1. SELECTION OF NEW TARGET PROTEINS FOR DRUG DESIGN IN GENOME OF MYCOBACTERIUM TUBERCULOSIS Alexander V. Veselovsky
V.N. Orechovich Institute of Biomedical Chemistry RAMS, Moscow, Russia

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
Annotation and
classification of genes
Drug targets
selection

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, (in Russian).

9. Algorithm of program 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. 3D protein structure modelling
< 150 amino acids
Results heavily dependent on human expertise and information from other methods for elimination decoy folds
Model and template sequence identity must be > 30%
Limitation
4-8 A < 30%
Ab initio (De novo)‏
3-6 A %
Threading (Fold recognition)
1-3 A %
Homology modelling
Accuracy*
Approach
* - 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 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 Jul 15;30(14):

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

21. Accuracy of scoring function21

22. Relationship between scoing functions22

23. Receptor-Ligand complex
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 23

24. Consensus of scoring functions

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

26. Active site of phosphopantetheine adenylyltransferase M.tuberculosis26

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

28. Experimental testing of selected ligands

29. Institute of Bioorganic Chemistry RAS Institute of Crystallography RAS
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 29

30. Thank you for attention!30