How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis

Better treatment of protein flexibility is essential in structure-based drug design projects such as virtual screening and protein-ligand docking. Diversity in ligand-binding mechanisms and receptor conformational changes makes it difficult to treat dynamic features of the receptor during the docking simulation. Thus, the use of pregenerated multiple receptor conformations is applied today in virtual screening studies. However, generation of a small relevant set of receptor conformations remains challenging. To address this problem, we propose a new protocol for the generation of multiple receptor conformations via normal mode analysis and for the selection of several receptor conformations suitable for docking/virtual screening. We validated this protocol on cyclin-dependent kinase 2, which possesses a binding site located at the interface between two subdomains and is known to undergo significant conformational changes in the active site region upon ligand binding. We believe that the suggested rules for the choice of suitable receptor conformations can be applied to other targets when dealing with in silico screening on flexible receptors.     

Applying conformational selection theory to improve crossdocking efficiency in 3‐phosphoinositide dependent protein kinase‐1

The emerging picture of biomolecular recognition is that of conformational selection followed by induced‐fit. Conformational selection theory states that binding partners exist in various conformations in solution, with binding involving a “selection” between complementary conformers. In this study, we devise a docking protocol that mimics conformational selection in protein–ligand binding and demonstrate that it significantly enhances crossdocking accuracy over Glide's flexible docking protocol, which is widely used in the pharmaceutical industry. Our protocol uses a pregenerated conformational ensemble to simulate ligand flexibility. The ensemble was generated by thorough conformational sampling coupled with conformer minimization. The generated conformers were then rigidly docked in the active site of the protein along with a postdocking minimization step that allows limited induced fit effects to be modeled for the ligand. We illustrate the improved performance of our protocol through crossdocking of 31 ligands to cocomplexed proteins of the kinase 3‐phosphoinositide dependent protein kinase‐1 extracted from the crystal structures 1H1W (ATP bound), 1OKY (staurosporine bound) and 3QD0 (bound to a potent inhibitor). Consistent with conformational selection theory, the performance of our protocol was the best for crossdocking to the cognate protein bound to the natural ligand, ATP. Proteins 2014; 82:436–451. © 2013 Wiley Periodicals, Inc.     

Representation of target-bound drugs by computed conformers: implications for conformational libraries

Background  

The increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.     

3D-QSAR and docking studies of aldehyde inhibitors of human cathepsin K

In order to better understand the structural and chemical features of human cathepsin K (CatK), which is an important cysteine protease in the pathogenesis of osteoporosis, the 3D-QSAR (CoMFA) studies were conducted on recently explored aldehyde compounds with known CatK inhibitory activities. The genetic algorithm of GOLD2.2 has been employed to position 59 aldehyde compounds into the active sites of CatK to determine the probable binding conformation. Good correlations between the predicted binding free energies and the experimental inhibitory activities suggested that the identified binding conformations of these potential inhibitors are reliable. The docking results also provided a reliable conformational alignment scheme for 3D-QSAR model. Based on the docking conformations, highly predictive comparative molecular field analysis (CoMFA) was performed with q2 value of 0.723. The predictive ability was validated by some compounds that were not included in the training set. Furthermore, the CoMFA model was mapped back to the binding sites of CatK, to get a better understanding of vital interactions between the aldehyde compounds and the protease. The CoMFA field distributions are in good agreement with the structural characteristics of the binding groove of the CatK, which suggested that the n-Bu in R4 position is the favor group substitute at P1 and moderate groups in R2 group are required on P2 substitute. In addition, 3D-QSAR results also demonstrated that aldehyde is an important pharmacophore because of electrostatic effect. These results, together with the good correlations between the inhibitory activities and the binding free energies predicted by GOLD2.2, demonstrated the power of combining docking/QSAR approach to explore the probable binding conformations of compounds at the active sites of the protein target, and further provided useful information in understanding the structural and chemical features of CatK in designing and finding new potential inhibitors.     

Systematic exploitation of multiple receptor conformations for virtual ligand screening

The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario.     

Adaptability in protein structures: structural dynamics and implications in ligand design

The basic framework of understanding the mechanisms of protein functions is achieved from the knowledge of their structures which can model the molecular recognition. Recent advancement in the structural biology has revealed that in spite of the availability of the structural data, it is nontrivial to predict the mechanism of the molecular recognition which progresses via situation-dependent structural adaptation. The mutual selectivity of protein–protein and protein–ligand interactions often depends on the modulations of conformations empowered by their inherent flexibility, which in turn regulates the function. The mechanism of a protein’s function, which used to be explained by the ideas of ‘lock and key’ has evolved today as the concept of ‘induced fit’ as well as the ‘population shift’ models. It is felt that the ‘dynamics’ is an essential feature to take into account for understanding the mechanism of protein’s function. The design principles of therapeutic molecules suffer from the problems of plasticity of the receptors whose binding conformations are accurately not predictable from the prior knowledge of a template structure. On the other hand, flexibility of the receptors provides the opportunity to improve the binding affinity of a ligand by suitable substitution that will maximize the binding by modulating the receptors surface. In this paper, we discuss with example how the protein’s flexibility is correlated with its functions in various systems, revealing the importance of its understanding and for making applications. We also highlight the methodological challenges to investigate it computationally and to account for the flexible nature of the molecules in drug design.     

Improved 3D-QSAR CoMFA of the dopamine transporter blockers with multiple conformations using the genetic algorithm

A 3D-QSAR/CoMFA was performed for a series of 42 piperidine-based dopamine transporter (DAT) blockers. The overall process consisted of three major steps: (1) a pharmacophore model was built using the Genetic Algorithm Similarity Program (GASP); (2) the Flexible Superposition (FlexS) technique was applied to generate multiple conformations for each of the ligands based on the pharmacophore; (3) the Genetic Algorithm was employed to optimize the selection of the ligand conformations for the CoMFA modeling. The CoMFA models were found to be more detailed in the putative binding site by exploring multiple conformations of each ligand. The comparison of the contour maps shows that, in general, these models are comparable and the differences between them result from the ability of the flexible 3-substituents of the ligands to adopt multiple conformations satisfying the same pharmacophore model. These findings provide guidance for the design and improvement of compounds with DAT activity, which is important for the development of a treatment of cocaine addiction and certain neurological disorders.     

Structural insights on identification of potential lead compounds targeting WbpP in Vibrio vulnificus through structure-based approaches

WbpP encoding UDP-GlcNAC C4 epimerase is responsible for the activation of virulence factor in marine pathogen Vibrio vulnificus (V. vulnificus) and it is linked to many aquatic diseases, thus making it a potential therapeutic target. There are few reported compounds that include several natural products and synthetic compounds targeting Vibrio sp, but specific inhibitor targeting WbpP are unavailable. Here, we performed structure-based virtual screening using chemical libraries such as Binding, TOSLab and Maybridge to identify small molecule inhibitors of WbpP with better drug-like properties. Deficient structural information forced to model the structure and the stable protein structure was obtained through 30?ns of MD simulations. Druggability regions are focused for new lead compounds and our screening protocol provides fast docking of entire small molecule library with screening criteria of ADME/Lipinski filter/Docking followed by re-docking of top hits using a method that incorporates both ligand and protein flexibility. Docking conformations of lead molecules interface displays strong H-bond interactions with the key residues Gly101, Ser102, Val195, Tyr165, Arg298, Val209, Ser142, Arg233 and Gln200. Subsequently, the top-ranking compounds were prioritized using the molecular dynamics simulation-based conformation and stability studies. Our study suggests that the proposed compounds may aid as a starting point for the rational design of novel therapeutic agents.     

Chemical,Target, and Bioactive Properties of Allosteric Modulation

Allosteric modulators are ligands for proteins that exert their effects via a different binding site than the natural (orthosteric) ligand site and hence form a conceptually distinct class of ligands for a target of interest. Here, the physicochemical and structural features of a large set of allosteric and non-allosteric ligands from the ChEMBL database of bioactive molecules are analyzed. In general allosteric modulators are relatively smaller, more lipophilic and more rigid compounds, though large differences exist between different targets and target classes. Furthermore, there are differences in the distribution of targets that bind these allosteric modulators. Allosteric modulators are over-represented in membrane receptors, ligand-gated ion channels and nuclear receptor targets, but are underrepresented in enzymes (primarily proteases and kinases). Moreover, allosteric modulators tend to bind to their targets with a slightly lower potency (5.96 log units versus 6.66 log units, p<0.01). However, this lower absolute affinity is compensated by their lower molecular weight and more lipophilic nature, leading to similar binding efficiency and surface efficiency indices. Subsequently a series of classifier models are trained, initially target class independent models followed by finer-grained target (architecture/functional class) based models using the target hierarchy of the ChEMBL database. Applications of these insights include the selection of likely allosteric modulators from existing compound collections, the design of novel chemical libraries biased towards allosteric regulators and the selection of targets potentially likely to yield allosteric modulators on screening. All data sets used in the paper are available for download.     

Protein affinity map of chemical space

Affinity fingerprinting is a quantitative method for mapping chemical space based on binding preferences of compounds for a reference panel of proteins. An effective reference panel of <20 proteins can be empirically selected which shows differential interaction with nearly all compounds. By using this map to iteratively sample the chemical space, identification of active ligands from a library of 30 000 candidate compounds has been accomplished for a wide spectrum of specific protein targets. In each case, <200 compounds were directly assayed against the target. Further, analysis of the fingerprint database suggests a strategy for effective selection of affinity chromatography ligands and scaffolds for combinatorial chemistry. With such a system, the large numbers of potential therapeutic targets emerging from genome research can be categorized according to ligand binding properties, complementing sequence based classification.     

Towards in silico lead optimization: scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Accurately ranking protein/ligand interactions and distinguishing subtle differences between homologous compounds in a virtual focused library in silico is essential in a structure-based drug discovery program. In order to establish a predictive model to design novel inhibitors of dihydrofolate reductase (DHFR) from the parasitic protozoa, Cryptosporidium hominis, we docked a series of 30 DHFR inhibitors with measured inhibition constants against the crystal structure of the protein. By including protein flexibility and averaging the energies of the 25 lowest protein/ligand conformers we obtained more accurate total nonbonded energies from which we calculated a predicted biological activity. The calculated and measured biological activities showed reliable correlations of 72.9%. Additionally, visual analysis of the ensemble of protein/ligand conformations revealed alternative ligand binding pockets in the active site. Using the same principles we then created a homology model of DHFR from Toxoplasma gondii and docked 11 inhibitors. A correlation of 50.2% between docking score and activity validates both the method and the model. The correlations presented here are particularly compelling considering the high structural similarity of the ligands and the fact that we have used structures derived from crystallographic data and homology modeling. These docking principles may be useful in any lead optimization study where accurate ranking of similar compounds is desired.     

Improved reliability,accuracy and quality in automated NMR structure calculation with ARIA

In biological NMR, assignment of NOE cross-peaks and calculation of atomic conformations are critical steps in the determination of reliable high-resolution structures. ARIA is an automated approach that performs NOE assignment and structure calculation in a concomitant manner in an iterative procedure. The log-harmonic shape for distance restraint potential and the Bayesian weighting of distance restraints, recently introduced in ARIA, were shown to significantly improve the quality and the accuracy of determined structures. In this paper, we propose two modifications of the ARIA protocol: (1) the softening of the force field together with adapted hydrogen radii, which is meaningful in the context of the log-harmonic potential with Bayesian weighting, (2) a procedure that automatically adjusts the violation tolerance used in the selection of active restraints, based on the fitting of the structure to the input data sets. The new ARIA protocols were fine-tuned on a set of eight protein targets from the CASD–NMR initiative. As a result, the convergence problems previously observed for some targets was resolved and the obtained structures exhibited better quality. In addition, the new ARIA protocols were applied for the structure calculation of ten new CASD–NMR targets in a blind fashion, i.e. without knowing the actual solution. Even though optimisation of parameters and pre-filtering of unrefined NOE peak lists were necessary for half of the targets, ARIA consistently and reliably determined very precise and highly accurate structures for all cases. In the context of integrative structural biology, an increasing number of experimental methods are used that produce distance data for the determination of 3D structures of macromolecules, stressing the importance of methods that successfully make use of ambiguous and noisy distance data.     

Sampling-based exploration of folded state of a protein under kinematic and geometric constraints

Flexibility is critical for a folded protein to bind to other molecules (ligands) and achieve its functions. The conformational selection theory suggests that a folded protein deforms continuously and its ligand selects the most favorable conformations to bind to. Therefore, one of the best options to study protein-ligand binding is to sample conformations broadly distributed over the protein-folded state. This article presents a new sampler, called kino-geometric sampler (KGS). This sampler encodes dominant energy terms implicitly by simple kinematic and geometric constraints. Two key technical contributions of KGS are (1) a robotics-inspired Jacobian-based method to simultaneously deform a large number of interdependent kinematic cycles without any significant break-up of the closure constraints, and (2) a diffusive strategy to generate conformation distributions that diffuse quickly throughout the protein folded state. Experiments on four very different test proteins demonstrate that KGS can efficiently compute distributions containing conformations close to target (e.g., functional) conformations. These targets are not given to KGS, hence are not used to bias the sampling process. In particular, for a lysine-binding protein, KGS was able to sample conformations in both the intermediate and functional states without the ligand, while previous work using molecular dynamics simulation had required the ligand to be taken into account in the potential function. Overall, KGS demonstrates that kino-geometric constraints characterize the folded subset of a protein conformation space and that this subset is small enough to be approximated by a relatively small distribution of conformations.     

NMR spectroscopic and molecular modeling studies of protein-carbohydrate and protein-peptide interactions

Investigations of the conformations of carbohydrates, their analogues and their molecular mimics are described, with emphasis on structural and functional information that can be gained by NMR spectroscopic techniques in combination with molecular modeling. The transferred nuclear Overhauser effect (trNOE) has been employed to determine the bound conformations of carbohydrates and other bioactive molecules in complex with protein receptors. The corresponding experiments in the rotating frame (trROE) and selective editing experiments (e.g., QUIET-NOESY) are used to eliminate indirect cross-relaxation pathways (spin diffusion), thereby minimizing errors in the data used for calculation of conformations. Saturation transfer difference NMR experiments reveal detailed information about intermolecular contacts between ligand and protein. Computational techniques are integrated with NMR-derived information to construct structural models of these bioactive molecules and of their complexes with proteins. Recent investigations into the nature of molecular mimicry with regard to protein-ligand interactions are described, along with applications in determining the mode of action of enzyme inhibitors. The results are relevant for the design of the next generation of drug and vaccine candidates.     

Computer design of bioactive molecules: a method for receptor-based de novo ligand design.

The design of molecules to bind specifically to protein receptors has long been a goal of computer-assisted molecular design. Given detailed structural knowledge of the target receptor, it should be possible to construct a model of a potential ligand, by algorithmic connection of small molecular fragments, that will exhibit the desired structural and electrostatic complementarity with the receptor. However, progress in this area of receptor-based, de novo ligand design has been hampered by the complexity of the construction process, in which potentially huge numbers of structures must be considered. By limiting the scope of the structure-space examined to one particular class of ligands--namely, peptides and peptide-like compounds--the problem complexity has been reduced to the point that successful, de novo design is now possible. The methodology presented employs a large template set of amino acid conformations which are iteratively pieced together in a model of the target receptor. Each stage of ligand growth is evaluated according to a molecular mechanics-based energy function, which considers van der Waals and coulombic interactions, internal strain energy of the lengthening ligand, and desolvation of both ligand and receptor. The search space is managed by use of a data tree which is kept under control by pruning according to the energy evaluation. Ligands grown by this procedure are subjected to follow-up evaluation in which an approximate binding enthalpy is determined. This methodology has proven useful as a precise model-builder and has also shown the ability to design bioactive ligands.