Gurumayum Suraj Sharma

APPLICATION OF BIOINFORMATICS

Course Contents-

Structural Bioinformatics In drug discovery

QSAR

Bioinformatics in Agriculture and Microbial Studies

COMPUTER-AIDED DRUG DESIGN

� Computer-Aided Drug Design [CADD]

� A specialized discipline that uses computational

methods to simulate drug-receptor interactions.

� CADD methods are heavily dependent on bioinformatics

tools, applications and databases.

� As such, there is considerable overlap in CADD

research and bioinformatics.

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STRUCTURAL BIOINFORMATICS IN

DRUG DISCOVERY

STRUCTURAL BIOINFORMATICS

Structural Bioinformatics [SBI]- A subset of bioinformatics

concerned with the use of biological structures- proteins,

DNA, RNA, ligands etc. and complexes thereof to further

our understanding of biological systems

SBI In Drug Design And Discovery

� SBI can be used to examine:

o Drug targets (usually proteins)

o Binding of Ligands

↓

“Rational” drug design

Benefits: Saves Time & Money

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TRADITIONAL DRUG DISCOVERY METHODS

Natural (plant-derived) treatment

for illness/ailments

↓

Isolation of active compound

[Small, organic]

Aspirin

Synthesis of compound

↓

Manipulation of structure to get better

drug

[Greater efficacy, Fewer side effects]

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MODERN METHODS OF DRUG DISCOVERY� Drug discovery process begins with a disease (rather than a

treatment)

� Use disease model to pinpoint relevant genetic/biological

components (i.e. possible drug targets)

DISEASE → Genetic/Biological target

↓

Discovery of a “LEAD” molecule

oDesign assay to measure function of target

o Use assay to look for modulators of

target’s function

↓

High Throughput Screen [HTS]

o To identify “hits” (compounds with binding

in low nM to low µM range)Gurumayum Suraj Sharma

MODERN DRUG DISCOVERY

Small Molecule Hits

↓

Manipulate Structure to Increase Potency

i.e. decrease Ki to low nM affinity

↓

Optimization of lead molecule into candidate drug

Fulfillment of required Pharmacological properties

[Potency, absorption, bioavailability, metabolism, safety]

↓

Clinical Trials

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Hits Identification

Pre-clinical Studies

Manufacturing

Clinical Trials

Regulatory

Evaluation Stage

Marketing Stage

If C

om

po

un

d L

ack

s

Eff

icie

ncy

An

d I

s In

feri

or

Screening of Potential Drug

from Compound Libraries

Lead Identification &

Optimization

Production

Checking of Compound’s

Efficacy & Superiority

Drug registration

& Licensing

Commercial Launch

Drug Discovery StepsGurumayum Suraj Sharma

Hits Identification

o Screening of Compound Libraries with validated assays after

successful target evaluation and validation.

o Establish compounds with arbitrarily established potency.

o Involves screening of 10,000s of compounds as potential new

drug.

o Only few may be worthy and selected for further studies.

Pre-clinical Studies

o Identified HITS advance through Lead Identification &

Optimization to identify candidate with desired efficacy.

Manufacturing

o Production in pilot-scale batches done to support Pre-clinical

studies.

o Increased to meet larger requirement for clinical trials.

o May further be scaled up if it meet expectation, for

commercial supply

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Clinical Trial Stage

o Probable Drug candidate may fail at this stage for several

reasons

o If the compound lacks efficacy or is inferior to available

products, it would be terminated.

o Project returns to HITS Identification stage

Regulatory Evaluation Stage

o Drug Registration & Licensing- Lengthy & Costly Process

o Depends on Medical & Regulatory Mechanisms

o Which may change from time to time

o Insufficient or Inappropriate Data may lead to failure

Marketing Stage

oAfter commercial launch, the product is subject to ongoing

post-market surveillance by regulatory agencies

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Time and the cost involved in a drug discovery process

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IMPACT OF SBI ON DRUG DISCOVERY

Genome Gene Protein HTS Hit Lead Candidate Drug

Genomics

Bioinformatics

Structural Bioinformatics

Chemoinformatics

Structure-based Drug Design

ADMET Modelling

� Speeds up key steps in Drug

Design [DD] process by

combining aspects of

Bioinformatics, Structural

Biology & Structure-based

drug design

Bio-

informatics

Structure-based

Drug Design

Structural

Biology

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IDENTIFYING TARGETS

The “Druggable Genome”

� The target to which the drug is likely to bind to fight the

causative factor responsible for the disease

� Usually chosen to be a PROTEIN.

� Polysaccharides, Lipids Nucleic Acids are eliminated-

� Due to problems with toxicity, specificity & difficulty in

creating potent inhibitors

Human Genome

Polysaccharides Lipids Nucleic acids Proteins

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Human Genome

Polysaccharides Lipids Nucleic acids Proteins

Proteins with

binding site

“Druggable Genome”

� Subset Of Genes Which Express Proteins Capable Of

Binding Small Drug-like Molecules

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GPCR

STY kinases

Zinc peptidases

Serine

proteases

PDE

Other 110

families

Cys proteases

Gated ion-

channelIon channels

Nuclear

receptor

P450 enzymes

RELATING DRUGGABLE TARGETS TO DISEASEAnalysis of pharm industry

reveals:

oOver 400 proteins

used as drug targets

o Sequence analysis of

these proteins shows

that most targets fall

within a few major

gene families

(GPCRs, kinases,

proteases and

peptidases)

More than 60% of the drug targets are membrane receptor proteins & enzymes

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Some enzymes as drug targets and drugs developed

Enzymes Drugs

Cyclooxygenase Aspirin

Angiotensin converting enzyme Captopril

Dihydrofolate reductase Methotrexate

HIV protease Saquinavir

Xanthine Oxidase Allopurinol

Carbonic anhydrase Acetazolamide

Reverse Transcriptase AZT( Retrovir)

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ASSESSING TARGET DRUGGABILITY� Once a target is defined for a disease of interest, SBI can help

answer the question:

� Is this a “druggable” target?

oDoes it have sequence/domains similar to known

targets?

oDoes the target have a site where a drug can bind, and

with appropriate affinity?

OTHER ROLES FOR SBI IN DD

� Binding pocket modeling

� Lead identification

� Similarity with known proteins or ligands

� Chemical library design / combinatorial chemistry

� Virtual screening

� Lead optimization

� Binding

� ADMET Gurumayum Suraj Sharma

LEAD COMPOUND

� A drug originally discovered by different sources such as

natural products, HTS, serendipity & endogenous substrates� A molecule that serves as the starting point for optimization

involving many small molecules that are closely related in

structure to the lead compound.

� Potential compounds modeled computationally to estimate

their “FIT” to the target.

� Structural & Functional Interactions

� Steric Fit

� H-bonding

� Hydrophobic interaction

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In silico Drug Design

Computational Method that is used to study:

I. The docking between a drug & its receptor

→ Molecular Dynamics [MD]

II. The Energy of Big Protein

→ Molecular Mechanics

III. The Heat of Formation of Drug

→ A Semi-empirical Method

IV. The Charges of Small Molecules

→ An ab initio or a Density Functional

Theory [DFT]

Ab initio method [For Protein Modelling]

Ab initio [de novo] protein modeling is a database independent approach

based exploring the physical properties of amino acids rather than

previously solved structure.

Ab-initio modeling takes into consideration that a protein native structure

has minimum global free energy.Gurumayum Suraj Sharma

DATABASE

Small Molecules

3D Structures

Partial Charges

3D Models of

Target Proteins

High Throughput Screening [HTS] in silico

Automated Docking [AutoDock]

1. Complex Receptor:Ligand-

Where/How does the Ligand bind?

2. Energy Scoring-

How strong does it bind?

AutoTors AutoGrids PDB

Structures

Modelling

Modeller

1. Primary

sequence of

Protein with

unknown

structure

2. Sequence search

for homologous

sequence

3. Alignment with

templates

In silico Drug Design using AutoDockGurumayum Suraj Sharma

1. Homology Modelling:

o Based on the seasonable assumption that two homologous proteins

will share very similar structure.

o Given the amino acid sequence of an unknown structure & the

solved structure of the homologous protein, each amino acid in the

solved structure is mutated [computationally], into the corresponding

amino acid from the unknown structure

2. Protein Threading:

o If two sequences show no detectable sequence similarity, threading

or fold recognition is employed to model a protein.

o Threading predicts the structure for a protein by matching its

sequence to each member of a library of known folds and seeing if

there is a statistically significant fit with any of them.

COMPARATIVE PROTEIN MODELLING

� Uses previously solved structure as starting points or templates.

� Effective- Since it appears that although no. of actual proteins is

vast, there is limited set of tertiary structural motifs to which most

proteins belong.

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DRUG DESIGN

MOLECULAR DOCKING[MD]

Virtual screening (Structureor ligand based)

QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP [QSAR]

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VIRTUAL SCREENING[Structure-based Approach]

� To identify potential lead

compounds from large dataset

� Known structures of organic

compounds

� Libraries of Virtual Compounds

� Programs calculate affinity for

protein

� Narrow down to small number of

possibilities

� Surface Representation that

efficiently represents the docking

surface & identifies the regions of

interest [cavity & protrusions]

� Surface Matching that matches

surfaces to optimize a binding scoreGurumayum Suraj Sharma

Virtual screening pipeline Gurumayum Suraj Sharma

DOCKING� DOCKING-

� Method which predicts the

preferred orientation of one

molecule to a second

when bound to each other to form

a stable complex.

� Associations between

biologically relevant molecules

such as proteins, nucleic

acids, carbohydrates,

and lipids play a central role

in signal transduction.

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MOLECULAR DOCKING� Molecular docking

�One of the most frequently used methods in structure-

based drug design

� Due to its ability to predict the binding-conformation

of small molecule ligands to appropriate target binding

site.

� Characterisation of binding behaviour plays an important

role in rational design of drugs

� As well as to elucidate fundamental biochemical

processes.

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Docking can be done between

I. Protein-Ligand

II. Protein-Protein

III. Protein-Nucleotide

� Protein may serve as the

“LOCK” & Ligand as

the “KEY”

� Each ligand has particular

binding site to its protein

partner

LIGANDPROTEIN

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MOLECULAR DOCKING: IMPORTANCE

� It is the key to Rational Drug Design

� The results of docking can be used to find inhibitors for

specific target proteins

� Thus to design new drugs

� In addition to new drugs discovery, it is of extreme

relevance in cellular biology.

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MOLECULAR DOCKING: APPLICATIONS

� Virtual Screening [Hit Identification]

� Docking with a scoring function can be used to quickly

screen large database of potential drugs in silico to identify

molecules that are likely to bind to protein target of interest.

� Bioremediation

� Protein ligand docking can also be used to predict pollutants

that can be degraded by enzymes

� To study geometry of a particular complex [Rational Design of

Drugs]

� Identification of ligand’s correct binding geometry in binding

site.

� Prediction of affinity binding

� For predicting protein-protein interactions

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SOFTWARES FOR MOLECULAR DOCKING

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Drug design software available in public domain

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SAR [STRUCTURE ACTIVITY RELATIONSHIP]

� Traditional practices of medicinal chemistry which try to

modify the effect or the potency (i.e. activity) of bioactive

chemical compounds by modifying their chemical structure.

� Medicinal chemists use the techniques of chemical synthesis to

insert new chemical groups into the biomedical compound and

test the modifications for their biological effects.

� Enables identification & determination of chemical groups

responsible for evoking a target biological effect in organism.

� Basic assumption for all molecule based hypotheses is that

similar molecules have similar activities.

Predicting Biological Activity From Structure

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� The underlying problem is therefore how to define a small

difference on a molecular level, since each kind of activity [e.g.

reaction ability, biotransformation ability, solubility, target

activity], might depend on another difference.

� In general, one is more interested in finding strong trends.

� Created hypotheses usually rely on a finite number of

chemical data.

� Thus, the induction principle should be respected to avoid

overfitted hypotheses and deriving overfitted and useless

interpretations on structural/molecular data.

� The SAR paradox refers to the fact that it is not the case that

all similar molecules have similar activities.

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STRUCTURE ACTIVITY RELATIONSHIP [SAR]� Drug design is an iterative process which begins with a

compound that displays an interesting biological profile and ends

with optimizing both the activity profile for the molecule and its

chemical synthesis.

� The process is initiated when the chemist conceives a

hypothesis which relates the chemical features of the molecule

(or series of molecules) to the biological activity.

� Without a detailed understanding of the biochemical processes

responsible for activity, the hypothesis generally is refined by

examining structural similarities and differences for active and

inactive molecules.

� Compounds are selected for synthesis which maximize the

presence of functional groups or features believed to be

responsible for activity.

� A Quantitative Structure Activity Relationship (QSAR) can

then be utilized to help guide chemical synthesis..Gurumayum Suraj Sharma

� SAR Method later refined to build mathematical relationships

between a chemical structure and its biological activity, known as

Quantitative Structure-Activity Relationships (QSAR).

� Process by which chemical structure is quantitatively correlated

with a well defined process, such as biological activity or chemical

reactivity.

� Biological activity can be expressed quantitatively as in the

concentration of a substance required to give a certain biological

response.

� Additionally, when physico-chemical properties or structures are

expressed by numbers, one can form a mathematical relationship, or

quantitative structure activity relationship, between the two.

� Mathematical expression can then be used to predict biological

response of other chemical structures.

QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP

[QSAR]

Sometimes QSPR: Quantitative Structure-property Relationship

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QSAR� QSAR represent an attempt to correlate structural or property

descriptors of compounds with activities.

� These physicochemical descriptors, which include parameters to

account for hydrophobicity, topology, electronic properties, &

steric effects

� Determined empirically or, more recently, by computational

methods.

� Activities used in QSAR include chemical measurements &

biological assays.

� QSAR currently are being applied in many disciplines, with

many pertaining to DRUG DESIGN and ENVIRONMENTAL

RISK ASSESSMENT

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� To relate the biological activity of a series of compounds to

their physicochemical parameters in a quantitative fashion

using a mathematical formula

Requirements

� Quantitative measurements for biological and

physicochemical properties-

�� HydrophobicityHydrophobicity ofof thethe moleculemolecule

�� HydrophobicityHydrophobicity ofof substituentsubstituent

�� ElectronicElectronic propertiesproperties ofof substituentsubstituent

�� StericSteric propertiesproperties ofof substituentsubstituent

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3D-QSAR� Structural descriptors are of immense importance in every QSAR

model.

� Common structural descriptors are pharmacophores and

molecular fields.

� Superimposition of the molecules is necessary.

� PHARMACOPHORE

� An abstract description of molecular features that are

necessary for molecular recognition of a ligand by a

biological macromolecule.

� A pharmacophore model explains how structurally diverse

ligands can bind to a common receptor site.

� Pharmacophore models can be used to identify through de

novo design or virtual screening novel ligands that will bind

to the same receptor.

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3D-QSAR

ASSUMPTIONS

� The effect is produced by modeled compound and not it’smetabolites.

� The proposed conformation is the bioactive one.

� The binding site is the same for all modeled compounds.

� The biological activity is largely explained by enthalpicprocesses.

�Entropic terms are similar for all the compounds.

� The system is considered to be at equilibrium, and kineticsaspects are usually not considered.

� Pharmacokinetics:

� Solvent effects, diffusion, transport are not included.

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3D-QSAR

ADVANTAGES

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Source-

Pevsner J. Bioinformatics & Functional Genomics

Ghosh Z. & Bibekanand M. Bioinformatics: Principles & Applications