4th National Symposium cum Workshop on "Recent Trends in Structural Bioinformatics and Computer Aided Drug Design"

Greetings from Department of Bioinformatics, Alagappa UniversityIt gives us immense pleasure to announce that the Department ofBioinformatics, Alagappa University, Karaikudi is organizing 4thNational Symposium cum Workshop on “Recent Trends in StructuralBioinformatics and Computer Aided Drug Design [SBCADD’2012]” duringFeb 20th – 23rd, 2012. The motive of the National symposium cumworkshop is to provide theoretical and practical exposure ofStructural Bioinformatics and Computer Aided Drug Design amongacademicians and technocrats in order to cater the industrial andacademic need.We have invited eminent personalities from reputed organizations, whowill enlighten the participants in the related fields. Please downloadthe brochure containing information and registration details alongwith this invitation for your kind perusal. We extend humbleinvitation to you and your colleagues to participate actively in theworkshop and make it a big success.It will be appreciated if you could inform to your colleague andstudents of your institute/organization. The National symposium cumworkshop on “RECENT TRENDS IN STRUCTURAL BIOINFORMATICS AND COMPUTERAIDED DRUG DESIGN” will be quite useful in various ways in differentfields such as Structural biology, System Biology, ComputationalChemistry and Pharmacogenomics.Call for Poster Presentations: All the participants are invited tosubmit an abstract of one page in English, describing their originalresearch related to the theme of this symposium cum workshop. Mailyour abstracts to bioinfoau@gmail.com.Please visit our website to get more details about the Workshop:www.bioinfoau.org/sbcadd'2012

New programming language will drive DNA

This is no Sci-Fi but reel to real! Yes we can in deed program the DNA for real Now! 

Bioinformatics scientists have built two logic gates for what they hope will become a new programming language for drug design as well as chemical and agricultural product engineering. The accomplishment seems hardly noteworthy except that these logic gates are made of E. coli. The two computational switches are based on two strains of the common bacterium. Researchers are now working to assemble them to perform computations.

This genetic programming software would resemble any other programming language, says Kevin Clancy, senior staff scientist for bioinformatics at Life Technologies Corp. The Carlsbad, CA, company is funding the work, which is being done by researchers at the UC San Francisco School of Pharmacy. Life Tech plans to commercialize the technology.

The software would convert instructions into a DNA sequence to be inserted into a bacterial, yeast or mammal cell. "It allows you to access and rewire biological systems on a scale that hasn't been possible in genetic engineering to date," says Christopher Voigt, UCSF associate professor.

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Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus

In this article, in the first part, we propose an artificial neural network-based intelligent technique to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARIs) for diabetes mellitus using two molecular descriptors, i.e., the electronegativity and molar volume of functional groups present in the main ARI lead structure. We have shown that the multilayer perceptron-based model is capable of determining the QSAR quite satisfactorily, with high R-value. Usually, the design of potent ARIs requires the use of complex computer docking and quantum mechanical (QM) steps involving excessive time and human judgement. In the second part of this article, to reduce the design cycle of potent ARIs, we propose a novel ANN technique to eliminate the computer docking and QM steps, to predict the total score. The MLP-based QSAR models obtained in the first part are used to predict the potent ARIs, using the experimental data reported by Hu et al. (J Mol Graph Mod 2006, 24, 244). The proposed ANN-based model can predict the total score with an R-value of 0.88, which indicates that there exists a close match between the predicted and experimental total scores. Using the ANN model, we obtained 71 potent ARIs out of 6.25 million new ARI compounds created by substituting different functional groups at substituting sites of main lead structure of known ARI. Finally, using high bioactivity relationship and total score values, we determined four potential ARIs out of these 71 compounds. Interestingly, these four ARIs include the two potent ARIs reported by Hu et al. (J Mol Graph Mod 2006, 24, 244) who obtained these through the complex computer docking and QM steps. This fact indicates the effectiveness of our proposed ANN-based technique. We suggest these four compounds to be the most promising candidates for ARIs to prevent the diabetic complications and further recommend for wet bench experiments to find their potential against AR in vitro and in vivo. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009 http://onlinelibrary.wiley.com/doi/10.1002/jcc.21240/abstract