1. The document discusses using databases like the Protein Data Bank (PDB) to better understand protein receptors and target recognition through data mining and analyzing protein structures and interactions.
2. It describes research tools for discovering and characterizing protein receptors, and how they can be used to undertake high-throughput hypothesis generation for protein-drug interactions on a proteome-wide scale.
3. The analysis of the Mycobacterium tuberculosis proteome and identification of potential drug targets from existing drugs is provided as an example of this approach.
Frontiers of Computing at the Cellular and Molecular Scales
Workshop031211
1. Mining databases for understanding target recognition Philip E. Bourne 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
2. Mining databases for understanding target recognition – well the PDB anyway Philip E. Bourne 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
3. “ If you remember 3 things from a lecture a week later it was a good lecture” from Ten Simple Rules for Making Good Oral Presentations PLoS Comp Biol 2007 3(4): e77 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
4. 1. The PDB services almost 200,000 scientists per month, but you are special – take advantage of this offer 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
5. “ I want to review all multimeric quaternary complexes in the PDB that may be of interest in the understanding of allosteric mechanisms exhibited by such complexes” Jean-Pierre Changeux
12. 2. We have research tools, not part of the PDB (yet), which are important for discovering and characterizing protein receptors 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
13.
14.
15.
16. 3. We can undertake high-throughput hypothesis generation for protein-drug interactions on a proteome-wide scale 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
17.
18.
19.
20. Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
23. Acknowledgements Funding Agencies: NSF, NIGMS, DOE, NLM, NCI, NCRR, NIBIB, NINDS, NIDDK 03/12/11 Workshop in Allosteric and Orthosteric Ligands in Drug Action
Editor's Notes
P distance to environmental boundary; Pi Di and alphai D distance to central atom alpha direction to central atom
3,996 proteins in TB proteome 749 solved structures in the PDB, representing a total of 284 proteins (7.2% coverage) ModBase contains homology models for entire TB proteome 1,446 ‘high quality’ homology models were added to the data set Structural coverage increased to 43.8% Retained only those models with a model score of > 0.7 and a Modpipe quality score of > 1.1 (2818 models). There were multiple models per protein. For each TB protein, chose the model with the best model score, and if they were equal, chose the model with the best Modpipe quality score (1703 models). However, 251 (+6) models were removed since they correspond to TB proteins that already have solved structures. 1446 models remained) Score for the reliability of a Model, derived from statistical potentials (F. Melo, R. Sanchez, A. Sali,2001 PDF ). A model is predicted to be good when the model score is higher than a pre-specified cutoff (0.7). A reliable model has a probability of the correct fold that is larger than 95%. A fold is correct when at least 30% of its Calpha atoms superpose within 3.5A of their correct positions. The ModPipe Protein Quality Score is a composite score comprising sequence identity to the template, coverage , and the three individual scores evalue , z-Dope and GA341 . We consider a MPQS of >1.1 as reliable
(nutraceuticals excluded)
Multi-target therapy may be more effective than single-target therapy to treat infectious diseases Most of the proteins listed are potential novel drug targets for the development of efficient anti-tuberculosis chemotherapeutics. GSMN-TB : Genome Scale Metabolic Reaction Network of M.tb (http://sysbio/sbs.surrey.ac.uk/tb) 849 reactions, 739 metabolites, 726 genes Can optimize the model for in vivo growth Carry out multiple gene inhibition and compute the maximal theoretical growth rate (if close to zero, that combination of genes is essential for growth)