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: veselov@ibmh.msk.su

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

+ -

Pipeline of target-based and main steps in drug development

Genomics for drug discovery

GenomeGenome

Drug targets Drug targets selectionselection

Annotation and Annotation and classification of genesclassification of genes

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

Requirements of “Ideal” Antimicrobial Agent and to Its Target

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

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).

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

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

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 (%)

Target selection in genome of Mycobacterium tuberculosis H37Rv

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.

Russian Federal Space Agency

International space station (ISC)

Program for protein crystallization in weightlessness

Target M. tuberculosis H37R

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

Comparison of spatial structures of PPAT M.tuberculosis

Green – from Russia (1,6 A)

Yellow – 1TFU.pdb (1,99 A)

Active site

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

Discovery ligands from molecular database by docking method

Empirical scoring function

The method is

fast

semi-automated

is applicable to 3-D models

does not need extensive training

Accuracy of scoring function

Relationship between scoing functions

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

Consensus of scoring functions

The first docking of compounds in PPAT active site

17500 complexes

Active site of phosphopantetheine adenylyltransferase M.tuberculosis

The second docking of compounds in PPAT active site

24000 complexes

Experimental testing of selected ligands

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