V.N. Orechovich Institute of Biomedical Chemistry RAMS, Moscow, Russia

1. V.N. Orechovich Institute of Biomedical Chemistry RAMS, Moscow, Russia e-mail: veselov@ibmh.msk.su SELECTION OF NEW TARGET PROTEINS FOR DRUG DESIGN IN GENOME OF MYCOBACTERIUM TUBERCULOSISAlexander V. Veselovsky

2. Modern pipeline of new drug development Identify disease Find a drug effective against disease protein (2-5 years)‏ Isolate protein involved in disease (2-5 years)‏ Preclinical testing (1-3 years)‏ Human clinical trials (2-10 years)‏ File IND Formulation & Scale-up File NDA Ability to decreasing finance and time cost +- FDA approval (2-3 years)‏

3. Pipeline of target-based and main steps in drug development

4. Genomics for drug discovery Genome Drug targets selection Annotation and classification of genes

5. Comparative genomics Human genome Gram(+) bacteria genome Genes-targets of bacteria that differ from human genes Gram(-) bacteria genome D.T.Moir et al., 1999

6. Requirements of “Ideal” Antimicrobial Agent and to Its Target

7. Target selection (Comparative genomics)‏ favourable similarity Unfavourable similarity Human genome Genomes of related species Target genome Genomes of human symbiont microorganisms Genomes of other strains of target species Proteins with known spatial structures (PDB)‏ 7

8. GeneMesh – program for protein-targets selection for antimicrobial drug discovery using comparative and functional genomics A.V. Dubanov, A.S. Ivanov, A.I. Archakov (2001) Computer searching of new targets for antimicrobial drugs based on comparative analysis of genomes. Vopr. Med. Khim. 47, 353-367. (in Russian).

9. Algorithm of program GenMesh GenMesh BLAST Set of proteins from PDB Spatial structure ability BLAST Genomes of related species Presence of homologs in genomes of related species BLAST Target genome Absence of mutations in other strains of target species databases BLAST Genomes of other strains of target species Absence of homologs in human genome BLAST Human genome

10. Target selection in Mycobacterium tuberculosis H37Rv using broadened set of genomes for analysis targets for antimycobacterial agents without influencing normal human microflora Common targets for Mycobacteria and fungi

11. Approach Limitation Accuracy* Homology modelling Model and template sequence identity must be > 30% 1-3 A80-95% Threading (Fold recognition) Results heavily dependent on human expertise and information from other methods for elimination decoy folds 3-6 A30-50% Ab initio (De novo)‏ < 150 amino acids 4-8 A< 30% 3D protein structure modelling * - RMSD of C (A) and residues true positions (%)‏

12. Target selection in genome of Mycobacterium tuberculosis H37Rv

13. Potential Targets Found in Genome of M. tuberculosis H37R Freiberg C, Wieland B, Spaltmann F, Ehlert K, Brötz H, Labischinski H.Identification of novel essential Escherichia coli genes conserved among pathogenic bacteria. J Mol Microbiol Biotechnol. 2001 Jul;3(3):483-9. Thanassi JA, Hartman-Neumann SL, Dougherty TJ, Dougherty BA, Pucci MJ. Identification of 113 conserved essential genes using a high-throughput gene disruption system in Streptococcus pneumoniae. Nucleic Acids Res. 2002 Jul 15;30(14):3152-62.

14. Russian Federal Space Agency Program for protein crystallization in weightlessness International space station (ISC)‏

15. Target M. tuberculosis H37R

16. Phosphopantetheine adenylyltransferase of bacteria PPAT 4'-phosphopantetetheine + ATP PPi + 3'dephospho-CoA + Pi Coenzyme A Penultimate and rate-limited enzyme of bacterial coenzyme A biosynthesis

17. Comparison of spatial structures of PPAT M.tuberculosis Active site Green – from Russia (1,6 A)‏ Yellow – 1TFU.pdb (1,99 A)‏

18. Scheme of virtual screening for new PPAT inhibitors in molecular database Molecular database Experimental testing Database preprocessing Manual selection Docking Compounds selection by scoring functions consensus Calculation of additional scoring function

19. Discovery ligands from molecular database by docking method

20. Empirical scoring function The methodis fast ‏ semi-automated is applicable to 3-D models does not need extensive training

21. Accuracy of scoring function

22. Relationship between scoing functions

23. Limitation of scoring functions Srt Receptor Ligand in solution HLW HRW Free energy bound water free rotation Sint G = H-TS loosely associated water molecules free water Entropy HLR Enthalpy SW Svib Receptor-Ligand complex

24. Consensus of scoring functions

25. The first docking of compounds in PPAT active site 17500 complexes

26. Active site of phosphopantetheine adenylyltransferase M.tuberculosis

27. The second docking of compounds in PPAT active site 24000 complexes

28. Experimental testing of selected ligands

29. Acknowledgments. This work was supported in part by Russian Federal Space Agency (in frame of ground preparation of space research). Participants: Institute of Bioorganic Chemistry RAS Institute of Crystallography RAS Institute of Biomedical Chemistry RAMS

30. Thank you for attention!