Use of bioinformatics in drug development and diagnostics

Bringing a New Drug to Market

Review and approval by Food & Drug Administration

1 compound approved

Phase III: Confirms effectiveness and monitors adverse reactions from long-term use in 1,000 to5,000 patient volunteers.

Phase II: Assesses effectiveness and looks for side effects in 100 to 500 patient volunteers.

Phase I: Evaluates safety and dosage in 20 to 100 healthy human volunteers.

5 compounds enter clinical trials

Discovery and preclininal testing: Compounds are identified and evaluated in laboratory and animal studies for safety, biological activity, and formulation.

5,000 compounds evaluated

0 2 4 6 8 10 12 14 Years

16

Source: Tufts Center for the Study of Drug Development

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

Identify target

Clone gene encoding target

Rational Approach to Drug Discovery

Express target in recombinant form

Synthesize modifications of lead compounds

Identify lead compounds

Screen recombinant target with available inhibitors

Crystal structures of target and target/inhibitor complexes

Preclinical trials

Identify lead compounds

Toxicity & pharmacokinetic

studies

Synthesize modifications of lead compounds

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

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.

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

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

Impact of Structural Genomics on Drug Discovery

Dry, S. et. al. (2000) Nat. Struc.Biol. 7:976-949.

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

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.

Sequence identities & Molecular Modeling methods

Methods Sequence Identity with known

structures

• ab initio 0-20%

• Fold recognition 20-35%

• Homology Modeling >35%

STRUCTURE-BASED DRUG DESIGN

Compound databases,

Microbial broths,Plants extracts,Combinatorial

Libraries

3-D ligand Databases

DockingLinking orBinding

Receptor-LigandComplex

Randomscreening synthesis

Lead molecule

Target EnzymeOR Receptor

3-D structure by Crystallography,NMR, electron microscopy OR

Homology Modeling

Redesign to improve

affinity, specificity etc.

Testing

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.

Lead Optimization

Lead Lead OptimizationActive site

Compounds which are weak inhibitors may be modifiedby combinatorial chemistry in silico if the target structure(3-dimensional!) is known, minimizing the number of potential test compounds

N

H

CX

Y

Z

Target structure

Factors Affecting The Affinity Of A Small Molecule For A Target Protein

LIGAND.wat n +PROTEIN.wat n LIGAND.PROTEIN.watp+(n+m-p) wat

• HYDROGEN BONDING

• HYDROPHOBIC EFFECT

• ELECTROSTATIC INTERACTIONS

• VAN DER WAALS INTERACTIONS

DIFFERENCE BETWEEN AN INHIBITOR AND DRUG

Extra requirement of a drug compared to an inhibitor

LIPINSKI’S RULE OF FIVEPoor 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 P>5)-The sums of N’s and O’s 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

Mecanismo antibacteriano de la PZA: Pro-droga

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 modes:non-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.

•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:

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

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.

GRID Based Docking Methods

• Grid Based methods– GRID (Goodford, 1985, J. Med. Chem. 28:849)– GREEN (Tomioka & Itai, 1994, J. Comp.

Aided. Mol. Des. 8:347)– MCSS (Mirankar & Karplus, 1991, Proteins,

11:29).• Functional groups are placed at regularly spaced

(0.3-0.5A) lattice points in the active site and their interaction energies are evaluated.

Folate Biosynthetic pathway

DHFR

CLUSTAL W (1.81) multiple sequence alignment chabaudi -----------------------E--KAGCFSNKTFKGLGNEGGLPWKCNSVDMKHFSSV 35 vinckei -----------AICACCKVLNSNE--KASCFSNKTFKGLGNAGGLPWKCNSVDMKHFVSV 47 berghei MEDLSETFDIYAICACCKVLNDDE--KVRCFNNKTFKGIGNAGVLPWKCNLIDMKYFSSV 58 yoelii -----------AICACCKVINNNE--KSGSFNNKTFNGLGNAGMLPWKYNLVDMNYFSSV 47 vivax MEDLSDVFDIYAICACCKVAPTSEGTKNEPFSPRTFRGLGNKGTLPWKCNSVDMKYFSSV 60 falciparum -------------------------KKNEVFNNYTFRGLGNKGVLPWKCNSLDMKYFCAV 35 * *. **.*:** * **** * :**::* :* chabaudi TSYVNETNYMRLKWKRDRYMEK---------NNVKLNTDGIPSVDKLQNIVVMGKASWES 86 vinckei TSYVNENNYIRLKWKRDKYIKE---------NNVKVNTDGIPSIDKLQNIVVMGKTSWES 98 berghei TSYINENNYIRLKWKRDKYMEKHNLK-----NNVELNTNIISSTNNLQNIVVMGKKSWES 113 yoelii TSYVNENNYIRLQWKRDKYMGKNNLK-----NNAELNNGELN--NNLQNVVVMGKRNWDS 100 vivax TTYVDESKYEKLKWKRERYLRMEASQGGGDNTSGGDNTHGGDNADKLQNVVVMGRSSWES 120 falciparum TTYVNESKYEKLKYKRCKYLNKET----------VDNVNDMPNSKKLQNVVVMGRTNWES 85 *:*::*.:* :*::** :*: * .:***:****: .*:* chabaudi IPSKFKPLQNRINIILSRTLKKEDLAKEYN------NVIIINSVDDLFPILKCIKYYKCF 140 vinckei IPSKFKPLENRINIILSRTLKKENLAKEYS------NVIIIKSVDELFPILKCIKYYKCF 152 berghei IPKKFKPLQNRINIILSRTLKKEDIVNENN--NENNNVIIIKSVDDLFPILKCTKYYKCF 171 yoelii IPPKFKPLQNRINIILSRTLKKEDIANEDNKNNENGTVMIIKSVDDLFPILKAIKYYKCF 160 vivax IPKQYKPLPNRINVVLSKTLTKEDVK---------EKVFIIDSIDDLLLLLKKLKYYKCF 171 falciparum IPKKFKPLSNRINVILSRTLKKEDFD---------EDVYIINKVEDLIVLLGKLNYYKCF 136 ** ::*** ****::**:**.**:. * **..:::*: :* :***** chabaudi I----------------------------------------------------------- 141 vinckei IIGGASVYKEFLDRNLIKKIYFTRINNAYT------------------------------ 182 berghei IIGGSSVYKEFLDRNLIKKIYFTRINNSYNCDVLFPEINENLFKITSISDVYYSNNTTLD 231 yoelii IIGGSYVYKEFLDRNLIKKIYFTRINNSYN------------------------------ 190 vivax IIGGAQVYRECLSRNLIKQIYFTRINGAYPCDVFFPEFDESQFRVTSVSEVYNSKGTTLD 231 falciparum I----------------------------------------------------------- 137 * chabaudi --------- vinckei --------- berghei FIIYSKTKE 240 yoelii --------- vivax FLVYSKVGG 240 falciparum ---------

Multiple alignment of DHFR of Plasmodium species

Drug binding pocket of L. casei DHFR

Antifolate drugs in the active site of DHFR L. casei to show hydrogen bonding with

surrounding residues

MTX

TMP

PYR

SO3

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.

Sitio Activo de la pirazinamidasa

Docking P. Horikoshii – PZA en presencia de Zn

Additional Drug Target: glutathione-GR

Glutathione-GR

Additional Drug Target:

Thioredoxin reductase (TrxR)

How Bioinformatics Aids in Vaccine Development / Peptide

Vaccine Development Using

Bionformatics Approaches

Emerging and re-emerging infectious diseases threats, 1980-2001Viral

- 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,Gabon;1995,Zaire;1996,United States(monkey)- Hantavirus-1993,United States; 1997, Argentina- HIV subtype O-1994,Africa- Influenza A/Beijing/32/92, A/Wuhan/359/95, HS:N1-1993,United States; 1995,China; 1997,

Hongkong- Japanese Encephalitis-1995, Australia- Lassa fever-1992,Nigeria- Measles-1997, Brazil- Monkey pox-1997,Congo- Morbillivirus – 1994, Australia- O’nyong-nyong fever-1996,Uganda- Polio-1996,Albania- Rift Valley fever-1993,Sudan- Venezuelan equine encephalitis-1995-96,Venezuela/Colombia- West Nile Virus-1996,Romania- Yellow fever-1993,Kenya;1995,Peru

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

Emerging and re-emerging infectious diseases threats contd.• Bacterial– Anthrax-1993,Caribbean

– Cat scratch disease/Bacillary angiomatosis(Bartonella 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 States;1996,Japan– Gonorrhea(drug resistant)-1995,United States– Helicobacter pylori(ulcers/cancer_-worldwide(discovered 1983)– Leptospirosis-195,Nicaragun– Lyme disease(Borrelia burgdorferi)-1990s,United states– Meningococcal meningitis(serogroup A)-1995-1997,West Africa– Pertussis-1994,UK/Netherlands;1996,USA– Plague-1994,India– Salmonella typhimurium DT104(drug resistant)-1995,USA– Staphylococcus aureus(drug resistant)-1997,United States/Japan– Toxic strep-United States – Trench fever(Barnionella quintana)-1990s,United States– Tuberculosis(highly transmissible)-1995,United states– Vibrio cholerae 0139-1992,Southern Asia

Types of Vaccines

• Killed virus vaccines

• Live-attenuated vaccines

• Recombinant DNA vaccines

• Genetic vaccines

• Subunit vaccines

• Polytope/multi-epitope vaccines

• Synthetic peptide vaccines

Systems with potential use as T-cell vaccines

CD4 + T-cell vaccines CD8+ T-cell vaccinesKilled microbe Live attenuated microbe

Live attenuated microbe -

Synthetic peptide coupled Synthetic peptide to protein delivered in liposomes

or ISCOMsRecombinant 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-MHCclass 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.

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.

What Are Epitopes?

Antigenic determinants or Epitopes are the portions of the antigen molecules which are responsible for specificity of the antigens in antigen-antibody (Ag-Ab) reactions and that combine with the antigen binding site of Ab, to which they are complementary.

Epitopes could be -

contiguous (when Ab binds to a contiguous sequence of amino acids)

non-contiguous (when Ab binds to non-contiguous residues, brought together by folding).

Sequential epitopes are contiguous epitopes.

Conformational epitopes are non-contiguous antigenic determinants.

Epitopes …

B-cell epitopes Th-cell epitopes

Properties of Amino Acids: predictors for Epitopes

Sequential epitope prediction methods Theoretical methods are based on properties of amino acids and their propensity scales. Hopp & Woods, 1981. Parker et al., 1986 Kolaskar & Tongaonkar, 1990.

The accuracy of prediction: 50-75%. Conformational epitope prediction method Kolaskar & Kulkarni-Kale, 1999.

Identified antigens must be checked for strain varyingpolymorphisms, these polymorphism must be representedin a anti-blood stage vaccine

Candidate protein X

Variants in strains A B C D

Protectiveepitope

Antigenic determinants of Egp of JEV Kolaskar & Tongaonkar approach

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 *

Various transformations on side-chain orientation in a model tetrapeptide

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.

Rappuoli 2001Curr. Opin. Microbiol.

Rappuoli 2001Curr. Opin. Microbiol.

Vaccine developmentIn Post-genomic era: Reverse Vaccinology Approach.

Genome SequenceGenome Sequence

Proteomics TechnologiesProteomics

TechnologiesIn silico analysisIn silico analysis

IVET, STM, DNAmicroarrays

High throughputCloning and expression

High throughputCloning and expression

In vitro and in vivo assays forVaccine candidate identification

In vitro and in vivo assays forVaccine candidate identification

Global genomic approach to identify new vaccine candidates

In Silico Analysis

Gene/Protein Sequence Database

Disease related protein DB

Candidate Epitope DB

VACCINOME

PeptideMultitope vaccines

Epitope prediction

Synthetic Peptide Vaccine

Design and Development of Synthetic Peptide vaccine

against Japanese encephalitis virus

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.

Model RefinementPARAMETERS 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

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.

Model Evaluation II: Ramachandran Plot

An Algorithm to Identify Conformational Epitopes

Calculate the percent accessible surface

area (ASA) of the amino acid residues.

If ASA 30%, then residue was termed as accessible residues.

A contiguous stretch of more than three accessible residues was termed as the antigenic determinant.

…Cont. A determinant is extended to N- and C-

terminals, only if, accessible amino acid(s) are present after an inaccessible amino acid residue.

A list of sequential antigenic

determinants was prepared.

Peptide ModelingInitial random conformationForce field: AmberDistance dependent dielectric constant 4rij

Geometry optimization: Steepest descents & Conjugate gradientsMolecular dynamics at 400 K for 1nsPeptides are:

SENHGNYSAQVGASQ NHGNYSAQVGASQ YSAQVGASQ

YSAQVGASQAAKFT NHGNYSAQVGASQAAKFTSENHGNYSAQVGASQAAKFT149 168

Lowest energy Allowed conformations were obtained using multiple MD simulations:

– Initial conformation: random, allowed

– Amber force field with distance dependent dielectric constant of 4*rij

– 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

Prediction of conformations of the antigenic peptides

B Cell Epitopes:

SENHGNYSAQVGASQ NHGNYSAQVGASQ YSAQVGASQ YSAQVGASQAAKFT

NHGNYSAQVGASQAAKFT149

168SENHGNYSAQVGASQAAKFT

Chimeric B+Th Cell Epitope With Spacer:

SENHGNYSAQVGASQAAKFTSIGKAVHQVF

T-helper Cell Epitope:

436 445SIGKAVHQVF

MD simulations of following peptides were carried out

Structural comparison of Egps of Nakayama and Sri Lanka strains of JEV.

Single amino acid differences are highlighted.

1 3 5 7 9 11 13 15 17 19

A65

0

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1 3 5 7 9 11 13 15 17 19A

650

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A65

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1.6

Ts18 epitope mapping

13-mers window skipping 3 aminoacids

Ts18 MHC II epitope profiles for different alleles

Ts18 MHC I and MHC II consensus profile

0

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1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73

Ts18 modeled 3D structure