Conformational Analysis of Drug-Like Molecules Bound to Proteins: An Extensive Study of Ligand Reorganization upon Binding

Computational ligand-based rational design: Role of conformational sampling and force fields in model development

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Rigorous treatment of multispecies multimode ligand-receptor interactions in 3D-QSAR: CoMFA analysis of thyroxine analogs binding to transthyretin

For a rigorous analysis of the receptor-ligand binding, speciation of the ligands caused by ionization, tautomerism, covalent hydration, and dynamic stereoisomerism needs to be considered. Each species may bind in several orientations or conformations (modes), especially for flexible ligands and receptors. A thermodynamic description of the multispecies (MS), multimode (MM) binding events shows that the overall association constant is equal to the weighted sum of the sums of microscopic association constants of individual modes for each species, with the weights given by the unbound fractions of individual species. This expression is a prerequisite for a precise quantitative characterization of the ligand-receptor interactions in both structure-based and ligand-based structure-activity analyses. We have implemented the MS-MM correlation expression into the comparative molecular field analysis (CoMFA), which deduces a map of the binding site from structures and binding affinities of a ligand set, in the absence of experimental structural information on the receptor. The MS-MM CoMFA approach was applied to published data for binding to transthyretin of 28 thyroxine analogs, each forming up to four ionization species under physiological conditions. The published X-ray structures of several analogs, exhibiting multiple binding modes, served as templates for the MS-MM superposition of thyroxine analogs. Additional modes were generated for compounds with flexible alkyl substituents, to identify bound conformations. The results demonstrate that the MS-MM modification improved predictive abilities of the CoMFA models, even for the standard procedure with MS-MM selected species and modes. The predicted prevalences of individual modes and the generated receptor site model are in reasonable agreement with the available X-ray data. The calibrated model can help in the design of inhibitors of transthyretin amyloid fibril formation.

Advances in all atom sampling methods for modeling protein-ligand binding affinities

Conformational dynamics plays a fundamental role in the regulation of molecular recognition processes. Conformational heterogeneity and entropy variations upon binding, although not always evident from the analysis of structural data, can substantially affect affinity and specificity. Computer modeling is able to provide some of the most direct insights into these aspects of molecular recognition. We review recent physics-based computational studies that employ advanced conformational sampling algorithms and effective potentials to model the three main classes of degrees of freedom relevant to the binding process: ligand positioning relative to the receptor, ligand and receptor internal reorganization, and hydration. Collectively these studies show that all of these elements are important for proper modeling of protein-ligand interactions.

FRED pose prediction and virtual screening accuracy

Results of a previous docking study are reanalyzed and extended to include results from the docking program FRED and a detailed statistical analysis of both structure reproduction and virtual screening results. FRED is run both in a traditional docking mode and in a hybrid mode that makes use of the structure of a bound ligand in addition to the protein structure to screen molecules. This analysis shows that most docking programs are effective overall but highly inconsistent, tending to do well on one system and poorly on the next. Comparing methods, the difference in mean performance on DUD is found to be statistically significant (95% confidence) 61% of the time when using a global enrichment metric (AUC). Early enrichment metrics are found to have relatively poor statistical power, with 0.5% early enrichment only able to distinguish methods to 95% confidence 14% of the time.

Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method

BACKGROUND

The reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes.

RESULTS

We applied this method to examine the binding affinity for a series of published cyclin-dependent kinase 2 (CDK2) inhibitors. We calculated the binding affinity for 28 CDK2 inhibitors using the ab initio FMO method based on a number of X-ray crystal structures. The sum of the pair interaction energies (PIE) was calculated and used to explain the gas-phase enthalpic contribution to binding. The correlation of the ligand potencies to the protein-ligand interaction energies gained from FMO was examined and was seen to give a good correlation which outperformed three MM force field based scoring functions used to appoximate the free energy of binding. Although the FMO calculation allows for the enthalpic component of binding interactions to be understood at the quantum level, as it is an in vacuo single point calculation, the entropic component and solvation terms are neglected. For this reason a more accurate and predictive estimate for binding free energy was desired. Therefore, additional terms used to describe the protein-ligand interactions were then calculated to improve the correlation of the FMO derived values to experimental free energies of binding. These terms were used to account for the polar and non-polar solvation of the molecule estimated by the Poisson-Boltzmann equation and the solvent accessible surface area (SASA), respectively, as well as a correction term for ligand entropy. A quantitative structure-activity relationship (QSAR) model obtained by Partial Least Squares projection to latent structures (PLS) analysis of the ligand potencies and the calculated terms showed a strong correlation (r2 = 0.939, q2 = 0.896) for the 14 molecule test set which had a Pearson rank order correlation of 0.97. A training set of a further 14 molecules was well predicted (r2 = 0.842), and could be used to obtain meaningful estimations of the binding free energy.

CONCLUSIONS

Our results show that binding energies calculated with the FMO method correlate well with published data. Analysis of the terms used to derive the FMO energies adds greater understanding to the binding interactions than can be gained by MM methods. Combining this information with additional terms and creating a scaled model to describe the data results in more accurate predictions of ligand potencies than the absolute values obtained by FMO alone.

Pharmacophore-based virtual screening

This chapter is a review of the most recent developments in the field of pharmacophore modeling, covering both methodology and application. Pharmacophore-based virtual screening is nowadays a mature technology, very well accepted in the medicinal chemistry laboratory. Nevertheless, like any empirical approach, it has specific limitations and efforts to improve the methodology are still ongoing. Fundamentally, the core idea of "stripping" functional groups of their actual chemical nature in order to classify them into very few pharmacophore types, according to their dominant physico-chemical features, is both the main advantage and the main drawback of pharmacophore modeling. The advantage is the one of simplicity - the complex nature of noncovalent ligand binding interactions is rendered intuitive and comprehensible by the human mind. Although computers are much better suited for comparisons of pharmacophore patterns, a chemist's intuition is primarily scaffold-oriented. Its underlying simplifications render pharmacophore modeling unable to provide perfect predictions of ligand binding propensities - not even if all its subsisting technical problems would be solved. Each step in pharmacophore modeling and exploitation has specific drawbacks: from insufficient or inaccurate conformational sampling to ambiguities in pharmacophore typing (mainly due to uncertainty regarding the tautomeric/protonation status of compounds), to computer time limitations in complex molecular overlay calculations, and to the choice of inappropriate anchoring points in active sites when ligand cocrystals structures are not available. Yet, imperfections notwithstanding, the approach is accurate enough in order to be practically useful and actually is the most used virtual screening technique in medicinal chemistry - notably for "scaffold hopping" approaches, allowing the discovery of new chemical classes carriers of a desired biological activity.

Recent theoretical and computational advances for modeling protein-ligand binding affinities

We review recent theoretical and algorithmic advances for the modeling of protein ligand binding free energies. We first describe a statistical mechanics theory of noncovalent association, with particular focus on deriving the fundamental formulas on which computational methods are based. The second part reviews the main computational models and algorithms in current use or development, pointing out the relations with each other and with the theory developed in the first part. Particular emphasis is given to the modeling of conformational reorganization and entropic effect. The methods reviewed are free energy perturbation, double decoupling, the Binding Energy Distribution Analysis Method, the potential of mean force method, mining minima and MM/PBSA. These models have different features and limitations, and their ranges of applicability vary correspondingly. Yet their origins can all be traced back to a single fundamental theory.

Bioactive conformational generation of small molecules: a comparative analysis between force-field and multiple empirical criteria based methods

Background

Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.

Results

Our analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost.

Conclusions

By incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.

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.

ConfGen: a conformational search method for efficient generation of bioactive conformers

We describe the methodology, parametrization, and application of a conformational search method, called ConfGen, designed to efficiently generate bioactive conformers. We define efficiency as the ability to generate a bioactive conformation within a small total number of conformations using a reasonable amount of computer time. The method combines physics-based force field calculations with empirically derived heuristics designed to achieve efficient searching and prioritization of the ligand's conformational space. While many parameter settings are supported, four modes spanning a range of speed and quality trades-offs are defined and characterized. The validation set used to test the method is composed of ligands from 667 crystal structures covering a broad array of target and ligand classes. With the fastest mode, ConfGen uses an average of 0.5 s per ligand and generates only 14.3 conformers per ligand, at least one of which lies within 2.0 A root-mean-squared deviation of the crystal structure for 96% of the ligands. The most computationally intensive mode raises this recovery rate to 99%, while taking 8 s per ligand. Combining multiple search modes to "fill-in" holes in the conformation space or energy minimizing using an all-atom force field each lead to improvements in the recovery rates at higher resolutions. Overall, ConfGen is at least as good as competing programs at high resolution and demonstrates higher efficiency at resolutions sufficient for many downstream applications, such as pharmacophore modeling.

Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database

Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.

Molecular shape and medicinal chemistry: a perspective

The eight contributions here provide ample evidence that shape as a volume or as a surface is a vibrant and useful concept when applied to drug discovery. It provides a reliable scaffold for "decoration" with chemical intuition (or bias) for virtual screening and lead optimization but also has its unadorned uses, as in library design, ligand fitting, pose prediction, or active site description. Computing power has facilitated this evolution by allowing shape to be handled precisely without the need to reduce down to point descriptors or approximate metrics, and the diversity of resultant applications argues for this being an important step forward. Certainly, it is encouraging that as computation has enabled our intuition, molecular shape has consistently surprised us in its usefulness and adaptability. The first Aurelius question, "What is the essence of a thing?", seems well answered, however, the third, "What do molecules do?", only partly so. Are the topics covered here exhaustive, or is there more to come? To date, there has been little published on the use of the volumetric definition of shape described here as a QSAR variable, for instance, in the prediction or classification of activity, although other shape definitions have been successful applied, for instance, as embodied in the Compass program described above in "Shape from Surfaces". Crystal packing is a phenomenon much desired to be understood. Although powerful models have been applied to the problem, to what degree is this dominated purely by the shape of a molecule? The shape comparison described here is typically of a global nature, and yet some importance must surely be placed on partial shape matching, just as the substructure matching of chemical graphs has proved useful. The approach of using surfaces, as described here, offers some flavor of this, as does the use of metrics that penalize volume mismatch less than the Tanimoto, e.g., Tversky measures. As yet, there is little to go on as to how useful a paradigm this will be because there is less software and fewer concrete results.Finally, the distance between molecular shapes, or between any shapes defined as volumes or surfaces, is a metric property in the mathematical sense of the word. As yet, there has been little, if any, application of this observation. We cannot know what new application to the design and discovery of pharmaceuticals may yet arise from the simple concept of molecular shape, but it is fair to say that the progress so far is impressive.

Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble

Representing the 3D structures of ligands in virtual screenings via multi-conformer ensembles can be computationally intensive, especially for compounds with a large number of rotatable bonds. Thus, reducing the size of multi-conformer databases and the number of query conformers, while simultaneously reproducing the bioactive conformer with good accuracy, is of crucial interest. While clustering and RMSD filtering methods are employed in existing conformer generators, the novelty of this work is the inclusion of a clustering scheme (NMRCLUST) that does not require a user-defined cut-off value. This algorithm simultaneously optimizes the number and the average spread of the clusters. Here we describe and test four inter-dependent approaches for selecting computer-generated conformers, namely: OMEGA, NMRCLUST, RMS filtering and averaged-RMS filtering. The bioactive conformations of 65 selected ligands were extracted from the corresponding protein:ligand complexes from the Protein Data Bank, including eight ligands that adopted dissimilar bound conformations within different receptors. We show that NMRCLUST can be employed to further filter OMEGA-generated conformers while maintaining biological relevance of the ensemble. It was observed that NMRCLUST (containing on average 10 times fewer conformers per compound) performed nearly as well as OMEGA, and both outperformed RMS filtering and averaged-RMS filtering in terms of identifying the bioactive conformations with excellent and good matches (0.5 < RMSD < 1.0 A). Furthermore, we propose thresholds for OMEGA root-mean square filtering depending on the number of rotors in a compound: 0.8, 1.0 and 1.4 for structures with low (1-4), medium (5-9) and high (10-15) numbers of rotatable bonds, respectively. The protocol employed is general and can be applied to reduce the number of conformers in multi-conformer compound collections and alleviate the complexity of downstream data processing in virtual screening experiments.

Accounting for ligand conformational restriction in calculations of protein-ligand binding affinities

The conformation adopted by a ligand on binding to a receptor may differ from its lowest-energy conformation in solution. In addition, the bound ligand is more conformationally restricted, which is associated with a configurational entropy loss. The free energy change due to these effects is often neglected or treated crudely in current models for predicting binding affinity. We present a method for estimating this contribution, based on perturbation theory using the quasi-harmonic model of Karplus and Kushick as a reference system. The consistency of the method is checked for small model systems. Subsequently we use the method, along with an estimate for the enthalpic contribution due to ligand-receptor interactions, to calculate relative binding affinities. The AMBER force field and generalized Born implicit solvent model is used. Binding affinities were estimated for a test set of 233 protein-ligand complexes for which crystal structures and measured binding affinities are available. In most cases, the ligand conformation in the bound state was significantly different from the most favorable conformation in solution. In general, the correlation between measured and calculated ligand binding affinities including the free energy change due to ligand conformational change is comparable to or slightly better than that obtained by using an empirically-trained docking score. Both entropic and enthalpic contributions to this free energy change are significant.

Toward flexibility-activity relationships by NMR spectroscopy: dynamics of Pin1 ligands

Drug design involves iterative ligand modifications. For flexible ligands, these modifications often entail restricting conformational flexibility. However, defining optimal restriction strategies can be challenging if the relationship between ligand flexibility and biological activity is unclear. Here, we describe an approach for ligand flexibility-activity studies using Nuclear Magnetic Resonance (NMR) spin relaxation. Specifically, we use (13)C relaxation dispersion measurements to compare site-specific changes in ligand flexibility for a series of related ligands that bind a common macromolecular receptor. The flexibility changes reflect conformational reorganization resulting from formation of the receptor-ligand complex. We demonstrate this approach on three structurally similar but flexibly differentiated ligands of human Pin1, a peptidyl-prolyl isomerase. The approach is able to map the ligand dynamics relevant for activity and expose changes in those dynamics caused by conformational locking. Thus, NMR flexibility-activity studies can provide information to guide strategic ligand rigidification. As such, they help establish an experimental basis for developing flexibility-activity relationships (FAR) to complement traditional structure-activity relationships (SAR) in molecular design.

The discodermolide hairpin structure flows from conformationally stable modular motifs

(+)-Discodermolide (DDM), a polyketide macrolide from marine sponge, is a potent microtubule assembly promoter. Reported solid-state, solution, and protein-bound DDM conformations reveal the unusual result that a common hairpin conformational motif exists in all three microenvironments. No other flexible microtubule binding agent exhibits such constancy of conformation. In the present study, we combine force-field conformational searches with NMR deconvolution in different solvents to compare DDM conformers with those observed in other environments. While several conformational families are perceived, the hairpin form dominates. The stability of this motif is dictated primarily by steric factors arising from repeated modular segments in DDM composed of the C(Me)-CHX-C(Me) fragment. Furthermore, docking protocols were utilized to probe the DDM binding mode in beta-tubulin. A previously suggested pose is substantiated (Pose-1), while an alternative (Pose-2) has been identified. SAR analysis for DDM analogues differentiates the two poses and suggests that Pose-2 is better able to accommodate the biodata.

In silico screening for non-nucleoside HIV-1 reverse transcriptase inhibitors using physicochemical filters and high-throughput docking followed by in vitro evaluation

Reverse transcriptase, being the pivot in human immunodeficiency virus replication, is one of the most attractive targets for the development of new antiretroviral agents. We applied a virtual screening workflow based on a combination of physicochemical filters with high-throughput rigid molecular docking to discover novel efficient lead scaffolds for human immunodeficiency virus type 1 reverse transcriptase inhibition. In our protocol, different filters were employed to enrich the lead-likeness and improve the ligands efficiency of the filtered compounds. Out of the 238,819 compounds included in the National Cancer Institute database, 500 virtual screening hits were retrieved employing FILTER and FRED (molecular docking engine) softwares. Four compounds from the 20 highest ranking scored hits tested positive in human immunodeficiency virus type 1 reverse transcriptase using non-radioactive colorimetric assay method. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for human immunodeficiency virus type 1 reverse transcriptase inhibition that could be useful for drug development in the area of acquired immune-deficiency syndrome treatment.

Characterization of the conformational ensemble from bioactive N-acylhydrazone derivatives

The search for bioactive conformations from prototypes is mostly referenced on crystallographic ligand-receptor complexes, in which the molecule conformation is already caged inside its binding site. However, the complexation process is a thermodynamic event depending on both complexed and uncomplexed states. As ligand affinity originates from such equilibrium, the development of novel computational models capable of supplying data on ligand dynamics in biological solutions is potentially applicable in more efficient methods for prediction of compounds binding and affinity. In this context, the current work employs a series of molecular dynamics simulations on three N-acylhydrazone derivatives, already shown to present promising cardioinotropic and vasodilatory activities, in order to obtain a precise characterization of each compound conformational ensemble in aqueous solutions, instead of a single minimum energy conformation. Consequently, we were able to observe the influence of each functional group of the studied molecules on the conformation of the entire compounds and thus on the exposure of functional groups that might potentially bind to target receptors. Additionally, the differences between the molecules conformational behavior were characterized, supporting a spatial and temporal image of each ligand, which may be potentially correlated to their biological activities. So in the context of conformational selection, such strategy may represent a useful methodology to contribute in the choice of ligands conformations for both 3D-QSAR and docking calculations.

Identification and characterisation of new inhibitors for the human hematopoietic prostaglandin D2 synthase

Prostaglandin D(2) synthesised by the hematopoietic prostaglandin D(2) synthase has a pro-inflammatory effect in allergic asthma, regulating many hallmark characteristics of the disease. Here we describe identification of hematopoietic prostaglandin D(2) synthase inhibitors including cibacron blue, bromosulfophthalein and ethacrynic acid. Expansion around the drug-like ethacrynic acid identified a novel inhibitor, nocodazole, and a fragment representing its aromatic core. Nocodazole binding was further characterised by docking calculations in combination with conformational strain analysis. The benzyl thiophene core was predicted to be buried in the active site, binding in the putative prostaglandin binding site, and a likely hydrogen bond donor site identified. X-ray crystallographic studies supported the predicted binding mode.

Prediction of the water content in protein binding sites

An efficient molecular simulation methodology has been developed to determine the positioning of water molecules in the binding site of a protein or protein-ligand complex. Occupancies and absolute binding free energies of water molecules are computed using a statistical thermodynamics approach. The methodology, referred to as Just Add Water Molecules (JAWS), features "theta-water" molecules that can appear and disappear on a binding-site grid. Key approximations render the technique far more efficient than conventional free energy simulations. Testing of JAWS on five diverse examples (neuraminidase, scytalone dehydratase, major urinary protein 1, beta-lactoglobulin, and COX-2) demonstrates its accuracy in locating hydration sites in comparison to results from high-resolution crystal structures. Possible applications include aid in refinement of protein crystal structures, drug lead optimization, setup of docking calculations, and simulations of protein-ligand complexes.

Mapping the dynamics of ligand reorganization via 13CH3 and 13CH2 relaxation dispersion at natural abundance

Flexible ligands pose challenges to standard structure-activity studies since they frequently reorganize their conformations upon protein binding and catalysis. Here, we demonstrate the utility of side chain (13)C relaxation dispersion measurements to identify and quantify the conformational dynamics that drive this reorganization. The dispersion measurements probe methylene (13)CH(2) and methyl (13)CH(3) groups; the latter are highly prevalent side chain moieties in known drugs. Combining these side chain studies with existing backbone dispersion studies enables a comprehensive investigation of mus-ms conformational dynamics related to binding and catalysis. We perform these measurements at natural (13)C abundance, in congruence with common pharmaceutical research settings. We illustrate these methods through a study of the interaction of a phosphopeptide ligand with the peptidyl-prolyl isomerase, Pin1. The results illuminate the side-chain moieties that undergo conformational readjustments upon complex formation. In particular, we find evidence that multiple exchange processes influence the side chain dispersion profiles. Collectively, our studies illustrate how side-chain relaxation dispersion can shed light on ligand conformational transitions required for activity, and thereby suggest strategies for its optimization.

A synthetic protein selected for ligand binding affinity mediates ATP hydrolysis

How primitive enzymes emerged from a primordial pool remains a fundamental unanswered question with important practical implications in synthetic biology. Here we show that a de novo evolved ATP binding protein, selected solely on the basis of its ability to bind ATP, mediates the regiospecific hydrolysis of ATP to ADP when crystallized with 1 equiv of ATP. Structural insights into this reaction were obtained by growing protein crystals under saturating ATP conditions. The resulting crystal structure refined to 1.8 A resolution reveals that this man-made protein binds ATP in an unusual bent conformation that is metal-independent and held in place by a key bridging water molecule. Removal of this interaction using a null mutant results in a variant that binds ATP in a normal linear geometry and is incapable of ATP hydrolysis. Biochemical analysis, including high-resolution mass spectrometry performed on dissolved protein crystals, confirms that the reaction is accelerated in the crystalline environment. This observation suggests that proteins with weak chemical reactivity can emerge from high affinity ligand binding sites and that constrained ligand-binding geometries could have helped to facilitate the emergence of early protein enzymes.

Novel method for the evaluation of 3D conformation generators

Conformation generation is a common and key process of computer-aided drug design. The reliability of the docking simulations, pharmacophore development, and 3D-QSAR analyses depends on the accuracy of conformations of small molecules used as input information for each program. Many conformation generators have been developed with the aim of efficiently generating all the putative bound conformations that small molecules adopt when they interact with macromolecules. Conformation generators have been evaluated by whether they can reproduce the experimentally determined bioactive conformations of bound small molecules. These bioactive conformations are usually obtained from publicly available crystal structures of protein-ligand complexes. However, it is difficult to obtain 2 or more than 2 bioactive conformations of one compound because multiple complex structures of a single molecule with various macromolecules are rarely available. Present methods, therefore, simply check whether a set of generated conformations includes the corresponding bioactive conformation. The overall validity of the entire set of generated conformations against bioactive conformation space has never been checked. In this work we developed a novel method for the evaluation of conformation generators, which makes it possible to measure the performance of a conformation generator based on its ability to reproduce the overall bioactive conformation space. We also determined the optimum parameter sets for OMEGA (OpenEye) based on the coverage of bioactive conformation space and computational efficiency. Our evaluation method elucidated that increasing the number of generated conformations is not necessary to obtain better reproducibility of the overall bioactive conformation space. Our method can be applied to the evaluation of the algorithm and/or design of the conformation generator program itself.

Conformational variability of benzamidinium-based inhibitors

Determining the structure of a small molecule bound to a biological receptor (e.g., a protein implicated in a disease state) is a necessary step in structure-based drug design. The preferred conformation of a small molecule can change when bound to a protein, and a detailed knowledge of the preferred conformation(s) of a bound ligand can help in optimizing the affinity of a molecule for its receptor. However, the quality of a protein/ligand complex determined using X-ray crystallography is dependent on the size of the protein, the crystal quality, and the realized resolution. The energy restraints used in traditional X-ray refinement procedures typically use "reduced" (i.e., neglect of electrostatics and dispersion interactions) Engh and Huber force field models that, while quite suitable for modeling proteins, often are less suitable for small molecule structures due to a lack of validated parameters. Through the use of ab initio QM/MM-based X-ray refinement procedures, this shortcoming can be overcome especially in the active site or binding site of a small-molecule inhibitor. Herein, we demonstrate that ab initio QM/MM refinement of an inhibitor/protein complex provides insights into the binding of small molecules beyond what is available using more traditional refinement protocols. In particular, QM/MM refinement studies of benzamidinium derivatives show variable conformational preferences depending on the refinement protocol used and the nature of the active-site region.

Hybrid structure-based virtual screening protocol for the identification of novel BACE1 inhibitors

BACE1, also called beta-secretase or memapsin 2, is an extensively studied aspartic protease, involved in etiopathogenesis and progression of Alzheimer's disease (AD). We report herein a modified structure-based virtual screening protocol that augments the lead identification process against BACE1 during virtual screening endeavors. A hybrid structure-based virtual screening protocol that incorporates elements from both ligand-based and structure-based techniques was used for the identification of prospective small molecule inhibitors. Virtual screening, using an active-site-derived pharmacophore, followed by ROCS (rapid overlay of chemical structures)-based GOLD (genetic optimization in ligand docking) docking was used to identify a library of focused candidates. The efficacy of the ROCS-based GOLD docking method together with our customized weighted consensus scoring function was evaluated against conventional docking methods for its ability to discern true positives from a screening library. An in-depth structural analysis of the binding mode of the top-ranking molecules reveals that emulation of the curial interaction patterns deemed necessary for BACE1 inhibition. The results obtained from our validation study ensure the superiority of our docking methodology over conventional docking methods in yielding higher enrichment rates.

Binding of small-molecule ligands to proteins: "what you see" is not always "what you get"

We review insights from computational studies of affinities of ligands binding to proteins. The power of structural biology is in translating knowledge of protein structures into insights about their forces, binding, and mechanisms. However, the complementary power of computer modeling is in showing "the rest of the story" (i.e., how motions and ensembles and alternative conformers and the entropies and forces that cannot be seen in single molecular structures also contribute to binding affinities). Upon binding to a protein, a ligand can bind in multiple orientations; the protein or ligand can be deformed by the binding event; waters, ions, or cofactors can have unexpected involvement; and conformational or solvation entropies can sometimes play large and otherwise unpredictable roles. Computer modeling is helping to elucidate these factors.

Potent and selective alpha1A adrenoceptor partial agonists--novel imidazole frameworks

Novel imidazole frameworks have been identified as potent partial agonists of the alpha(1A) adrenergic receptor, with good selectivity over the alpha(1B), alpha(1D) and alpha(2A) receptor sub-types. Nitrile 28 possessed attractive CNS drug-like properties with good membrane permeability and no P-pg mediated efflux. 28 also possessed excellent solubility, metabolic stability and wide ligand selectivity.

Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation

BACKGROUND

Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm.

RESULTS

The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1.

CONCLUSION

On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of reproducing the bioactive conformations against 329 structures. The speed advantage indicates Cyndi is a powerful alternative method for extensive conformational sampling and large-scale conformer database preparation.