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Dinitroanilines are chemical compounds with high selectivity for plant cell &alpha, tubulin in which they promote microtubule depolymerization. They target &alpha, tubulin regions that have diverged over evolution and show no effect on non-photosynthetic eukaryotes. Hence, they have been used as herbicides over decades. Interestingly, dinitroanilines proved active on microtubules of eukaryotes deriving from photosynthetic ancestors such as Toxoplasma gondii and Plasmodium falciparum, which are responsible for toxoplasmosis and malaria, respectively. By combining differential in silico screening of virtual chemical libraries on Arabidopsis thaliana and mammal tubulin structural models together with cell-based screening of chemical libraries, we have identified dinitroaniline related and non-related compounds. They inhibit plant, but not mammalian tubulin assembly in vitro, and accordingly arrest A. thaliana development. In addition, these compounds exhibit a moderate cytotoxic activity towards T. gondii and P. falciparum. These results highlight the potential of novel herbicidal scaffolds in the design of urgently needed anti-parasitic drugs.

We performed a virtual screen of ∼340 000 small molecules against the active site of proteasomes followed by in vitro assays and subsequent optimization, yielding a proteasome inhibitor with pyrazole scaffold. The pyrazole-scaffold compound displayed excellent metabolic stability and was highly effective in suppressing solid tumor growth in vivo. Furthermore, the effectiveness of this compound was not negatively impacted by resistance to bortezomib or carfilzomib.

As a major component of the crucial nonlysosomal protein degradation pathway in the cells, the proteasome has been implicated in many diseases such as Alzheimer's disease, Huntington's disease, inflammatory bowel diseases, autoimmune diseases, multiple myeloma (MM) and other cancers. There are two main proteasome subtypes: the constitutive proteasome which is expressed in all eukaryotic cells and the immunoproteasome which is expressed in immune cells and can be induced in other cell types. Majority of currently available proteasome inhibitors are peptide backbone-based, having short half-lives in the body. It is highly desirable to identify novel, immunoproteasome-selective inhibitors with non-peptide scaffolds for development of novel therapeutics. Through combined virtual screening and experimental studies targeting the immunoproteasome, we have identified a set of novel immunoproteasome inhibitors with diverse non-peptide scaffolds. Some of the identified inhibitors have significant selectivity for the immunoproteasome over the constitutive proteasome. Unlike most of the currently available proteasome inhibitors, these new inhibitors lacking electrophilic pharmacophores are not expected to form a covalent bond with proteasome after the binding. These non-peptide scaffolds may provide a new platform for future rational drug design and discovery targeting the immunoproteasome.

A series of novel 5-((1-aroyl-1H-indol-3-yl)methylene)-2-thioxodihydropyrimidine-4,6(1H,5H)-diones (3a-z) have been evaluated for in vitro cytotoxicity against a panel of 60 human tumor cell lines. Compound 3k exhibited the most potent growth inhibition against melanoma MDA-MB-435 cells (GI(50)=850 nM), against leukemia SR cancer cells (GI(50)=1.45 μM), and OVCAR-3 (GI(50)=1.26 μM) ovarian cancer cell lines. The structurally related compound 3s had a GI(50) value of 1.77 μM against MDA-MB-435 cells. The N-naphthoyl analogue 3t had GI(50) values of 1.30 and 1.91 μM against HOP-92 non-small cell lung cancer and MDA-MB-435 melanoma cell lines, respectively. The related analogue 3w had GI(50) values of 1.09 μM against HOP-92 non-small cell lung cancer cell lines. Interestingly, docking of the two active molecules 3k and 3w into the active site of COX-2 indicates that these compounds are COX-2 ligands with strong hydrophobic and hydrogen bonding interactions. Thus, compounds 3k, 3t, 3s, and 3w constitute a new class of anticancer/anti-inflammatory agents that may have unique potential for cancer therapy.

The use of protein-ligand interaction information has been reported to improve and optimize the docking results in virtual screening experiments. Here we propose an improved weighted-residue profile based method to profile the protein-ligand interactions based on the available dataset of known actives and utilize this weighted residue profile information, together with the scoring function, as selection criteria to increase hit rates in virtual screening experiments. The generated fingerprint data is not directly based on the protein-ligand complexes but taken from the available interaction data derived from the docking results. The ability of the method to recover the active compounds was tested on two data sets of a compound library designed for antagonists of the A2A receptor. The results show better hits enrichments by using the weighted-residue based profiles of protein-ligand interactions as compared to the normal binding energy based scoring schemes of the two docking programs.

Significance: Aberrant protein-protein interactions are a hallmark of disease and many cancers. Disrupting these interactions is a current therapeutic strategy. However, developing inhibitors for protein-protein interfaces (PPI) remains challenging due to large surface area over which these interactions occur. Computational methods can greatly aid in identifying druggable sites on the PPI enabling rational inhibitor design for PPI.Approach: We have developed a computational method termed FindBindSite (FBS), to identify druggable sites in the PPI starting from free monomer structures. Our method virtually screens a small database of compounds or dipeptides over the entire protein surface and identifies regions with high docked ligand atom density. Densely populated regions are then clustered and scored based on cluster size. The clustering allows us to identify binding surfaces in the interface regions.Results: FBS was validated 41 protein-protein structures crystallized in complex form. Structures were selected giving preference to free, protein-inhibitor, and then protein-protein complex when structures were not available. We predicted binding sites in interface regions of 71% with a high confidence and 90% with a low confidence using our test set. We tested the performance of FBS on homology models of free monomers achieving a hit rate of 68% when using templates with sequence identity between 20-97%. Applying a 60% sequence identity cutoff we achieved a hit rate of 86%. Using a library of dipeptides we were able to achieve 85% hit rate. We demonstrate that FBS is a useful computational method to predict binding sites in protein-protein interfaces because it uses the probe molecule diversity to span beyond well formed pockets and identify regions where one could likely disrupt any PPIs are likely to occur.

The first step in finding a "drug" is screening chemical compound databases against a protein target. In silico approaches like virtual screening by molecular docking are well established in modern drug discovery. As molecular databases of compounds and target structures are becoming larger and more and more computational screening approaches are available, there is an increased need in compute power and more complex workflows. In this regard, computational Grids are predestined and offer seamless compute and storage capacity. In recent projects related to pharmaceutical research, the high computational and data storage demands of large-scale in silico drug discovery approaches have been addressed by using Grid computing infrastructures, in both; pharmaceutical industry as well as academic research. Grid infrastructures are part of the so-called eScience paradigm, where a digital infrastructure supports collaborative processes by providing relevant resources and tools for data- and compute-intensive applications. Substantial computing resources, large data collections and services for data analysis are shared on the Grid infrastructure and can be mobilized on demand. This review gives an overview on the use of Grid computing for in silico drug discovery and tries to provide a vision of future development of more complex and integrated workflows on Grids, spanning from target identification and target validation via protein-structure and ligand dependent screenings to advanced mining of large scale in silico experiments.

PurposeThe purpose of this paper is to demonstrate how the information extracted from scientific text can be directly used in support of life science research projects. In modern digital‐based research and academic libraries, librarians should be able to support data discovery and organization of digital entities in order to foster research projects effectively; thus the paper aims to speculate that text mining and knowledge discovery tools could be of great assistance to librarians. Such tools simply enable librarians to overcome increasing complexity in the number as well as contents of scientific literature, especially in the emerging interdisciplinary fields of science. This paper seeks to present an example of how evidences extracted from scientific literature can be directly integrated into in silico disease models in support of drug discovery projects.Design/methodology/approachThe application of text‐mining as well as knowledge discovery tools is explained in the form of a knowledge‐based workflow for drug target candidate identification. Moreover, an in silico experimentation framework is proposed for the enhancement of efficiency and productivity in the early steps of the drug discovery workflow.FindingsThe in silico experimentation workflow has been successfully applied to searching for hit and lead compounds in the World‐wide In Silico Docking On Malaria (WISDOM) project and to finding novel inhibitor candidates.Practical implicationsDirect extraction of biological information from text will ease the task of librarians in managing digital objects and supporting research projects. It is expected that textual data will play an increasingly important role in evidence‐based approaches taken by biomedical and translational researchers.Originality/valueThe proposed approach provides a practical example for the direct integration of text‐ and knowledge‐based data into life science research projects, with the emphasis on their application by academic and research libraries in support of scientific projects.

In the last decade, life science applications have become more and more integrated into e-Science environments, hence they are typically very demanding, both in terms of computational capabilities and data capacities. Especially the access to life science applications, embedded in such environments via Grid clients still constitutes a major hurdle for scientists that do not have an IT background. Life science applications often comprise a whole set of small programs instead of a single executable. Many of the graphical Grid clients are not perfectly suited for these types of applications, as they often assume that Grid jobs will run a single executable instead of a set of chained executions (i.e. sequences). This means that in order to execute a sequence of multiple programs on a single Grid resource, piping data from one program to the next, the user would have to run a hand-written shell script. Otherwise each program is independently scheduled as a Grid job, which causes unnecessary file transfers between the jobs, even if they are scheduled on the same resource. We present a generic solution to this problem and provide a reference implementation, which seamlessly integrates with the Grid middleware UNICORE. Our approach focuses on a comfortable user interface for the creation of such program sequences, validated in UNICORE-driven HPC-based Grids. Thus, we applied our approach in order to provide support for the usage of the AMBER package (a widely-used collection of programs for molecular dynamics simulations) within Grid workflows. We finally provide a scientific use case of our approach leveraging the interoperability of two different scientific infrastructures that represents an instance of the infrastructure interoperability reference model.

Background Despite continuous efforts of the international community to reduce the impact of malaria on developing countries, no significant progress has been made in the recent years and the discovery of new drugs is more than ever needed. Out of the many proteins involved in the metabolic activities of the Plasmodium parasite, some are promising targets to carry out rational drug discovery. Motivation Recent years have witnessed the emergence of grids, which are highly distributed computing infrastructures particularly well fitted for embarrassingly parallel computations like docking. In 2005, a first attempt at using grids for large-scale virtual screening focused on plasmepsins and ended up in the identification of previously unknown scaffolds, which were confirmed in vitro to be active plasmepsin inhibitors. Following this success, a second deployment took place in the fall of 2006 focussing on one well known target, dihydrofolate reductase (DHFR), and on a new promising one, glutathione-S-transferase. Methods In silico drug design, especially vHTS is a widely and well-accepted technology in lead identification and lead optimization. This approach, therefore builds, upon the progress made in computational chemistry to achieve more accurate in silico docking and in information technology to design and operate large scale grid infrastructures. Results On the computational side, a sustained infrastructure has been developed: docking at large scale, using different strategies in result analysis, storing of the results on the fly into MySQL databases and application of molecular dynamics refinement are MM-PBSA and MM-GBSA rescoring. The modeling results obtained are very promising. Based on the modeling results, In vitro results are underway for all the targets against which screening is performed. Conclusion The current paper describes the rational drug discovery activity at large scale, especially molecular docking using FlexX software on computational grids in finding hits against three different targets (PfGST, PfDHFR, PvDHFR (wild type and mutant forms) implicated in malaria. Grid-enabled virtual screening approach is proposed to produce focus compound libraries for other biological targets relevant to fight the infectious diseases of the developing world.

Novel and potent inhibitors of Plasmodium falciparum plasmepsin II were identified by post-processing the results of a docking screening with BEAR, a recently reported procedure for the refinement and rescoring of docked ligands in virtual screening. FRET substrate degradation assays performed on the 30 most promising compounds resulted in 26 inhibitors with IC(50) values ranging from 4.3 nM to 1.8 microM.Herein we report the discovery of novel and potent inhibitors of Plasmodium falciparum plasmepsin II using GRID computing infrastructures. These compounds were identified by post-processing the results of a large docking screen of commercially available compounds using an automated procedure based on molecular dynamics refinement and binding free-energy estimation using MM-PBSA and MM-GBSA. Among the best-scored compounds, four highly populated and promising chemical classes were identified: N-alkoxyamidines, guanidines, amides, and ureas and thioureas. Thirty hit compounds representative of each class were selected on the basis of their favourable binding free energies and molecular interactions with key active site residues. These were experimentally validated using an inhibition assay based on FRET substrate degradation. Remarkably, 26 of the 30 tested compounds proved to be active as plasmepsin II inhibitors, with IC(50) values ranging from 4.3 nM to 1.8 microM.

The majority of previously deposited X-ray structures can be improved by applying current refinement methods., Structural biology, homology modelling and rational drug design require accurate three-dimensional macromolecular coordinates. However, the coordinates in the Protein Data Bank (PDB) have not all been obtained using the latest experimental and computational methods. In this study a method is presented for automated re-refinement of existing structure models in the PDB. A large-scale benchmark with 16 807 PDB entries showed that they can be improved in terms of fit to the deposited experimental X-ray data as well as in terms of geometric quality. The re-refinement protocol uses TLS models to describe concerted atom movement. The resulting structure models are made available through the PDB_REDO databank (http://www.cmbi.ru.nl/pdb_redo/). Grid computing techniques were used to overcome the computational requirements of this endeavour.

Malaria remains a global health concern, which kills over a million people each year. In this paper we present work extending the approach of the WISDOM initiative by focusing on the problems noticed during the first WISDOM challenge against malaria and test the newly established, high bandwidth optical Grid environment VIOLA for advanced bioinformatics applications using the UNICORE middleware service. In addition we present an approach to reduce the size of the compound database to improve the efficiency of the screening.

After having deployed a first data challenge on malaria and a second one on avian flu, respectively in summer 2005 and spring 2006, we are demonstrating here again how efficiently the computational grids can be used to produce massive docking data at a high-throughput. During more than 2 months and a half, we have achieved at least 140 million dockings, representing an average throughput of almost 80,000 dockings per hour. This was made possible by the availability of thousands of CPUs through different infrastructures worldwide. Through the acquired experience, the WISDOM production environment is evolving to enable an easy and fault-tolerant deployment of biological tools; in this case it is the FlexX commercial docking software which is used to dock the whole ZINC database against 4 different targets.

PCSV; International audience; Computational grids are solutions for several biological applications like virtual screening or molecular dynamics where large amounts of computing power and storage are required. The WISDOM project successfully deployed virtual screening at large scale on EGEE grid infrastructures in the summer 2005 and achieved 46 million dockings in 45 days, which is equivalent to 80 CPU years. WISDOM is one good example of a successful deployment of an embarrassingly parallel application. In this paper, we describe the improvements in our deployment. We screened ZINC database against four targets implicated in malaria. During more than 2 months and a half, we have achieved 140 million dockings, representing an average throughput of almost 80,000 dockings per hour. This was made possible by the availability of thousands of CPUs through different infrastructures worldwide. Through the acquired experience, the WISDOM production environment is evolving to enable an easy and fault-tolerant deployment of biological tools

After having deployed a first data challenge on malaria and a second one on avian flu, respectively in summer 2005 and spring 2006, we are demonstrating here again how efficiently the computational grids can be used to produce massive docking data at a high-throughput. During more than 2 months and a half, we have achieved at least 140 million dockings, representing an average throughput of almost 80,000 dockings per hour. This was made possible by the availability of thousands of CPUs through different infrastructures worldwide. Through the acquired experience, the WISDOM production environment is evolving to enable an easy and fault-tolerant deployment of biological tools; in this case it is the FlexX commercial docking software which is used to dock the whole ZINC database against 4 different targets.

PCSV, article in press in Parallel Computing; Large scale grids for in silico drug discovery open opportunities of particular interest to neglected and emerging diseases. In 2005 and 2006, we have been able to deploy large scale virtual docking within the framework of the WISDOM initiative against malaria and avian influenza requiring about 100 years of CPU on the EGEE, Auvergrid and TWGrid infrastructures. These achievements demonstrated the relevance of large scale grids for the virtual screening by molecular docking. This also allowed evaluating the performances of the grid infrastructures and to identify specific issues raised by large scale deployment.

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed., Comment: 43 pages, 4 figures, to appear in Malaria Journal

Allosteric modulators that regulate the activity of the orthosteric ligands are emerging as cutting-edge strategies in drug design. Unlike orthosteric ligands, allosteric modulators bind to topographically distinct domains from those utilized by orthosteric ligands. Allosteric modulators offer unique therapeutic advantages such as high selectivity thereby causing reduced side effects. However, allosteric pockets are difficult to find since they are often formed transiently during the protein dynamics and hence could be absent in the crystal structures. This poses a challenge in designing allosteric modulators using structure based drug design methods that rely solely on crystal structures or homology models. Moreover not all transient pockets are suitable for allosteric modulation, since the allosteric pocket must communicate with the orthosteric site for functional modulation. Thus there is a dire need for novel techniques that utilize information from protein dynamics to detect allosteric sites for drug design. We present here a comprehensive method for designing allosteric modulators using protein dynamics trajectories or NMR data. We have developed a method, VoidVol, to identify transient binding cavities during protein dynamics. Next, using mutual information calculated from the dynamics trajectories, we map the allosteric pipelines communicating with the orthosteric site. The transient pockets having strong allosteric communication with the orthosteric site can be used for screening allosteric modulators. These sites can be further tested for druggability using the program FindBindSite, also developed in our laboratory. The resulting druggable sites can then be used for high-throughput screening of small-molecule database. We have validated this approach using several kinases and GPCRs with known allosteric modulators. The above methodology demonstrates how molecular dynamics can be useful for allosteric drug design. Our method is applicable to any water-soluble or membrane protein with an available crystal structure or homology model.

Enabling the seamless integration between applications executing on heterogeneous Grid middleware poses a number of challenges to both application scientists and middleware developers. We highlight some of these challenges in the context of designing and implementing DockFlow. DockFlow is a virtual screening environment integrating four Grid-based protein docking tools that execute on different Grid middleware technologies at different locations. We propose extensions that can be applied to any Grid-based workflow system to support the run-time interoperability between the available tools. The extensions are generic, and as an example we describe how they have been implemented in the InforSense workflow system. We also present experimental results that evaluate the tradeoffs between performance and usability of the proposed methods.

Large scale grids for in silico drug discovery open opportunities of particular interest to neglected and emerging diseases. In 2005 and 2006, we have been able to deploy large scale virtual docking within the framework of the WISDOM initiative against malaria and avian influenza requiring about 100 years of CPU on the EGEE, Auvergrid and TWGrid infrastructures. These achievements demonstrated the relevance of large scale grids for the virtual screening by molecular docking. This also allowed evaluating the performances of the grid infrastructures and to identify specific issues raised by large scale deployment.