Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures

Incorporating receptor flexibility is considered crucial for improvement of docking-based virtual screening. With an abundance of crystallographic structures freely available, docking with multiple crystal structures is believed to be a practical approach to cope with protein flexibility. Here we describe a successful application of the docking of multiple structures to discover novel and potent Chk1 inhibitors. Forty-six Chk1 structures were first compared in single structure docking by predicting the binding mode and recovering known ligands. Combinations of different protein structures were then compared by recovery of known ligands and an optimal ensemble of Chk1 structures were selected. The chosen structures were used in the virtual screening of over 60?000 diverse compounds for Chk1 inhibitors. Six novel compounds ranked at the top of the hits list were tested experimentally, and two of these compounds inhibited Chk1 activity-the best with an IC(50) value of 9.6 μM. Further study indicated that achieving a better enrichment and identifying more diverse compounds was more likely using multiple structures than using only a single structure even when protein structures were randomly selected. Taking into account conformational energy difference did not help to improve enrichment in the top ranked list.     

Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring

Docking and scoring are critical issues in virtual drug screening methods. Fast and reliable methods are required for the prediction of binding affinity especially when applied to a large library of compounds. The implementation of receptor flexibility and refinement of scoring functions for this purpose are extremely challenging in terms of computational speed. Here we propose a knowledge-based multiple-conformation docking method that efficiently accommodates receptor flexibility thus permitting reliable virtual screening of large compound libraries. Starting with a small number of active compounds, a preliminary docking operation is conducted on a large ensemble of receptor conformations to select the minimal subset of receptor conformations that provides a strong correlation between the experimental binding affinity (e.g., Ki, IC50) and the docking score. Only this subset is used for subsequent multiple-conformation docking of the entire data set of library (test) compounds. In conjunction with the multiple-conformation docking procedure, a two-step scoring scheme is employed by which the optimal scoring geometries obtained from the multiple-conformation docking are re-scored by a molecular mechanics energy function including desolvation terms. To demonstrate the feasibility of this approach, we applied this integrated approach to the estrogen receptor alpha (ERalpha) system for which published binding affinity data were available for a series of structurally diverse chemicals. The statistical correlation between docking scores and experimental values was significantly improved from those of single-conformation dockings. This approach led to substantial enrichment of the virtual screening conducted on mixtures of active and inactive ERalpha compounds.     

Validation of the AmpC β-lactamase binding site and identification of inhibitors with novel scaffolds

AmpC β-lactamase confers resistance to β-lactam antibiotics in multiple Gram-negative bacteria. Therefore, identification of non-β-lactam compounds that inhibit the enzyme is considered crucial to the development of novel antibacterial therapies. Given the highly solvent-exposed active site, it is important to study the induced-fit movements and water-mediated interactions to improve docking accuracy and virtual screening enrichments in structure-based design of new AmpC inhibitors. Here, we tested multiple models of the AmpC binding site to investigate the importance of conserved water molecules and binding site plasticity on molecular docking. The results indicate that at least one conserved water molecule greatly improves the binding pose predictions and virtual screening enrichments of known noncovalent AmpC inhibitors. The best model was tested prospectively in the virtual screening of about 6 million commercially available compounds. Sixty-one chemically diverse top-scoring compounds were experimentally tested, which led to the identification of seven previously unknown inhibitors. These findings validate the essential features of the AmpC binding site for molecular recognition and are useful for further optimization of identified inhibitors.     

A virtual screen for diverse ligands: discovery of selective G protein-coupled receptor antagonists

Virtual screening has become a major focus of bioactive small molecule lead identification, and reports of agonists and antagonists discovered via virtual methods are becoming more frequent. G protein-coupled receptors (GPCRs) are the one class of protein targets for which success with this approach has been limited. This is likely due to the paucity of detailed experimental information describing GPCR structure and the intrinsic function-associated structural flexibility of GPCRs which present major challenges in the application of receptor-based virtual screening. Here we describe an in silico methodology that diminishes the effects of structural uncertainty, allowing for more inclusive representation of a potential docking interaction with exogenous ligands. Using this approach, we screened one million compounds from a virtual database, and a diverse subgroup of 100 compounds was selected, leading to experimental identification of five structurally diverse antagonists of the thyrotropin-releasing hormone receptors (TRH-R1 and TRH-R2). The chirality of the most potent chemotype was demonstrated to be important in its binding affinity to TRH receptors; the most potent stereoisomer was noted to have a 13-fold selectivity for TRH-R1 over TRH-R2. A comprehensive mutational analysis of key amino acid residues that form the putative binding pocket of TRH receptors further verified the binding modality of these small molecule antagonists. The described virtual screening approach may prove applicable in the search for novel small molecule agonists and antagonists of other GPCRs.     

Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets

Virtual screening by molecular docking has become a widely used approach to lead discovery in the pharmaceutical industry when a high-resolution structure of the biological target of interest is available. The performance of three widely used docking programs (Glide, GOLD, and DOCK) for virtual database screening is studied when they are applied to the same protein target and ligand set. Comparisons of the docking programs and scoring functions using a large and diverse data set of pharmaceutically interesting targets and active compounds are carried out. We focus on the problem of docking and scoring flexible compounds which are sterically capable of docking into a rigid conformation of the receptor. The Glide XP methodology is shown to consistently yield enrichments superior to the two alternative methods, while GOLD outperforms DOCK on average. The study also shows that docking into multiple receptor structures can decrease the docking error in screening a diverse set of active compounds.     

Design of new plasmepsin inhibitors: a virtual high throughput screening approach on the EGEE grid

Though different species of the genus Plasmodium may be responsible for malaria, the variant caused by P. falciparum is often very dangerous and even fatal if untreated. Hemoglobin degradation is one of the key metabolic processes for the survival of the Plasmodium parasite in its host. Plasmepsins, a family of aspartic proteases encoded by the Plasmodium genome, play a prominent role in host hemoglobin cleavage. In this paper we demonstrate the use of virtual screening, in particular molecular docking, employed at a very large scale to identify novel inhibitors for plasmepsins II and IV. A large grid infrastructure, the EGEE grid, was used to address the problem of large computation resources required for docking hundreds of thousands of chemical compounds on different plasmepsin targets of P. falciparum. A large compound library of about 1 million chemical compounds was docked on 5 different targets of plasmepsins using two different docking software, namely FlexX and AutoDock. Several strategies were employed to analyze the results of this virtual screening approach including docking scores, ideal binding modes, and interactions to key residues of the protein. Three different classes of structures with thiourea, diphenylurea, and guanidino scaffolds were identified to be promising hits. While the identification of diphenylurea compounds is in accordance with the literature and thus provides a sort of "positive control", the identification of novel compounds with a guanidino scaffold proves that high throughput docking can be effectively used to identify novel potential inhibitors of P. falciparum plasmepsins. Thus, with the work presented here, we do not only demonstrate the relevance of computational grids in drug discovery but also identify several promising small molecules which have the potential to serve as candidate inhibitors for P. falciparum plasmepsins. With the use of the EGEE grid infrastructure for the virtual screening campaign against the malaria causing parasite P. falciparum we have demonstrated that resource sharing on an eScience infrastructure such as EGEE provides a new model for doing collaborative research to fight diseases of the poor.     

Representing receptor flexibility in ligand docking through relevant normal modes

Inspired by the current representation of the ligand-receptor binding process, a normal-mode-based methodology is presented to incorporate receptor flexibility in ligand docking and virtual screening. However, the systematic representation of the deformation space grows geometrically with the number of modes, and furthermore, midscale loop rearrangements like those found in protein kinase binding pockets cannot be accounted for with the first lowest-frequency modes. We thus introduced a measure of relevance of normal modes on a given region of interest and showed that only very few modes in the low-frequency range are necessary and sufficient to describe loop flexibility in cAMP-dependent protein kinase. We used this approach to generate an ensemble of representative receptor backbone conformations by perturbing the structure along a combination of relevant modes. Each ensemble conformation is complexed with known non-native binders to optimize the position of the binding-pocket side chains through a full flexible docking procedure. The multiple receptor conformations thus obtained are used in a small-scale virtual screening using receptor ensemble docking. We evaluated this algorithm on holo and apo structures of cAMP-dependent protein kinase that exhibit backbone rearrangements on two independent loop regions close to the binding pocket. Docking accuracy is improved, since the ligands considered in the virtual screening docked within 1.5 A to at least one of the structures. The discrimination between binders and nonbinders is also enhanced, as shown by the improvement of the enrichment factor. This constitutes a new step toward the systematic integration of flexible ligand-flexible receptor docking tools in structure-based drug discovery.     

Optimization of high throughput virtual screening by combining shape-matching and docking methods

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiple-receptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a "distributed docking" method that improves virtual high throughput screening by combining a shape-matching method with a multiple-receptor conformation docking. Database compounds are classified in advance based on shape similarities to one of the crystal ligands complexed with the target protein. This classification enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm. The present virtual screening method was tested for reidentification of known PPARgamma and p38 MAP kinase active compounds. We demonstrate that this method improves the enrichment while maintaining the computation speed of a typical single-receptor conformation docking.     

Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling,molecular docking, 3D-QSAR model and binding free energy calculation studies

B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG_bind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC₅₀ < 50 μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs.     

Structure-based virtual screening of dipeptidyl peptidase 4 inhibitors and their in vitro analysis

Type 2 diabetes mellitus (T2DM) is one of the most widely prevalent metabolic disorders with no cure to date thus remains the most challenging task in the current drug discovery. Therefore, the only strategy to control diabetes prevalence is to develop novel efficacious therapeutics. Dipeptidyl Peptidase 4 (DPP-4) inhibitors are currently used as anti-diabetic drugs for the inhibition of incretins. This study aims to construct the chemical feature based on pharmacophore models for dipeptidyl peptidase IV. The structure-based pharmacophore modeling has been employed to evaluate new inhibitors of DPP-4. A four-featured pharmacophore model was developed from crystal structure of DPP-4 enzyme with 4-(2-aminoethyl) benzenesulfonyl fluoride in its active site via pharmacophore constructing tool of Molecular Operating Environment (MOE) consisting F1 Hyd (hydrophobic region), F2 Hyd|Cat|Don (hydrophobic cationic and donor region), F3 Acc (acceptor region) and F4 Hyd (hydrophobic region). The generated pharmacophore model was used for virtual screening of in-house compound library (the available compounds which were used for initial screening to get the few compounds for the current studies). The resultant selected compounds, after virtual screening were further validated using in vitro assay. Furthermore, structure-activity relationship was carried out for the compounds possessing significant inhibition potential after docking studies. The binding free energy of analogs was evaluated via molecular mechanics generalized Born surface area (MM-GBSA) and Poisson-Boltzmann surface area (MM-PBSA) methods using AMBER 16 as a molecular dynamics (MD) simulation package. Based on potential findings, we report that selected candidates are more likely to be used as DPP-4 inhibitors or as starting leads for the development of novel and potent DPP-4 inhibitors.     

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.     

Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.     

Novel inhibitor discovery through virtual screening against multiple protein conformations generated via ligand-directed modeling: a maternal embryonic leucine zipper kinase example

Kinase targets have been demonstrated to undergo major conformational reorganization upon ligand binding. Such protein conformational plasticity remains a significant challenge in structure-based virtual screening methodology and may be approximated by screening against an ensemble of diverse protein conformations. Maternal embryonic leucine zipper kinase (MELK), a member of serine-threonine kinase family, has been recently found to be involved in the tumerogenic state of glioblastoma, breast, ovarian, and colon cancers. We therefore modeled several conformers of MELK utilizing the available chemogenomic and crystallographic data of homologous kinases. We carried out docking pose prediction and virtual screening enrichment studies with these conformers. The performances of the ensembles were evaluated by their ability to reproduce known inhibitor bioactive conformations and to efficiently recover known active compounds early in the virtual screen when seeded with decoy sets. A few of the individual MELK conformers performed satisfactorily in reproducing the native protein-ligand pharmacophoric interactions up to 50% of the cases. By selecting an ensemble of a few representative conformational states, most of the known inhibitor binding poses could be rationalized. For example, a four conformer ensemble is able to recover 95% of the studied actives, especially with imperfect scoring function(s). The virtual screening enrichment varied considerably among different MELK conformers. Enrichment appears to improve by selection of a proper protein conformation. For example, several holo and unliganded active conformations are better to accommodate diverse chemotypes than ATP-bound conformer. These results prove that using an ensemble of diverse conformations could give a better performance. Applying this approach, we were able to screen a commercially available library of half a million compounds against three conformers to discover three novel inhibitors of MELK, one from each template. Among the three compounds validated via experimental enzyme inhibition assays, one is relatively potent (15; K(d) = 0.37 μM), one moderately active (12; K(d) = 3.2 μM), and one weak but very selective (9; K(d) = 18 μM). These novel hits may be utilized to assist in the development of small molecule therapeutic agents useful in diseases caused by deregulated MELK, and perhaps more importantly, the approach demonstrates the advantages of choosing an appropriate ensemble of a few conformers in pursuing compound potency, selectivity, and novel chemotypes over using single target conformation for structure-based drug design in general.     

Identification of potential inhibitors against nuclear Dam1 complex subunit Ask1 of Candida albicans using virtual screening and MD simulations

Identification of hit compounds against specific target form the starting point for a drug discovery program. A consistent decline of new chemical entities (NCEs) in recent years prompted a challenge to explore newer approaches to discover potential hit compounds that in turn can be converted into leads, and ultimately drug with desired therapeutic efficacy. The vast amount of omics and activity data available in public databases offers an opportunity to identify novel targets and their potential inhibitors. State of the art in silico methods viz., clustering of compounds, virtual screening, molecular docking, MD simulations and MMPBSA calculations were employed in a pipeline to identify potential ‘hits’ against those targets as well whose structures, as of now, could only predict through threading approaches. In the present work, we have started from scratch, amino acid sequence of target and compounds retrieved from PubChem compound database, modeled it in such a way that led to the identification of possible inhibitors of Dam1 complex subunit Ask1 of Candida albicans. We also propose a ligand based binding site determination approach. We have identified potential inhibitors of Ask1 subunit of a Dam1 complex of C. albicans, which is required to prevent precocious spindle elongation in pre-mitotic phases. The proposed scheme may aid to find virtually potential inhibitors of other unique targets against candida.     

Significant enhancement of docking sensitivity using implicit ligand sampling

The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the 'ligand-model' concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to rerank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.     

Design of multi-binding-site inhibitors, ligand efficiency, and consensus screening of avian influenza H5N1 wild-type neuraminidase and of the oseltamivir-resistant H274Y variant

The binding sites of wild-type avian influenza A H5N1 neuraminidase, as well as those of the Tamiflu (oseltamivir)-resistant H274Y variant, were explored computationally to design inhibitors that target simultaneously several adjacent binding sites of the open conformation of the virus protein. The compounds with the best computed free energies of binding, in agreement by two docking methods, consensus scoring, and ligand efficiency values, suggest that mimicking a polysaccharide, beta-lactam, and other structures, including known drugs, could be routes for multibinding site inhibitor design. This new virtual screening method based on consensus scoring and ligand efficiency indices is introduced, which allows the combination of pharmacodynamic and pharmacokinetic properties into unique measures.     

Clustering and classifying diverse HIV entry inhibitors using a novel consensus shape-based virtual screening approach: further evidence for multiple binding sites within the CCR5 extracellular pocket

HIV entry inhibitors have emerged as a new generation of antiretroviral drugs that block viral fusion with the CXCR4 and CCR5 membrane coreceptors. Several small molecule antagonists for these coreceptors have been developed, some of which are currently in clinical trials. However, because no crystal structures for the coreceptor proteins are available, the binding modes of the known inhibitors within the coreceptor extracellular pockets need to be analyzed by means of site-directed mutagenesis and computational experiments. Previous studies have indicated that there is more than one binding site within the CCR5 extracellular pocket. This article investigates and develops this hypothesis using a novel spherical harmonic-based consensus shape clustering approach. The consensus shape approach is evaluated using retrospective virtual screening of CXCR4 and CCR5 inhibitors. Multiple combinations of CCR5 ligands in multiple trial superpositions are constructed to find consensus queries that give high virtual screening enrichments. Receiver-operator-characteristic performance analyses for both CXCR4 and CCR5 inhibitors show that the new consensus shape matching approach gives better virtual screening enrichments than existing shape matching and docking virtual screening techniques. The results obtained also provide strong evidence to support the notion that there are three main binding sites within the CCR5 extracellular cavity.     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

Molecular modeling on pyrimidine-urea inhibitors of TNF-α production: an integrated approach using a combination of molecular docking, classification techniques, and 3D-QSAR CoMSIA

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC(50) values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.