Search

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. more...

WISDOM is an international initiative to enable a virtual screening pipeline on a Grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public Grid infrastructure. Protein–ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to Grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to desktop Grids. The first large scale docking experiment ran on the EGEE Grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with Grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the Grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was still required at the application level to resubmit the jobs that failed. But the experiment demonstrated how Grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time. This success leads to a second computing challenge targeting avian flu neuraminidase N1. more...

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. more...

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. more...

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. more...

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. more...

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. more...

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 more...

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. more...

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 more...

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. more...

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. more...