability, using a congeneric series of pyridines, and (2) compare molecular descriptors from a computational chemistry approach against molecular descriptors from the Hansch approach for their abilities to explain the chemical substituent effect on pyridine permeability. The passive permeability of parent pyridine and 14 monosubstituted pyridines were measured across Caco-2 monolayers. Computational chemistry analysis was used to obtain the following molecular descriptions: solvation free energies, solvent accessible surface area, polar surface area, and cavitation energy. Results indicate that the parent pyridine was highly permeable and that chemical substitution was able to reduce pyridine permeability almost 20-fold. The substituent effect on permeability provided the following rank order: 3-COO- < 4-NH2 < 3-CONH2 < 3-Cl < 3-CHO < 3-OH < 3-CH2OH < 3-C6H5 < 3-NH2 < 3-CH2C6H5 < 3-C2H5 < 3-H < 3-CH3 < 3-F < 4-C6H5. This substituent effect was better explained via molecule descriptors from the computational chemistry approach than explained by classic descriptors from Hansch. Computational descriptors indicate that aqueous desolvation, but not membrane partitioning per se, dictated substituent effect on permeability.

A structure-based strategy to identify new molecular scaffolds targeting the bacterial ribosomal A-site.

The need for novel antibiotics is widely recognized. A well validated target of antibiotics is the bacterial ribosome. Recent X-ray structures of the ribosome bound to antibiotics have shed new light on the binding sites of these antibiotics, providing fresh impetus for structure-based strategies aiming at identifying new ribosomal ligands. In that respect, the ribosomal decoding region of the aminoacyl-tRNA acceptor site (A-site) is of particular interest because oligonucleotide model systems of this site are available for crystallography, NMR and compound binding assays. This work presents how these different resources can be combined in a hierarchical screening strategy which has led to the identification of new A-site ligands. The approach exploits an X-ray structure of the A-site against which large and diverse libraries of compounds were computationally docked. The complementarity of the compounds to the A-site was assessed using a scoring function specifically calibrated for RNA targets. Starting from approximately 1 million compounds, the computational selection of candidate ligands allowed us to focus the experimental work on 129 compounds, 34 of which showed affinity for the A-site in a FRET-based binding assay. NMR experiments confirmed binding to the A-site for some compounds. For the most potent compound in the FRET assay, a tentative binding mode is suggested, which is compatible with the NMR data and the limited SAR in this series. Overall, the results validate the screening strategy.

Structure-based inhibitor design targeting HIV-1 integrase.

HIV integrase (IN) is a viral-encoded protein that catalyzes the breaking and joining reactions that mediate integration of viral DNA into the host genome. Therefore, IN offers a unique target for the development of novel anti-HIV and anti-AIDS therapeutics. To take advantage of this potential, drug discovery efforts via structure-based design approaches have been undertaken. Presented is a review of computer-aided drug design efforts targeting HIV IN. Included is an overview of the life-cycle of HIV, with emphasis on the mechanism of action of IN, biological assays for measuring IN activity and identifying IN inhibitors, and the appropriate cell-based assays required for determining the antiviral activity of IN inhibitors. This is followed by a review of the available three-dimensional structures of HIV IN. Structure-based drug design efforts are then critiqued, including both ligand-based (e.g. pharmacophore) and target-based (e.g. docking) methods. Results from recent computational chemistry studies of IN are also discussed.

Combined ab initio/empirical approach for optimization of Lennard-Jones parameters for polar-neutral compounds.

The study of small functionalized organic molecules in aqueous solution is a useful step toward gaining a basic understanding of the behavior of biomolecular systems in their native aqueous environment. Interest in studying amines and fluorine-substituted compounds has risen from their intrinsic physicochemical properties and their prevalence in biological and pharmaceutical compounds. In the present study, a previously developed approach which optimizes Lennard-Jones (LJ) parameters via the use of rare gas atoms combined with the reproduction of experimental condensed phase properties was extended to polar-neutral compounds. Compounds studied included four amines (ammonia, methylamine, dimethylamine, and trimethylamine) and three fluoroethanes (1-fluoroethane, 1,1-difluoroethane, and 1,1,1-trifluoroethane). The resulting force field yielded heats of vaporization and molecular volumes in excellent agreement with the experiment, with average differences less than 1%. The current amine CHARMM parameters successfully reproduced experimental aqueous solvation data where methylamine is more hydrophilic than ammonia, with hydrophobicity increasing with additional methylation on the nitrogen. For both the amines and fluoroethanes the parabolic relationship of the extent of methylation or fluorination, respectively, to the heats of vaporization were reproduced by the new parameters. The present results are also discussed with respect to the impact of parameterization approach to molecular details obtained from computer simulations and to the unique biological properties of fluorine in pharmaceutical compounds.