Secondary Target Identification for Neuraminidase Inhibitor Drugs

1
Secondary Target Identification for Neuraminidase
Inhibitor Drugs

• Michael Wang
• PRIME
• August 15, 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 Autodock4s 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
Completed Tasks

• Gained familiarity with Linux
• Protein structure similarity by using SMAP
• Successfully installed program and figured out
  how to run jobs
• Obtained extensive list of 90,000 druggable human
  proteins, selected representative protein from
  each cluster within the protein list file
• Split representative protein list into 10 smaller
  files
• Ran jobs with smaller files and successfully got
  output files with Z-score, Raw-score, Tanimoto
  Coefficient, and RMSD.
• Reverse Docking through TarFisDock
• Successfully submitted oseltamivir as ligand, and
  was able to retrieve the top protein hits from
  the TarFisDock web service
• Docking Analysis using Autodock
• Obtained list of Top 27 Ligand Hits for
  Neuraminidase and mapped ligands according to NCI
  Diversity Set Numbers, NSC Numbers, and ZINC
  IDs.
• Redocked known crystal structures of 1ING ligand
  to the receptor and established the proper
  parameters for docking of Top 27 Hit Ligands to
  Neuraminidase subtypes.

4
Remaining Tasks

• Write research paper on project.
• Through Autodock4, use established parameters for
  N2 subtype to dock against Top 27 ligand hits for
  neuraminidase for characterization of ligand
  affinity towards group 1 or group 2
  neuraminidases.
• Establish parameters for the N7 subtype, in
  preparation for redocking against the Top 27
  ligand hits for neuraminidase.
• Compile SMAP comparison results from output files
  to see which proteins are closest in structure to
  N1.
• Make comparisons of SMAP results and TarFisDock
  protein hits

5
Around Beijing
Peking Duck ????
Qianmen Street ????
Beach Volleyball ????
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Acknowledgements

• UCSD/PRIME
• Dr. Wilfred Li
• Dr. Peter Arzberger
• Dr. Gabriel Wienhausen
• Teri Simas
• Dr. Dong Xu
• Dr. Lei Xie
• Dr. Rommie Amaro
• Jacob Durrant
• Lily Cheng
• Hsing Pao
• CNIC, CAS
• Dr. Nan Kai
• Dr. Zhonghua Lu
• Guangyuan Liu
• Yanhua Sun
• Dr. Jianjun Yu