SELECTION OF NEW TARGET PROTEINS FOR DRUG DESIGN IN GENOME OF MYCOBACTERIUM TUBERCULOSIS Alexander...
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Transcript of SELECTION OF NEW TARGET PROTEINS FOR DRUG DESIGN IN GENOME OF MYCOBACTERIUM TUBERCULOSIS Alexander...
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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
e-mail: [email protected]
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Identify disease
Isolate proteininvolved in disease (2-5 years)
Find a drug effectiveagainst disease protein(2-5 years)
Preclinical testing(1-3 years)
Formulation &Scale-up
Human clinical trials(2-10 years)
FDA approval(2-3 years)
File
IN
D
File
NDA
Modern pipeline of new drug development
Ability to decreasing finance and time cost
+ -
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Pipeline of target-based and main steps in drug development
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Genomics for drug discovery
GenomeGenome
Drug targets Drug targets selectionselection
Annotation and Annotation and classification of genesclassification of genes
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Comparative genomics
Gram(+) bacteria Gram(+) bacteria genomegenome
Gram(-) bacteria Gram(-) bacteria genomegenome
Human genome Human genome
Genes-targets of Genes-targets of bacteria that differ bacteria that differ from human genesfrom human genes
D.T.Moir et al., 1999
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Requirements of “Ideal” Antimicrobial Agent and to Its Target
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Target selection (Comparative genomics)
7
favourable similarityfavourable similarity Unfavourable similarityUnfavourable similarity
Targetgenome
Genomes of related species
Genomes of other strains of target species
Proteins with known spatial structures
(PDB)
Human genome
Genomes of human symbiont microorganisms
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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).
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Algorithm of program GenMesh
databases
Target genome
Genomes of related species
Genomes of other strains of target species
Human genome
Set of proteins from PDB
Spatial structure ability
Presence of homologs in genomes of related species
Absence of mutations in other strains of
target species
Absence of homologs in human genome
BLAST
GenMesh
BLAST
BLAST
BLAST
BLAST
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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
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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 30-50%
Threading (Fold recognition)
1-3 A80-95%
Homology modelling
Accuracy*Approach
* - RMSD of C (A) and residues true positions (%)
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Target selection in genome of Mycobacterium tuberculosis H37Rv
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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.
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Russian Federal Space Agency
International space station (ISC)
Program for protein crystallization in weightlessness
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Target M. tuberculosis H37R
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Phosphopantetheine adenylyltransferase of bacteria
4'-phosphopantetetheine + ATP PPi + 3'dephospho-CoA
PPAT
+ Pi
Coenzyme A
Penultimate and rate-limited enzyme of bacterial
coenzyme A biosynthesis
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Comparison of spatial structures of PPAT M.tuberculosis
Green – from Russia (1,6 A)
Yellow – 1TFU.pdb (1,99 A)
Active site
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Scheme of virtual screening for new PPAT inhibitors in molecular database
Molecular database
Database preprocessing
Docking
Calculation of additional scoring
function
Experimental testing
Manual selection
Compounds selection by scoring functions consensus
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Discovery ligands from molecular database by docking method
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Empirical scoring function
The method is
fast
semi-automated
is applicable to 3-D models
does not need extensive training
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Accuracy of scoring function
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Relationship between scoing functions
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Limitation of scoring functions
Srt
Sint
HLW
HRW
HLR
SW
Svib
G = H-TS
Free energy
Enthalpy
Entropy
Ligand insolution
free rotation
Receptor
bound water
loosely associatedwater molecules
free water
Receptor-Ligand complex
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Consensus of scoring functions
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The first docking of compounds in PPAT active site
17500 complexes
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Active site of phosphopantetheine adenylyltransferase M.tuberculosis
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The second docking of compounds in PPAT active site
24000 complexes
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Experimental testing of selected ligands
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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 RASInstitute of Crystallography RASInstitute of Biomedical Chemistry RAMS
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