Secondary Target Identification for Neuraminidase Inhibitor Drugs

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Project Goals. Strategy for secondary target identificationSMAP (SOIPPA Algorithm): Target the druggable" human proteome with known structural homologuesTarFisDockProgram simulates binding of drugs to database proteinsProteins are ranked based on best energetic interactionsDisadvantage: does

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Secondary Target Identification for Neuraminidase Inhibitor Drugs

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1. Secondary Target Identification for Neuraminidase Inhibitor Drugs Michael Wang PRIME July 18, 2008 Computer Network Information Center Beijing, China

2. Project Goals Strategy for secondary target identification SMAP (SOIPPA Algorithm): Target the “druggable” human proteome with known structural homologues TarFisDock Program simulates binding of drugs to database proteins Proteins are ranked based on best energetic interactions Disadvantage: does not take into account protein flexibility To take advantage of Autodock4’s flexible receptor feature to support receptor flexibility. To refine the target selection criteria by ensuring the proper parameterization of the druggable proteome.  Additionally, I will use known NA inhibitors and all known neuraminidases from different species, especially humans, with crystal structures as my initial test set. Selectivity of known Neuraminidase Inhibitors against viral subtypes Make comparative studies of the NA inhibitors against other potentially pandemic subtypes, such as N2, N7 Classify the previously identified top 27 hits in terms of their specificity for group-1, group-2 enzymes, and in particular the N2 and N7 enzymes Use Autodock4 to establish the parameters for optimal binding conditions through redocking of ligands from known crystal structures, using conditions previously characterized for N1 as a starting point. Neuraminidase virtual screening hits and Polymorphism Study the effects of polymorphism in neuraminidase and examine the interaction of oseltamivir with the R41Q mutant, situated near the active site of sialic acid, by using Autodock and Modeler Confirm validity using Procheck, WHATIF, and SCWRL to examine the selectivity for different neuraminidases inhibitors with this particular phenotype

3. Progress Obtained a list of druggable human proteins from Jacob Durrant Got SMAP to run successfully after editing scripts and revising commands Finished setting up SMAP environment by changing parameters in .sh shell script Edited parameters in pdbdefault.properties and smap.properties Recompiled qhull source code and placed it in the qhull linux directory within the SMAP distribution Installed and configured JDK 1.6 on the server Completed setup for MySQL database in order to match known ligand-binding sites Contacted Dr. Lei Xie regarding running larger datasets for SMAP Emailed developers of Autodock regarding program compatibility with different operating systems

4. Work to be done Figure out how to run SMAP by setting the query output with the druggable human proteome instead of with a single query chain Analyze and understand the resulting output values for SMAP results Create an Excel Spreadsheet for a data compilation of structural alignment between ligand-binding sites, Z-scores, Raw-scores, Tanimoto Coefficients and RMSD values Successfully load Autodock Tools on my OS in preparation for evaluating optimal binding conditions of neuraminidase inhibitor drugs and neuraminidases Generate mol2 file for oseltamivir and other inhibitor drugs in preparation for reverse docking analysis through TarFisDock web service.

5. Summer Palace ???

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