Direct Use of Information Extraction from Scientific Text for Modeling and Simulation in the Life Sciences

Purpose: 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 we 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. In this paper we 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/approach: The application of text-mining as well as knowledge discovery tools are explained in the form of a knowledge-based workflow for drug target candidate identification. Moreover, we propose an in silico experimentation framework for the enhancement of efficiency and productivity in the early steps of the drug discovery workflow. Findings: Our 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 implications: Direct extraction of biological information from text will ease the task of librarians in managing digital objects and supporting research projects. We expect that textual data will play an increasingly important role in evidence-based approaches taken by biomedical and translational researchers. Originality / value: Our 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 its application by academic and research libraries in support of scientific projects

Selective Immunoproteasome Inhibitors with Non-Peptide Scaffolds

Compounds useful for inhibiting the immunoproteasome have the formula of [image on patent]. Methods and compounds for inhibiting the immunoproteasome, particularly, immunoproteasome inhibitors with non-peptide scaffolds, are described

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

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 Journa

WISDOM-II: Screening against multiple targets implicated in malaria using computational grid infrastructures

<p>Abstract</p> <p>Background</p> <p>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 <it>Plasmodium </it>parasite, some are promising targets to carry out rational drug discovery.</p> <p>Motivation</p> <p>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.</p> <p>Methods</p> <p>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 <it>in silico </it>docking and in information technology to design and operate large scale grid infrastructures.</p> <p>Results</p> <p>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, <it>In vitro </it>results are underway for all the targets against which screening is performed.</p> <p>Conclusion</p> <p>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.</p

PDB_REDO: automated re-refinement of X-ray structure models in the PDB

The majority of previously deposited X-ray structures can be improved by applying current refinement methods

<em>In silico </em>drug discovery on computational Grids for finding novel drugs against neglected diseases

Malaria is a dreadful disease affecting 300 million people and killing 1-1.5 million people every year. Malaria is caused by a protozoan parasite, belonging to the genus Plasmodium. There are several species of Plasmodium infecting cattle, birds, and humans. The four species P.falciparum, P.vivax, P.malariae and P.ovale are in particular considered important, as these species infect humans. One of the main causes for the comeback of malaria is that the most widely used drug against malaria, chloroquine, has been rendered useless by drug resistance in much of the world. New antimalarial drugs are presently available but the potential emergence of resistance, the difficulty to synthesize these drugs at a large-scale and their cost make it of utmost importance to keep searching for new drugs. 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. 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. One potential limitation of structure-based methods, such as molecular docking and molecular dynamics is that; both are computational intensive tasks. Recent years have witnessed the emergence of Grids, which are highly distributed computing infrastructures particularly well fitted for embarrassingly parallel computations such as docking and molecular dynamics. The current thesis is a part of WISDOM project, which stands for Wide In silico Docking on Malaria. This thesis describes the rational drug discovery activity at large-scale, especially molecular docking and molecular dynamics on computational Grids in finding hits against four different targets (PfPlasmepsin, PfGST, PfDHFR, PvDHFR (wild type and mutant forms) implicated in malaria. The first attempt at using Grids for large-scale virtual screening (combination of molecular docking and molecular dynamics) focused on plasmepsins and ended up in the identification of previously unknown scaffolds, which were confirmed in vitro to be active plasmepsin inhibitors. The combination of docking and molecular dynamics simulations, followed by rescoring using sophisticated scoring functions resulted in the identification of 26 novel sub-micromolar inhibitors. The inhibitors are further clustered into five different scaffolds. While two scaffolds, diphenyl urea, and thiourea analogues are already known as plasmepsin inhibitors, albeit the compounds identified here are different from the existing ones, with the new class of potential inhibitors, the guanidino group of compounds, we have established a new class of chemical entities with inhibitory activity against Plasmodium falciparum plasmepsins. Following the success achieved on plasmepsin, a second drug finding effort was performed, focussed on one well known target, dihydrofolate reductase (DHFR), and on a new promising one, glutathione-S-transferase. Modeling results are very promising and based on these in silico results, in vitro tests are in progress. Thus, with the work presented here, we not only demonstrate the relevance of computational grids in drug discovery, but also identify several promising small molecules (success achieved on P. falciparum plasmepsins). With the use of the EGEE infrastructure for the virtual screening campaign against the malaria-causing parasite P. falciparum, we have demonstrated that resource sharing on an e-Science infrastructure such as EGEE provides a new model for doing collaborative research to fight diseases of the poor. Through WISDOM project, we propose a Grid-enabled virtual screening approach, to produce focus compound libraries for other biological targets relevant to fight the infectious diseases of the developing world