High-throughput Docking Research Articles

The Use of MoStBioDat for Rapid Screening of Molecular Diversity

MoStBioDat is a uniform data storage and extraction system with an extensive array of tools for structural similarity measures and pattern matching which is essential to facilitate the drug discovery process. Structure-based database screening has recently become a common and efficient technique in early stages of the drug development, shifting the emphasis from rational drug design into the probability domain of more or less random discovery. The virtual ligand screening (VLS), an approach based on high-throughput flexible docking, samples a virtually infinite molecular diversity of chemical libraries increasing the concentration of molecules with high binding affinity. The rapid process of subsequent examination of a large number of molecules in order to optimize the molecular diversity is an attractive alternative to the traditional methods of lead discovery. This paper presents the application of the MoStBioDat package not only as a data management platform but mainly in substructure searching. In particular, examples of the applications of MoStBioDat are discussed and analyzed.

Pushing Structural Information into the Yeast Interactome by High-Throughput Protein Docking Experiments

The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.

Library screening by fragment‐based docking

We review our computational tools for high-throughput screening by fragment-based docking of large collections of small molecules. Applications to six different enzymes, four proteases, and two protein kinases, are presented. Remarkably, several low-micromolar inhibitors were discovered in each of the high-throughput docking campaigns. Probable reasons for the lack of submicromolar inhibitors are the tiny fraction of chemical space covered by the libraries of available compounds, as well as the approximations in the methods employed for scoring, and the use of a rigid conformation of the target protein.

Discovery of Plasmepsin Inhibitors by Fragment‐Based Docking and Consensus Scoring

Plasmepsins (PMs) are essential proteases of the plasmodia parasites and are therefore promising targets for developing drugs against malaria. We have discovered six inhibitors of PM II by high-throughput fragment-based docking of a diversity set of approximately 40,000 molecules, and consensus scoring with force field energy functions. Using the common scaffold of the three most active inhibitors (IC(50)=2-5 microM), another seven inhibitors were identified by substructure search. Furthermore, these 13 inhibitors belong to at least three different classes of compounds. The in silico approach was very effective since a total of 13 active compounds were discovered by testing only 59 molecules in an enzymatic assay. This hit rate is about one to two orders of magnitude higher than those reported for medium- and high-throughput screening techniques in vitro. Interestingly, one of the inhibitors identified by docking was halofantrine, an antimalarial drug of unknown mechanism. Explicit water molecular dynamics simulations were used to discriminate between two putative binding modes of halofantrine in PM II.

Flaviviral Protease Inhibitors Identified by Fragment-Based Library Docking into a Structure Generated by Molecular Dynamics

Fragment-based docking was used to select a conformation for virtual screening from a molecular dynamics trajectory of the West Nile virus nonstructural 3 protease. This conformation was chosen from an ensemble of 100 molecular dynamics snapshots because it optimally accommodates benzene, the most common ring in known drugs, and two positively charged fragments (methylguanidinium and 2-phenylimidazoline). The latter fragments were used as probes because of the large number of hydrogen bond acceptors in the substrate binding site of the protease. Upon high-throughput docking of a diversity set of 18,694 molecules and pose filtering, only five compounds were chosen for experimental validation, and two of them are active in the low micromolar range in an enzymatic assay and a tryptophan fluorescence quenching assay. Evidence for specific binding to the protease active site is provided by nuclear magnetic resonance spectroscopy. The two inhibitors have different scaffolds (diphenylurea and diphenyl ester) and are promising lead candidates because they have a molecular weight of about 300 Da.

Life Science Research and Drug Discovery at the Turn of the 21st Century: The Experience of SwissBioGrid

It is often said that the life sciences are transforming into an information science. As laboratory experiments are starting to yield ever increasing amounts of data and the capacity to deal with those data is catching up, an increasing share of scientific activity is seen to be taking place outside the laboratories, sifting through the data and modelling "in silico" the processes observed "in vitro." The transformation of the life sciences and similar developments in other disciplines have inspired a variety of initiatives around the world to create technical infrastructure to support the new scientific practices that are emerging. The e-Science programme in the United Kingdom and the NSF Office for Cyberinfrastructure are examples of these. In Switzerland there have been no such national initiatives. Yet, this has not prevented scientists from exploring the development of similar types of computing infrastructures. In 2004, a group of researchers in Switzerland established a project, SwissBioGrid, to explore whether Grid computing technologies could be successfully deployed within the life sciences. This paper presents their experiences as a case study of how the life sciences are currently operating as an information science and presents the lessons learned about how existing institutional and technical arrangements facilitate or impede this operation. SwissBioGrid gave rise to two pilot projects: one for proteomics data analysis and the other for high-throughput molecular docking ("virtual screening") to find new drugs for neglected diseases (specifically, for dengue fever). The proteomics project was an example of a data management problem, applying many different analysis algorithms to Terabyte-sized datasets from mass spectrometry, involving comparisons with many different reference databases; the virtual screening project was more a purely computational problem, modelling the interactions of millions of small molecules with a limited number of protein targets on the coat of the dengue virus. Both present interesting lessons about how scientific practices are changing when they tackle the problems of large-scale data analysis and data management by means of creating a novel technical infrastructure. In the experience of SwissBioGrid, data intensive discovery has a lot to gain from close collaboration with industry and harnessing distributed computing power. Yet the diversity in life science research implies only a limited role for generic infrastructure; and the transience of support means that researchers need to integrate their efforts with others if they want to sustain the benefits of their success, which are otherwise lost.

A Small-Molecule Dengue Virus Entry Inhibitor

The incidence of dengue fever epidemics has increased dramatically over the last few decades. However, no vaccine or antiviral therapies are available. Therefore, the need for safe and effective antiviral drugs has become imperative. The entry of dengue virus into a host cell is mediated by its major envelope (E) protein. The crystal structure of the E protein reveals a hydrophobic pocket that is presumably important for low-pH-mediated membrane fusion. High-throughput docking with this hydrophobic pocket was performed, and hits were evaluated in cell-based assays. Compound 6 was identified as one of the inhibitors and had an average 50% effective concentration of 119 nM against dengue virus serotype 2 in a human cell line. Mechanism-of-action studies demonstrated that compound 6 acts at an early stage during dengue virus infection. It arrests dengue virus in vesicles that colocalize with endocytosed dextran and inhibits NS3 expression. The inhibitors described in this report can serve as molecular probes for the study of the entry of flavivirus into host cells.

Discovery of a Non-Peptidic Inhibitor of West Nile Virus NS3 Protease by High-Throughput Docking

BackgroundThe non-structural 3 protease (NS3pro) is an essential flaviviral enzyme and therefore one of the most promising targets for drug development against West Nile virus (WNV) and dengue infections.MethodologyIn this work, a small-molecule inhibitor of the WNV NS3pro has been identified by automatic fragment-based docking of about 12000 compounds and testing by nuclear magnetic resonance (NMR) spectroscopy of only 22 molecules. Specific binding of the inhibitor into the active site of NS3pro and its binding mode are confirmed by 15N-HSQC NMR spectra. The inhibitory activity is further validated by an enzymatic assay and a tryptophan fluorescence quenching assay.ConclusionThe inhibitor [4-(carbamimidoylsulfanylmethyl)-2,5-dimethylphenyl]-methylsulfanylmethanimidamide has a good ratio of binding affinity versus molecular weight (ligand efficiency of 0.33 kcal/mol per non-hydrogen atom), and thus has good potential as lead compound for further development to combat West Nile virus infections.

3-D clustering: a tool for high throughput docking

This report describes a computer program for clustering docking poses based on their 3-dimensional (3D) coordinates as well as on their chemical structures. This is chiefly intended for reducing a set of hits coming from high throughput docking, since the capacity to prepare and biologically test such molecules is generally far more limited than the capacity to generate such hits. The advantage of clustering molecules based on 3D, rather than 2D, criteria is that small variations on a scaffold may bring about different binding modes for molecules that would not be predicted by 2D similarity alone. The program does a pose-by-pose/atom-by-atom comparison of a set of docking hits (poses), scoring both spatial and chemical similarity. Using these pair-wise similarities, the whole set is clustered based on a user-supplied similarity threshold. An output coordinate file is created that mirrors the input coordinate file, but contains two new properties: a cluster number and similarity to the cluster center. Poses in this output file can easily be sorted by cluster and displayed together for visual inspection with any standard molecular viewing program, and decisions made about which molecule should be selected for biological testing as the best representative of this group of similar molecules with similar binding modes.

Data Management System for Distributed Virtual Screening

High throughput docking (HTD) using high performance computing platforms is a multidisciplinary challenge. To handle HTD data effectively and efficiently, we have developed a distributed virtual screening data management system (DVSDMS) in which the data handling and the distribution of jobs are realized by the open-source structured query language database software MySQL. The essential concept of DVSDMS is the separation of the data management from the docking and ranking applications. DVSDMS can be used to dock millions of molecules effectively, monitor the process in real time, analyze docking results promptly, and process up to 10(8) poses by energy ranking techniques. In an HTD campaign to identify kinase inhibitors a low cost Linux PC has allowed DVSDMS to efficiently assign the workload to more than 500 computing clients. Notably, in a stress test of DVSDMS that emulated a large number of clients, about 60 molecules per second were distributed to the clients for docking, which indicates that DVSDMS can run efficiently on very large compute cluster (up to about 40000 cores).

A double‐headed cathepsin B inhibitor devoid of warhead

Most synthetic inhibitors of peptidases have been targeted to the active site for inhibiting catalysis through reversible competition with the substrate or by covalent modification of catalytic groups. Cathepsin B is unique among the cysteine peptidase for the presence of a flexible segment, known as the occluding loop, which can block the primed subsites of the substrate binding cleft. With the occluding loop in the open conformation cathepsin B acts as an endopeptidase, and it acts as an exopeptidase when the loop is closed. We have targeted the occluding loop of human cathepsin B at its surface, outside the catalytic center, using a high-throughput docking procedure. The aim was to identify inhibitors that would interact with the occluding loop thereby modulating enzyme activity without the help of chemical warheads against catalytic residues. From a large library of compounds, the in silico approach identified [2-[2-(2,4-dioxo-1,3-thiazolidin-3-yl)ethylamino]-2-oxoethyl] 2-(furan-2-carbonylamino) acetate, which fulfills the working hypothesis. This molecule possesses two distinct binding moieties and behaves as a reversible, double-headed competitive inhibitor of cathepsin B by excluding synthetic and protein substrates from the active center. The kinetic mechanism of inhibition suggests that the occluding loop is stabilized in its closed conformation, mainly by hydrogen bonds with the inhibitor, thus decreasing endoproteolytic activity of the enzyme. Furthermore, the dioxothiazolidine head of the compound sterically hinders binding of the C-terminal residue of substrates resulting in inhibition of the exopeptidase activity of cathepsin B in a physiopathologically relevant pH range.

Use of the X-ray structure of the β 2-adrenergic receptor for drug discovery. Part 2: Identification of active compounds

The recently published X-ray structures of the β 2-adrenergic receptor are the first examples of ligand-mediated GPCR crystal structures. We have previously performed computational studies that examine the potential viability of these structures for use in drug design, exploiting known ligand activities. Our previous study and a newly reported β 2/Timolol X-ray complex provide validation of the computational approaches. In the present work, we use the X-ray structures to extract, via in silico high-throughput docking, compounds from proprietary and commercial databases and demonstrate the successful identification of active compounds by radioligand binding.

Docking and High Throughput Docking: Successes and the Challenge of Protein Flexibility

Docking and High Throughput Docking: Successes and the Challenge of Protein Flexibility

Discovery of a Drug‐Like G‐Quadruplex Binding Ligand by High‐Throughput Docking

A new G-quadruplex binding ligand, namely 1H-pyrazole-3-carboxy-4-methyl-5-phenyl-(1H-indol-3-ylmethylene)hydrazide, was identified from a database of 100 000 drug-like compounds by in silico high-throughput docking. This compound was demonstrated experimentally to be an effective stabilizer of G-quadruplex DNA; it exhibits high selectivity for G-quadruplex over duplex DNA.

Specific inhibitors of the protein tyrosine phosphatase Shp2 identified by high-throughput docking

The protein tyrosine phosphatase Shp2 is a positive regulator of growth factor signaling. Gain-of-function mutations in several types of leukemia define Shp2 as a bona fide oncogene. We performed a high-throughput in silico screen for small-molecular-weight compounds that bind the catalytic site of Shp2. We have identified the phenylhydrazonopyrazolone sulfonate PHPS1 as a potent and cell-permeable inhibitor, which is specific for Shp2 over the closely related tyrosine phosphatases Shp1 and PTP1B. PHPS1 inhibits Shp2-dependent cellular events such as hepatocyte growth factor/scatter factor (HGF/SF)-induced epithelial cell scattering and branching morphogenesis. PHPS1 also blocks Shp2-dependent downstream signaling, namely HGF/SF-induced sustained phosphorylation of the Erk1/2 MAP kinases and dephosphorylation of paxillin. Furthermore, PHPS1 efficiently inhibits activation of Erk1/2 by the leukemia-associated Shp2 mutant, Shp2-E76K, and blocks the anchorage-independent growth of a variety of human tumor cell lines. The PHPS compound class is therefore suitable for further development of therapeutics for the treatment of Shp2-dependent diseases.

Development and Experimental Validation of a Docking Strategy for the Generation of Kinase-Targeted Libraries

A high-throughput docking strategy for the filtering of in silico compounds and the generation of kinase-targeted libraries is described. Systematic docking and scoring in three kinase crystal 3D structures of 123 structurally diverse kinase ligands led to the determination of six thresholds for each kinase. These thresholds were used as filters for the virtual screening of two collections of compounds: a collection of more than 2500 drugs and drug-like compounds (negative control) and a kinase-targeted library of 1440 compounds. This strategy was then experimentally validated by testing 60 compounds from the kinase-targeted library on 41 kinases from five different families. The 60 compounds were split into those passing all the thresholds and the others (30 compounds in each group). The overall hit enrichment was 6.70-fold higher in the first group, validating our approach for the generation of kinase-targeted libraries and the identification of scaffolds with high kinase inhibitory potential.

Structure‐based tailoring of compound libraries for high‐throughput screening: Discovery of novel EphB4 kinase inhibitors

High-throughput docking is a computational tool frequently used to discover small-molecule inhibitors of enzymes or receptors of known three-dimensional structure. Because of the large number of molecules in chemical libraries, automatic procedures to prune multimillion compound collections are useful for high-throughput docking and necessary for in vitro screening. Here, we propose an anchor-based library tailoring approach (termed ALTA) to focus a chemical library by docking and prioritizing molecular fragments according to their binding energy which includes continuum electrostatics solvation. In principle, ALTA does not require prior knowledge of known inhibitors, but receptor-based pharmacophore information (hydrogen bonds with the hinge region) is additionally used here to identify molecules with optimal anchor fragments for the ATP-binding site of the EphB4 receptor tyrosine kinase. The 21,418 molecules of the focused library (from an initial collection of about 730,000) are docked into EphB4 and ranked by force-field-based energy including electrostatic solvation. Among the 43 compounds tested in vitro, eight molecules originating from two different anchors show low-micromolar activity in a fluorescence-based enzymatic assay. Four of them are active in a cell-based assay and are potential anti-angiogenic compounds.

AutoShim: Empirically Corrected Scoring Functions for Quantitative Docking with a Crystal Structure and IC50 Training Data

It has been notoriously difficult to develop general all-purpose scoring functions for high-throughput docking that correlate with measured binding affinity. As a practical alternative, AutoShim uses the program Magnet to add point-pharmacophore like "shims" to the binding site of each protein target. The pharmacophore shims are weighted by partial least-squares (PLS) regression, adjusting the all-purpose scoring function to reproduce IC 50 data, much as the shims in an NMR magnet are weighted to optimize the field for a better spectrum. This dramatically improves the affinity predictions on 25% of the compounds held out at random. An iterative procedure chooses the best pose during the process of shim parametrization. This method reproducibly converges to a consistent solution, regardless of starting pose, in just 2-4 iterations, so these robust models do not overtrain. Sets of complex multifeature shims, generated by a recursive partitioning method, give the best activity predictions, but these are difficult to interpret when designing new compounds. Sets of simpler single-point pharmacophores still predict affinity reasonably well and clearly indicate the molecular interactions producing effective binding. The pharmacophore requirements are very reproducible, irrespective of the compound sets used for parametrization, lending confidence to the predictions and interpretations. The automated procedure does require a training set of experimental compounds but otherwise adds little extra time over conventional docking.

Efficient 3D pharmacophore alignment as a tool for structure-based modeling and scoring

Chemical-feature based pharmacophore models have been established as state-of-the-art technique for bio-activity prediction. While in ligand-based drug design, chemical feature-based pharmacophore creation from a set of bio-active molecules is a frequently chosen approach, structure-based pharmacophores are still lacking the reputation to be an alternative or at least a supplement to docking techniques. Nevertheless, 3D pharmacophore screening bears the advantage of being much more efficient than unattended high-throughput docking and to transparently provide the user with all the information that is used by the screening algorithms to characterize the ligand-macromolecule interaction. Our efficient, rigid 3D pharmacophore superpositioning technique [1] will be applied to a number of structure-based pharmacophores selected from a diverse set of targets. Molecule coordinates generated from geometric fitting of multi-conformational models of small organic molecules to structure-based pharmacophores are compared to docking poses and discussed in terms of scoring, conformational coverage, and eligibility for virtual screening.

Discovery of Kinase Inhibitors by High-Throughput Docking and Scoring Based on a Transferable Linear Interaction Energy Model

The linear interaction energy method with continuum electrostatics (LIECE) is evaluated in depth on five kinases. The two multiplicative coefficients for the van der Waals energy and electrostatic free energy are shown to be transferable among different kinases. Moreover, good enrichment factors are obtained for a library of 40375 diverse compounds seeded with 73 known inhibitors of CDK2. Therefore, a general two-parameter LIECE model for kinases is derived by combining large data sets of inhibitors of CDK2, Lck, and p38. This two-parameter model is cross-validated on two kinases not used for fitting; it shows an average error of about 1.5 kcal/mol for the prediction of absolute binding affinity of 37 and 128 known inhibitors of EphB4 and EGFR, respectively. High-throughput docking and ranking by two-parameter LIECE models are shown to be able to identify novel low-micromolar EphB4 and CDK2 inhibitors of low-molecular weight (< or =355 g/mol).