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Use of bioinformatics in drug development and diagnostics

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Title: Use of bioinformatics in drug development and diagnostics


1
Use of bioinformatics in drug development and
diagnostics
2
Bringing a New Drug to Market
1 compound approved
Review and approval by Food Drug Administration
Phase III Confirms effectiveness and monitors
adverse reactions from long-term use in 1,000
to 5,000 patient volunteers.
Phase II Assesses effectiveness and looks for
side effects in 100 to 500 patient volunteers.
5 compounds enter clinical trials
Phase I Evaluates safety and dosage in 20 to
100 healthy human volunteers.
5,000 compounds evaluated
Discovery and preclininal testing Compounds are
identified and evaluated in laboratory and animal
studies for safety, biological activity, and
formulation.
0
2
4
6
8
10
12
14 Years
16
Source Tufts Center for the Study of Drug
Development

3
Biological Research in 21st Century
  • The new paradigm, now emerging is that all the
    'genes' will be known (in the sense of being
    resident in databases available electronically),
    and that the starting "point of a biological
    investigation will be theoretical.
  • - Walter Gilbert

4
Rational Approach to Drug Discovery
Identify target
Clone gene encoding target
Express target in recombinant form
5
Crystal structures of target and target/inhibitor
complexes
Screen recombinant target with available
inhibitors
Synthesize modifications of lead compounds
Identify lead compounds
6
Synthesize modifications of lead compounds
Identify lead compounds
Toxicity pharmacokinetic studies
Preclinical trials
7
An Ideal Target
  • Is generally an enzyme/receptor in a pathway and
    its inhibition leads to either killing a
    pathogenic organism (Malarial Parasite) or to
    modify some aspects of metabolism of body that is
    functioning dormally.
  • An ideal target
  • Is essential for the survival of the organism.
  • Located at a critical step in the metabolic
    pathway.
  • Makes the organism vulnerable.
  • Concentration of target gene product is low.
  • The enzyme amenable for simple HTS assays

8
How Bioinformatics can help in Target
Identification?
  • Homologous Orthologous genes
  • Gene Order
  • Gene Clusters
  • Molecular Pathways Wire diagrams
  • Gene Ontology
  • Identification of Unique Genes of Parasite as
    potential drug target.

9
Comparative Genomics Malarial Parasites Source
for identification of new target molecules.
  • Genome comparisons of malarial parasites of
    human.
  • Genome comparisons of malarial parasites of human
    and rodent.
  • Comparison of genomes of
  • Human
  • Malarial parasite
  • Mosquito

10
What one should look for?
Human P.f Mosquito
  • Proteins that are shared by
  • All genomes
  • Exclusively by Human P.f.
  • Exclusively by Human Mosquito
  • Exclusively by P.f. Mosquito

Unique proteins in Human P.f. Targets
for anti-malarial drugs
11
Impact of Structural Genomics on Drug Discovery
  • Dry, S. et. al. (2000) Nat. Struc.Biol. 7976-949.

12
Drug Development Flowchart
  • Check if structure is known
  • If unknown, model it using KNOWLEDGE-BASED
    HOMOLOGY MODELING APPROACH.
  • Search for small molecules/ inhibitors
  • Structure-based Drug Design
  • Drug-Protein Interactions
  • Docking

13
Why Modeling?
  • Experimental determination of structure is still
    a time consuming and expensive process.
  • Number of known sequences are more than number of
    known structures.
  • Structure information is essential in
    understanding function.

14
Sequence identities Molecular Modeling methods
  • Methods Sequence Identity with known
    structures
  • ab initio 0-20
  • Fold recognition 20-35
  • Homology Modeling gt35

15
STRUCTURE-BASED DRUG DESIGN
Target Enzyme OR Receptor
Compound databases, Microbial broths, Plants
extracts, Combinatorial Libraries
3-D ligand Databases
3-D structure by Crystallography, NMR, electron
microscopy OR Homology Modeling
Docking Linking or Binding
Random screening synthesis
Receptor-Ligand Complex
Testing
Redesign to improve affinity, specificity etc.
Lead molecule
16
Binding Site Analysis
  • In the absence of a structure of Target-ligand
    complex, it is not a trivial exercise to locate
    the binding site!!!
  • This is followed by Lead optimization.

17
Lead Optimization
Lead
Lead Optimization
Active site
18
Compounds which are weak inhibitors may be
modified by combinatorial chemistry in silico if
the target structure (3-dimensional!) is known,
minimizing the number of potential test compounds
Target structure
Z
N
C
X
Y
H
19
Factors Affecting The Affinity Of A Small
Molecule For A Target Protein
  • LIGAND.wat n PROTEIN.wat n
    LIGAND.PROTEIN.watp(nm-p) wat
  • HYDROGEN BONDING
  • HYDROPHOBIC EFFECT
  • ELECTROSTATIC INTERACTIONS
  • VAN DER WAALS INTERACTIONS

20
DIFFERENCE BETWEEN AN INHIBITOR AND DRUG Extra
requirement of a drug compared to an inhibitor
LIPINSKIS RULE OF FIVE Poor absorption or
permeation are more likely when -There are
more than five H-bond donors -The mol.wt is over
500 Da -The MlogP is over 4.15(or CLOG Pgt5) -The
sums of Ns and Os is over 10
  • Selectivity
  • Less Toxicity
  • Bioavailability
  • Slow Clearance
  • Reach The Target
  • Ease Of Synthesis
  • Low Price
  • Slow Or No Development Of Resistance
  • Stability Upon Storage As Tablet Or Solution
  • Pharmacokinetic Parameters
  • No Allergies

21
Mecanismo antibacteriano de la PZA Pro-droga
22
  • THERMODYNAMICS OF RECEPTOR-LIGAND BINDING
  • Proteins that interact with drugs are typically
    enzymes or receptors.
  • Drug may be classified as substrates/inhibitors
    (for enzymes)
  • agonists/antagonists (for receptors)
  • Ligands for receptors normally bind via a
    non-covalent reversible binding.
  • Enzyme inhibitors have a wide range of
    modesnon-covalent reversible,covalent
    reversible/irreversible or suicide inhibition.
  • Inhibitors are designed to bind with higher
    affinity their affinities often exceed the
    corresponding substrate affinities by several
    orders of magnitude!
  • Agonists are analogous to enzyme substrates part
    of the binding energy may be used for signal
    transduction, inducing a conformation or
    aggregation shift.

23
  • To understand what forces are responsible for
    ligands binding to Receptors/Enzymes,
  • The observed structure of Protein is generally a
    consequence of the hydrophobic effect!
  • Proteins generally bury hydrophobic residues
    inside the core,while exposing hydrophilic
    residues to the exterior Salt-bridges
    inside
  • Ligand building clefts in proteins often expose
    hydrophobic residues to solvent and may contain
    partially desolvated hydrophilic groups that are
    not paired

24
Docking Methods
  • Docking of ligands to proteins is a formidable
    problem since it entails optimization of the 6
    positional degrees of freedom.
  • Rigid vs Flexible
  • Manual Interactive Docking

25
Automated Docking Methods
  • Speed vs Reliability
  • Basic Idea is to fill the active site of the
    Target protein with a set of spheres.
  • Match the centre of these spheres as good as
    possible with the atoms in the database of small
    molecules with known 3-D structures.
  • Examples
  • DOCK, CAVEAT, AUTODOCK, LEGEND, ADAM, LINKOR,
    LUDI.

26
GRID Based Docking Methods
  • Grid Based methods
  • GRID (Goodford, 1985, J. Med. Chem. 28849)
  • GREEN (Tomioka Itai, 1994, J. Comp. Aided. Mol.
    Des. 8347)
  • MCSS (Mirankar Karplus, 1991, Proteins, 1129).
  • Functional groups are placed at regularly spaced
    (0.3-0.5A) lattice points in the active site and
    their interaction energies are evaluated.

27
Folate Biosynthetic pathway
DHFR
28
Multiple alignment of DHFR of Plasmodium species
29
Drug binding pocket of L. casei DHFR
30
Antifolate drugs in the active site of DHFR L.
casei to show hydrogen bonding with surrounding
residues
MTX
PYR
SO3
TMP
31
How molecular modeling could be used in
identifying new leads
  • These two compounds
  • a triazinobenzimidazole
  • a pyridoindole were found to be active with high
    Ki against recombinant wild type DHFR.
  • Thus demonstrate use of molecular modeling in
    malarial drug design.

32
Sitio Activo de la pirazinamidasa
33
Docking P. Horikoshii PZA en presencia de Zn
34
Additional Drug Target glutathione-GR
Glutathione-GR
35
Additional Drug Target Thioredoxin reductase
(TrxR)
36
How Bioinformatics Aids in Vaccine Development /
Peptide Vaccine Development Using Bionformatics
Approaches
37
Emerging and re-emerging infectious diseases
threats, 1980-2001
  • Viral
  • Bolivian hemorrhagic fever-1994,Latin America
  • Bovine spongiform encephalopathy-1986,United
    Kingdom
  • Creulzfeldt-Jackob disease(a new variant
    V-CID)/mad cow disease-1995-96, UK/France
  • Dengue fever-1994-97,Africa/Asia/Latin
    America/USA
  • Ebola virus-1994,Gabon1995,Zaire1996,United
    States(monkey)
  • Hantavirus-1993,United States 1997, Argentina
  • HIV subtype O-1994,Africa
  • Influenza A/Beijing/32/92, A/Wuhan/359/95,
    HSN1-1993,United States 1995,China 1997,
    Hongkong
  • Japanese Encephalitis-1995, Australia
  • Lassa fever-1992,Nigeria
  • Measles-1997, Brazil
  • Monkey pox-1997,Congo
  • Morbillivirus 1994, Australia
  • Onyong-nyong fever-1996,Uganda
  • Polio-1996,Albania
  • Rift Valley fever-1993,Sudan
  • Venezuelan equine encephalitis-1995-96,Venezuela/C
    olombia
  • West Nile Virus-1996,Romania

38
Emerging and re-emerging infectious diseases
threats contd.,
  • Parasitic
  • African trypanosomiasis-1997,Sudan
  • Ancylcostoma caninum(eosinophilic
    enteritis)-1990s,Australia
  • Cryptosporiadiasis-1993,United States
  • Malaria-1995-97,Africa/Asia/Latin America/United
    states
  • Metorchis-1996,Canada
  • Microsporidiosis-Worldwide
  • Fungal
  • Coccidiodomycosis-1993,United States
  • Penicillium marneffi

39
Emerging and re-emerging infectious diseases
threats contd.
  • Bacterial
  • Anthrax-1993,Caribbean
  • Cat scratch disease/Bacillary angiomatosis(Bartone
    lla henseiae)-1900s, USA
  • Chlamydia pneumoniae(Pneumonia/Coronary artery
    disease?)-1990s, USA(discovered 1983)
  • Cholera-1991,Latin America
  • Diphtheria-1993,Former Soviet Union
  • Ehrlichia chaffeensis,Human monocytic
    ahrlichiosis(HME)-United States
  • Ehrlichia phagocytophilia,Human Granulocytic
    ehrlichis(HGE)-United States
  • Escherichia coli O157-1982-1997,United
    States1996,Japan
  • Gonorrhea(drug resistant)-1995,United States
  • Helicobacter pylori(ulcers/cancer_-worldwide(disco
    vered 1983)
  • Leptospirosis-195,Nicaragun
  • Lyme disease(Borrelia burgdorferi)-1990s,United
    states
  • Meningococcal meningitis(serogroup
    A)-1995-1997,West Africa
  • Pertussis-1994,UK/Netherlands1996,USA
  • Plague-1994,India
  • Salmonella typhimurium DT104(drug
    resistant)-1995,USA
  • Staphylococcus aureus(drug resistant)-1997,United
    States/Japan
  • Toxic strep-United States

40
Types of Vaccines
  • Killed virus vaccines
  • Live-attenuated vaccines
  • Recombinant DNA vaccines
  • Genetic vaccines
  • Subunit vaccines
  • Polytope/multi-epitope vaccines
  • Synthetic peptide vaccines

41
Systems with potential use as T-cell vaccines
CD4 T-cell vaccines CD8 T-cell
vaccines Killed microbe Live attenuated
microbe Live attenuated microbe - Synthetic
peptide coupled Synthetic peptide to
protein delivered in liposomes or
ISCOMs Recombinant microbial protein - bearing
CD4 T-cell epitope Chimeric virus
expressing Chimeric virus expressing CD4
T-cell epitope CD8 T-cell epitope Chimeric
Ig Self-molecule expressing CD8 T-cell
epitope Chimeric-peptide-MHC Chimeric
peptide-MHC class II complex Class I
complex Receptor-linked peptide - Naked DNA
expressing Naked DNA expressing CD4 T-cell
epitope CD8 T-cell epitope Abbreviations Ig,
Immunoglobulin, ISCOM, immune-stimulating
complex MHC,Major histocompability complex.
42
Why Synthetic Peptide Vaccines?
  • Chemically well defined, selective and safe.
  • Stable at ambient temperature.
  • No cold chain requirement hence cost effective in
    tropical countries.
  • Simple and standardised production facility.

43
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45
Epitopes
B-cell epitopes
Th-cell epitopes
46
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47
Identified antigens must be checked for strain
varying polymorphisms, these polymorphism must be
represented in a anti-blood stage vaccine
Protective epitope
Variants in strains A B
C D
Candidate protein X
48
Antigenic determinants of Egp of JEV Kolaskar
Tongaonkar approach
49
Peptide vaccines to be launched in near future
  • Foot Mouth Disease Virus (FMDV)
  • Human Immuno Deficiency Virus (HIV)
  • Metastatic Breast Cancer
  • Pancreatic Cancer
  • Melanoma
  • Malaria
  • T.solium cysticercosis

50
Various transformations on side-chain orientation
in a model tetrapeptide
51
Reverse Vaccinology
  • Advantages
  • Fast access to virtually every antigen
  • Non-cultivable can be approached
  • Non abundant antigens can be identified
  • Antigens not expressed in vitro can be
    identified.
  • Non-structural proteins can be used
  • Disadvantages
  • Non proteinous antigens like polysaccharides,
    glycolipids cannot be used.

52
Rappuoli 2001 Curr. Opin. Microbiol.
53
Rappuoli 2001 Curr. Opin. Microbiol.
54
  • Vaccine development
  • In Post-genomic era
  • Reverse Vaccinology
  • Approach.

55
Genome Sequence
Proteomics Technologies
In silico analysis
IVET, STM, DNA microarrays
High throughput Cloning and expression
In vitro and in vivo assays for Vaccine candidate
identification
Global genomic approach to identify new vaccine
candidates
56
In Silico Analysis
Peptide Multitope vaccines
VACCINOME
Candidate Epitope DB
Epitope prediction
Disease related protein DB
Gene/Protein Sequence Database
57
Synthetic Peptide Vaccine Design and
Development of Synthetic Peptide vaccine against
Japanese encephalitis virus
58
Egp of JEV as an Antigen
  • Is a major structural antigen.
  • Responsible for viral haemagglutination.
  • Elicits neutralising antibodies.
  • 500 amino acids long.
  • Structure of extra-cellular domain (399) was
    predicted using knowledge-based homology modeling
    approach.

59
Model Refinement PARAMETERS USED
  • force field AMBER all atom
  • Dielectric const Distance dependent
  • Optimisation Steepest Descents
  • Conjugate Gradients.
  • rms derivative 0.1 kcal/mol/A for SD
  • rms derivative 0.001 kcal/mol/A for CG
  • Biosym from InsightII, MSI and modules therein

60
Model For Solvated Protein
  • Egp of JEV molecule was soaked in the water layer
    of 10A?.
  • 4867 water molecules were added.
  • The system size was increased to 20,648 atoms
    from 6047.

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Model Evaluation II Ramachandran Plot
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65
Peptide Modeling
Initial random conformation Force field
Amber Distance dependent dielectric constant
4rij Geometry optimization Steepest descents
Conjugate gradients Molecular dynamics at 400 K
for 1ns Peptides are SENHGNYSAQVGASQ
NHGNYSAQVGASQ YSAQVGASQ YSAQVGASQAAKFT
NHGNYSAQVGASQAAKFT SENHGNYSAQVGASQAAKFT 149
168
66
Prediction of conformations of the antigenic
peptides
  • Lowest energy Allowed conformations were
    obtained using multiple MD simulations
  • Initial conformation random, allowed
  • Amber force field with distance dependent
    dielectric constant of 4rij
  • Geometry optimization using Steepest descents
    Conjugate gradient
  • 10 cycles of molecular dynamics at 400 K each of
    1ns duration, with an equilibration for 500 ps
  • Conformations captured at 10ps intervals,
    followed by energy minimization of each
  • Analysis of resulting conformations to identify
    the lowest energy, geometrically and
    stereochemically allowed conformations

67
MD simulations of following peptides were carried
out
  • B Cell Epitopes
  • SENHGNYSAQVGASQ
  • NHGNYSAQVGASQ
  • YSAQVGASQ
  • YSAQVGASQAAKFT
  • NHGNYSAQVGASQAAKFT
  • 149 168
  • SENHGNYSAQVGASQAAKFT

T-helper Cell Epitope 436 445 SIGKAVHQVF
  • Chimeric BTh Cell Epitope With Spacer
  • SENHGNYSAQVGASQAAKFTSIGKAVHQVF

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70
Structural comparison of Egps of Nakayama and Sri
Lanka strains of JEV. Single amino acid
differences are highlighted.
71
Ts18 epitope mapping 13-mers window skipping 3
aminoacids
72
Ts18 MHC II epitope profiles for different alleles
73
Ts18 MHC I and MHC II consensus profile
74
Ts18 modeled 3D structure
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