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Dinitroanilines are chemical compounds with high selectivity for plant cell α-tubulin in which they promote microtubule depolymerization. They target α-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., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Protein-protein interactions are implicated in the pathogenesis of many diseases and are therefore attractive but challenging targets for drug design. One of the challenges in development is the identification of potential druggable binding sites in protein interacting interfaces. Identification of interface surfaces can greatly aid rational drug design of small molecules inhibiting protein-protein interactions. In this work, starting from the structure of a free monomer, we have developed a ligand docking based method, called "FindBindSite" (FBS), to locate protein-protein interacting interface regions and potential druggable sites in this interface. FindBindSite utilizes the results from docking a small and diverse library of small molecules to the entire protein structure. By clustering regions with the highest docked ligand density from FBS, we have shown that these high ligand density regions strongly correlate with the known protein-protein interacting surfaces. We have further predicted potential druggable binding sites on the protein surface using FBS, with druggability being defined as the site with high density of ligands docked. FBS shows a hit rate of 71% with high confidence and 93% with lower confidence for the 41 proteins used for predicting druggable binding sites on the protein-protein interface. Mining the regions of lower ligand density that are contiguous with the high scoring high ligand density regions from FBS, we were able to map 70% of the protein-protein interacting surface in 24 out of 41 structures tested. We also observed that FBS has limited sensitivity to the size and nature of the small molecule library used for docking. The experimentally determined hotspot residues for each protein-protein complex cluster near the best scoring druggable binding sites identified by FBS. These results validate the ability of our technique to identify druggable sites within protein-protein interface regions that have the maximal possibility of interface disruption.

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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.

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.

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.

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.

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.

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.