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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.
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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]
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
Time and the cost involved in a drug discovery process
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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)
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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.
Gurumayum Suraj Sharma
DRUG DESIGN
MOLECULAR DOCKING[MD]
Virtual screening (Structureor ligand based)
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP [QSAR]
Gurumayum Suraj Sharma
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.
Gurumayum Suraj Sharma
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.
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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.
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
SOFTWARES FOR MOLECULAR DOCKING
Gurumayum Suraj Sharma
Drug design software available in public domain
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
� 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.
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
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
Gurumayum Suraj Sharma
� 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
Gurumayum Suraj Sharma
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.
Gurumayum Suraj Sharma
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.
Gurumayum Suraj Sharma
3D-QSAR
ADVANTAGES
Gurumayum Suraj Sharma
Gurumayum Suraj Sharma
Source-
Pevsner J. Bioinformatics & Functional Genomics
Ghosh Z. & Bibekanand M. Bioinformatics: Principles & Applications
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