34,814 research outputs found
Innovative in silico approaches to address avian flu using grid technology
The recent years have seen the emergence of diseases which have spread very
quickly all around the world either through human travels like SARS or animal
migration like avian flu. Among the biggest challenges raised by infectious
emerging diseases, one is related to the constant mutation of the viruses which
turns them into continuously moving targets for drug and vaccine discovery.
Another challenge is related to the early detection and surveillance of the
diseases as new cases can appear just anywhere due to the globalization of
exchanges and the circulation of people and animals around the earth, as
recently demonstrated by the avian flu epidemics. For 3 years now, a
collaboration of teams in Europe and Asia has been exploring some innovative in
silico approaches to better tackle avian flu taking advantage of the very large
computing resources available on international grid infrastructures. Grids were
used to study the impact of mutations on the effectiveness of existing drugs
against H5N1 and to find potentially new leads active on mutated strains. Grids
allow also the integration of distributed data in a completely secured way. The
paper presents how we are currently exploring how to integrate the existing
data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target
XML in Motion from Genome to Drug
Information technology (IT) has emerged as a central to the solution of contemporary genomics and drug discovery problems. Researchers involved in genomics, proteomics, transcriptional profiling, high throughput structure determination, and in other sub-disciplines of bioinformatics have direct impact on this IT revolution. As the full genome sequences of many species, data from structural genomics, micro-arrays, and proteomics became available, integration of these data to a common platform require sophisticated bioinformatics tools. Organizing these data into knowledgeable databases and developing appropriate software tools for analyzing the same are going to be major challenges. XML (eXtensible Markup Language) forms the backbone of biological data representation and exchange over the internet, enabling researchers to aggregate data from various heterogeneous data resources. The present article covers a comprehensive idea of the integration of XML on particular type of biological databases mainly dealing with sequence-structure-function relationship and its application towards drug discovery. This e-medical science approach should be applied to other scientific domains and the latest trend in semantic web applications is also highlighted
A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment
Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N˙ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening
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
LAS: a software platform to support oncological data management
The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data qualit
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors
Improving the throughput of molecular docking, a computationally intensive
phase of the virtual screening process, is a highly sought area of research
since it has a significant weight in the drug designing process. With such
improvements, the world might find cures for incurable diseases like HIV
disease and Cancer sooner. Our approach presented in this paper is to utilize a
multi-core environment to introduce Data Level Parallelism (DLP) to the
Autodock Vina software, which is a widely used for molecular docking software.
Autodock Vina already exploits Instruction Level Parallelism (ILP) in
multi-core environments and therefore optimized for such environments. However,
with the results we have obtained, it can be clearly seen that our approach has
enhanced the throughput of the already optimized software by more than six
times. This will dramatically reduce the time consumed for the lead
identification phase in drug designing along with the shift in the processor
technology from multi-core to many-core of the current era. Therefore, we
believe that the contribution of this project will effectively make it possible
to expand the number of small molecules docked against a drug target and
improving the chances to design drugs for incurable diseases.Comment: Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th
International Conference o
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