Genome to Future Drug Discovery and Design in Cancer and Infectious

Diseases

Dr. B.K. MalikDr. B.K. Malik

School of Engineering & Technology,School of Engineering & Technology,

Department of BiotechnologyDepartment of Biotechnology

Sharda UniversitySharda University

Greater Noida,UPGreater Noida,UP

DRUG DISCOVERY AND DEVELOPMENT PROCESS CAN BE DIVIDEDINTO SEVERAL PHASES

Safety

Efficacy

Efficacy

2 years 2 years 4 years Appropriate target? Appropriate selectivity? Prediction of Unanticipated

Human drug SS Toxicity responses

Lack of efficacy in some patients

THE PROCESS OF DRUG DISCOVERY AND DRUG DEVELOPMENT. THROUGHOUT THE

PROCESS THERE ARE ISSUES WHICH HAVE SIGNIFICANT IMPLICATIONS FOR THE

SUCCESS OF THE DRUG

Drug metabolism & toxicity assessment

Phase 1

Phase 2

Phase 3

Lead compound Efficacy & selectivity

TO DEVELOP A PHARMACOGENOMIC

DATABASES FOR ETIOLOGY, INDICATION,

PREVELANCE,CHEMOTHERAPEUTIC

INDICATIONS, CONTRAINDICATIONS AND

DRUG-DRUG INTERACTIONS WITH A VIEW

TO ESTABLISH THE ROLE OF

PHARMACOGENETIC POLYMORPHISM IN

DIFFERENTIAL DRUG RESPONSE IN

CANCER AND INFECTIOUS DISEASES

WITH SPECIAL REFERENCE TO THE INDIAN

POPULATION.

DATABASES ADD TO THE KNOWLEDGE OF

DISEASES AND CAN BE USED IN DIFFERENT

WAYS E.g. TO ANALYSE MECHANISMS OR

TO RETRIEVE RETROSPECTIVE AND

PROSPECTIVE INFORMATION ON CLINICAL

PRESENTATION, DISEASE PHENOTYPE,

LONG-TERM PROGNOSIS, AND EFFICACY

OF THERAPEUTIC OPTIONS. THE CLINICAL

INFORMATION MAY BE CRUCIAL FOR THE

DEVELOPMENT OF NEW TREATMENTS

INCLUDING DRUG DESIGN

PREVELANCE OF TUBERCULOSIS

Allergy prevalence is increasing across the World, including India.

Rising Industrialization and pollution are among the factors contributing this increase

Currently asthma prevalence in different parts of India varies between 4% and 20%

The increase in air pollution has been blamed for the rise in the prevalence of asthma

The International study on Asthma and Allergies in Children (ISAAC) has also revealed much higher prevalence in developed countries compared to South-East Asia.

Cancer of the cervix is the most common cancer among Women in developing countries.

Rates for this cancer have been declining in developed countries partly as a result of improved socio-economic circumstances, better access to medical facilities and screening.

Cancer of the cervix is the second most common in Women, comprising 16.6% of all cancers.

It is the most common cancer in black (31.2%) and colored (22.9%)

Second most common in Asian (8.9%) and fourth most common in white Women (2.7%)

MTb SYSBORG

DRUG-ACTIVITY QUERY RESULTS

M Tuberculosis SysBorg

MODELING OF PROTEINS FOR TUBERCULOSIS

TargetTemplate

3D STRUCTURE OF Emb A BY MODELLER

RAMACHANDRAN PLOT

Active Site of the EmbA Protein

Docked Structure of EmbA Protein with UDP-GlcNAc

Emb B PROTEIN MODELING

3D STRUCTURE OF Emb B BY MODELLER

RAMACHANDRAN PLOT

Active Site of the Emb B Protein

Docked Structure of Emb B Protein with UDP-GlcNAc

EMB B MUTATED PROTEIN

3D STRUCTURE OF EMB B PROTEIN BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE EMB B MUTATED PROTEIN

DOCKED STRUCTURE OF EMB B MUTATED PROTEIN WITH UDP-GlcNAc

Emb C Protein Modeling

3D Structure of Emb C By Modeller

TemplateTarget

RAMACHANDRAN PLOT

Active Site of the Emb C Protein

Docked Structure of Emb C Protein with UDP-Glc NAC

MODELING OF PROTEINS FOR DIARRHOEA

3D Structure of Human Elongation Factor 2 kinase By Modeller

TEMPLATE TARGET

RAMACHANDRAN PLOT

Active Site For Elongation Factor-2-kinase Protein

Docked Structure of Human Elongation Factor-2-kinase Protein with STAUROSPORINE

HUMAN AFAMIN PRECURSOR PROTEIN MODELING

3D STRUCTURE OF HUMAN AFAMIN PRECURSOR BY MODELLER

Target Template

RAMACHANDRAN PLOT

ACTIVE SITE OF THE HUMAN AFAMIN PRECURSOR PROTEIN

Docked Structure of Human Afamin Precursor with MALVIDIN (Natural Ligand)

IHA-HUMAN INHIBIN ALPHA CHAIN PRECURSOR-HOMOSAPIENS PROTEIN MODELING

3D STRUCTURE OF IHA-HUMAN INHIBIN ALPHA CHAIN PRECURSOR-HOMOSAPIENS THROUGH MODELLER

RAMACHANDRAN PLOT

Active Site of IHA-HUMAN INHIBIN ALPHA CHAIN PRECURSOR Protein

Docked Structure of Human Inhibin Alpha Chain with LEUTEOLIN (Natural Ligand)

HUMAN LONG CHAIN FATTY ACID-CoA LIGASE 5 PROTEIN MODELING

3D STRUCTURTE OF HUMAN LONG CHAIN FATTY ACID CoA LIGASE 5 BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE HUMAN LONG CHAIN FATTY ACID CoA LIGASE 5

DOCKED STRUCTURE OF HUMAN LONG CHAIN FATTYACID CoA LIGASE 5 PROTEIN WITH

PYRAZINAMIDE

Docked Structure of Human Long Chain fatty acid CoA Ligase 5 Protein with DELPHINIDIN (Natural Ligand)

G-PROTEIN AOUPLED RECEPTRO CHEMR23 PROTEIN MODELING

3D STRUCTURTE OF G-PROTEIN AOUPLED RECEPTRO CHEMR23 PROTEIN BY MODELLER

TARGET TEMPLATE

RAMACHANDRAN PLOT

ACTIVE SITE OF G-PROTEIN AOUPLED RECEPTRO CHEMR23 PROTEIN

DOCKED STRUCTURE OF G-PROTEIN AOUPLED RECEPTRO CHEMR23 PROTEIN WITH AMYLOID

BETA PEPTIDE

Docked Structure of G-PROTEIN AOUPLED RECEPTRO CHEMR23 PROTEIN with CYANIDIN

(Natural Ligand)

MODELLING OF PROTEINS FOR HIV

3D STRUCTURE OF HIV INTEGRASE OBTAINED THROUGH MODELLER

TARGET TEMPALTE

RAMACHANDRAN PLOT

ACTIVE SITE FOR HIV INTEGRASE PROTEIN

DOCKING STRUCTURE OF HIV INTEGRASE PROTEIN WITH FLAVOPIRODOL (Natural Ligand)

DOCKING STRUCTURE OF HIV INTEGRASE PROTEIN WITH QO2793(SMALL PEPTIDE)

MUTATED HIV INTEGRASE

3D STRUCTURE OF MUTATED HIV INTEGRASE

RAMACHANDRAN PLOT

ACTIVE SITE FOR MUTATED HIV INTEGRASE

Docked structure of MUTATED HIV INTEGRASE with FLAVOPIRIDOL (Natural Ligand)

DOCKED STRUCTURE MUTATED HIV INTEGRASE WITH 1QCJB(SMALL PEPTIDE)

GAG-POL POLYPROTEIN

3D STRUCTURE OF GAG-POL POLYPROTEIN BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE GAG-POL POLY PROTEIN

DOCKED STRUCTURE OF GAG-POL POLY PROTEIN WITH RO 31-8959(CHEMICAL LIGAND)

Docked structure of HIV Gag-Pol Polyprotein and 2BDS(SMALL PEPTIDE)

MUTATED GAG-POL POLYPROTEIN

ACTIVE SITE OF THE MUTATED GAG-POL POLY PROTEIN

Docked structure of Mutated HIV Gag-Pol Protein and Abrelix

Docked structure of Mutated HIV Gag-Pol Polyprotein and Q40772(Small Peptide)

MODELING OF PROTEINS FOR NEUROLOGICAL DISORDERS

ACY2_HUMAN ASPARTOACYLASE

3D STRUCTURE OF ACY2_HUMAN ASPARTOACYLASE BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE ACY2_HUMAN ASPARTOACYLASE

DOCKED STRUCTURE OF ACY2_HUMAN ASPARTOACYLASE

WITH D-ASPARTIC ACID

CAH3_HUMAN Carbonic anhydrase Protein Modeling

3D STRUCTURE OF CAH3_HUMAN Carbonic anhydrase Protein BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE CAH3_HUMAN Carbonic anhydrase Protein

DOCKED STRUCTURE OF CAH3_HUMAN Carbonic anhydrase Protein WITH ACETAZOLAMODE

GAMMA-AMINOBUTYRIC ACID(GABA) A RECEPTOR PROTEIN MODELING

3D STRUCTURE OF gamma-aminobutyric acid (GABA) A receptor BY MODELLER

Target Template

RAMACHANDRAN PLOT

ACTIVE SITE OF THE gamma-aminobutyric acid (GABA) A receptor

DOCKED STRUCTURE OF gamma-aminobutyric acid (GABA) A receptor WITH GABACULINE

GLUTAMATE TRANSPORTER

3D STRUCTURE OF GLUTAMATE TRANSPORTER BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE GLUTAMATE TRANSPORTER

DOCKED STRUCTURE OF GLUTAMATE TRANSPORTER WITH LIDOCANE

GBRA6_HUMAN Gamma-aminobutyric-acid receptor alpha-6 subunit precursor

3D STRUCTURE OF GBRA6_HUMAN Gamma-aminobutyric-acid receptor alpha-6 subunit precursor BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE GBRA6_HUMAN Gamma-aminobutyric-acid receptor alpha-6 subunit precursor

DOCKED STRUCTURE OF GBRA6_HUMAN Gamma-aminobutyric-acid receptor alpha-6 subunit precursor WITH

FELBAMATE

MODELING OF PROTEINS FOR BREAST CANCER

TUBULIN BETA-1 CHAIN PROTEIN

3D STRUCTURE OF 2BTOA BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE 2BTOA PROTEIN

DOCKED STRUCTURE OF TUBULIN BETA-1 CHAIN PROTEIN WITH CALPHOSTIN-I (chemsketch)

DOCKED STRUCTURE OF TUBULIN BETA-1 CHAIN PROTEIN WITH 2BDS (small poly-peptide)

MUTATED TUBULIN BETA-1 CHAIN PROTEIN

3D STRUCTURE OF 10FUA BY MODELLER

RAMACHANDRAN PLOT

ACTIVE SITE OF THE 1OFUA PROTEIN

DOCKED STRUCTURE OF MUTATED TUBULIN BETA-1 CHAIN PROTEIN WITH CALPHOSTIN-I (chemsketch)

DOCKED STRUCTURE OF MUTATED TUBULIN BETA-1 CHAIN PROTEIN WITH 2BDS (small poly-peptide)

CONCLUSION

THE DATABASES MODEL PRESENTED AND APPLIED HERE WILL ALSO BE USEFUL FOR THE ANALYSIS OF THE GENES TO BE FOUND TOGETHER ALL THESE DATABASES ADD CONSIDERABLY TO OUR KNOWLEDGE ABOUT MECHANISMOF ACTION, DRUG-DRUG INTERACTION, IMMUNODEFECIENCIES, GENETICS, AND TREATMENT.

PHARMACOGENOMIC DATA BASES TOOLS CAN BE APPLIEDTO DRUG DEVELOPMENT WITH THE AIM TO INCREASE THE EFFICIENCY OF THE PROCESS AND THE QUALITY OF PRODUCT.

IT MAY BE CONCLUDED THAT MODELS OF PROTEINSOF DIFFERENT DISEASES SHALL HELP IN UNDERSTANDING DISEASE PROCESSES AND DRUG DESIGN.