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The drug discovery process in general is a very resource intensive undertaking that has existed for a very long time. In the last two decades, performing molecular simulations that determine the level of interaction between a protein and ligand have been refined to the point where they are now an essential part of the drug discovery process. These simulations serve to reduce the time to discovery and improve the positive "hit" rates when screening for molecule with biological activity. As a result, the chemical search space is greatly reduced in silico, prior to any in vitro experiments that validate the results. Recently, there have been many advances in computer science technologies that have improved the virtual screening process. This paper will give a brief overview of the virtual screening process and then summarize the current state-of-the-art technologies applied to virtual screenings. Both biomedical researchers and computer scientists can use this review as a guide to the implementation requirements for computational resources of virtual screening.

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SUMMARY: ViewDock TDW is a modification of the pre-existing ViewDock Chimera extension (http://www.cgl.ucsf.edu/chimera/) used to visualize results of virtual screening experiments. By combing TDW hardware and an enhanced ViewDock interface, dozens of ligand-protein complexes are rendered simultaneously to parallelize the analysis of candidate ligands. The ViewDock TDW GUI allows the user to easily and interactively manipulate the molecules on the TDW as an entire set, a selected subset or a single ligand-protein complex and preserves all Chimera functionality. AVAILABILITY AND IMPLEMENTATION: ViewDock TDW is an open source software; freely available on the web at http://www.tdw-prime.webs.com. Chimera UCSF is also available, free of charge, at http://www.cgl.ucsf.edu/chimera/

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Large-scale in-silico screening is a necessary part of drug discovery and Grid computing is one answer to this demand. A disadvantage of using Grid computing is the heterogeneous computational environments characteristic of a Grid. In our study, we have found that for the molecular docking simulation program DOCK, different clusters within a Grid organization can yield inconsistent results. Because DOCK in-silico virtual screening (VS) is currently used to help select chemical compounds to test with in-vitro experiments, such differences have little effect on the validity of using virtual screening before subsequent steps in the drug discovery process. However, it is difficult to predict whether the accumulation of these discrepancies over sequentially repeated VS experiments will significantly alter the results if VS is used as the primary means for identifying potential drugs. Moreover, such discrepancies may be unacceptable for other applications requiring more stringent thresholds. This highlights the need for establishing a more complete solution to provide the best scientific accuracy when executing an application across Grids. One possible solution to platform heterogeneity in DOCK performance explored in our study involved the use of virtual machines as a layer of abstraction. This study investigated the feasibility and practicality of using virtual machine and recent cloud computing technologies in a biological research application. We examined the differences and variations of DOCK VS variables, across a Grid environment composed of different clusters, with and without virtualization. The uniform computer environment provided by virtual machines eliminated inconsistent DOCK VS results caused by heterogeneous clusters, however, the execution time for the DOCK VS increased. In our particular experiments, overhead costs were found to be an average of 41% and 2% in execution time for two different clusters, while the actual magnitudes of the execution time costs were minimal. Despite the increase in overhead, virtual clusters are an ideal solution for Grid heterogeneity. With greater development of virtual cluster technology in Grid environments, the problem of platform heterogeneity may be eliminated through virtualization, allowing greater usage of VS, and will benefit all Grid applications in general.

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Grid computing offers the powerful alternative of sharing resources on a worldwide scale, across different institutions to run computationally intensive, scientific applications without the need for a centralized supercomputer. Much effort has been put into development of software that deploys legacy applications on a grid-based infrastructure and efficiently uses available resources. One field that can benefit greatly from the use of grid resources is that of drug discovery since molecular docking simulations are an integral part of the discovery process. In this paper, we present a scalable, reusable platform to choreograph large virtual screening experiments over a computational grid using the molecular docking simulation software DOCK. Software components are applied on multiple levels to create automated workflows consisting of input data delivery, job scheduling, status query, and collection of output to be displayed in a manageable fashion for further analysis. This was achieved using Opal OP to wrap the DOCK application as a grid service and PERL for data manipulation purposes, alleviating the requirement for extensive knowledge of grid infrastructure. With the platform in place, a screening of the ZINC 2,066,906 compound "drug-like" subset database against an enzyme's catalytic site was successfully performed using the MPI version of DOCK 5.4 on the PRAGMA grid testbed. The screening required 11.56 days laboratory time and utilized 200 processors over 7 clusters.

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This paper describes a sub-project of BioGrid project called "HTC (High Throughput Computing) group." Generally, a protein structure prediction which requires large amount of computational resources is done by trial-and-error method. HTC group have been developing a high throughput computing system with a flexible workflow handling mechanism for a protein structure prediction. In this paper, we show how to apply our high throughput computing system to the protein structure predictor called "ROKKY."

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The recent advance in information technologies has bought about the borderlessness in every field of both science and business. The borderlessness has increasingly made activities in interdisciplinary field more important. This current situation produces a strong demand that people want to establish a virtual group, organization and society for their business and scientific purposes irrespective of the actual structure formed by organizations. Remarkably, bio sciences require a research platform that satisfies such demand for further development. In this paper, we present a research platform for bioinformatics in detail. The prominent feature of the research platform is the use of Grid and its location transparency, which means that bio scientists and researchers are able to utilize a large amount of computational power for their analysis and to access data of their interest without being aware of where data and computational resources are located. The usefulness and feasibility of the architecture of the research platform is shown as well as future issues to achieve toward the final goal of our research in this paper.

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Magnetoencephalographic findings in a 6-year-old patient suffering from acquired aphasia with convulsive disorder (Landau-Kleffner Syndrome, LKS) are presented. The data were analyzed using wavelet-cross-correlation analysis, a nonstationary analysis method developed to analyze the localization of an epileptogenic lesion and the propagation of epileptiform discharges. The results indicate that LKS might be a disorder of the primary temporal cortex, and that the auditory neural network may function as the circuit for the epileptic discharge propagation.