Role of bioinformatics of drug designing

 “The field of science in which biology, computer science, and information
technology merge into a single discipline. The ultimate goal of the field is to
enable the discovery of new biological insights as well as to create a global
perspective from which unifying principles in biology can be discerned. There
are three important sub- disciplines within bioinformatics: the development
of new algorithms and statistics with which to assess relationships among
members of large data sets; the analysis and interpretation of various types of
data including nucleotide and amino acid sequences, protein domains, and
protein structures; and the development and implementation of tools that
enable efficient access and management of different types of information.
"Education" NCBI, 2003 http://www.ncbi.nlm.nih.gov/Education/index.html
M.Phil, Periyar University

 Drug design or rational drug design, is the discover process of finding new medications based
on the knowledge of a biological target.
 The drug is most commonly an organic small molecule that activates or inhibits the function
of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient
& it is mostly involves the design of molecules that are complementary in shape and charge
to the biomolecular target with which they interact & therefore will bind to it & drug design
frequently but not necessarily relies on computer modeling technique.
 This type of modeling is often referred to as CADD.
 Finally drug design that relies on the knowledge of the 3D-Structure of the biomolecular
target is known as SBDD.
 In addition to small molecules, biopharmaceutical & especially therapeutic antibodies are an
increasingly important class of drugs and computational method for improving the affinity,
selectivity & stability of these protein- based therapeutics have also been developed.

 2 MAJOR TYPES:
1. Ligand – based drug design:
molecules that bind with the target.
Eg., Ritonavir-antiretro viral drug.
2. Structure – based drug design:
3D Structure of molecules.

QSAR
Pharmacophore
mapping
Data base
docking
Score
receptor
denovo
Bioavailability & Toxicity checking
hits

 Quantitative Structure Activity Relationships (QSAR)
◦ Compute functional group in compound
◦ QSAR compute every possible number
◦ Enormous curve fitting to identify drug activity
◦ chemical modifications for synthesis and testing.

 Identify disease
 Isolate protein involved in disease (2-5 years)
 Find a drug effective against disease protein (2-5 years)
 Preclinical testing (1-3 years) Scale-up: using animal studies, formulation;
 Human clinical trails(2-10 years)
 FDA approval (2-3 years)
 Drug.
 Aim:
 The diagnosis- determine the cause of disease.
 Cure- relieve of the symptoms of a disease.
 Migration –action of reducing the severity of a disease.
 Treatment- Medical care.
 Prevention of disease.

Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Preclinical testing
(1-3 years)
Formulation
Human clinical trials
(2-10 years)
Scale-up
FDA approval
(2-3 years)

Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and “personalized” targets
Screening up to 100,000 compounds a
day for activity against a target protein
Using a computer to
predict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing

 “Gene chips” allow us to look
for changes in protein
expression for different
people with a variety of
conditions, and to see if the
presence of drugs changes
that expression
 Makes possible the design of
drugs to target different
phenotypes
compounds administered
people / conditions
e.g. obese, cancer, caucasian
expression profile
(screen for 35,000 genes)

Screening perhaps millions of compounds in a corporate collection to
see if any show activity against a certain disease protein

 Drug companies now have millions of samples of chemical compounds
 High-throughput screening can test 100,000 compounds a day for activity
against a protein target
 Maybe tens of thousands of these compounds will show some activity for
the protein
 The chemist needs to intelligently select the 2 - 3 classes of compounds
that show the most promise for being drugs to follow-up

 Machine Learning Methods
◦ E.g. Neural nets, Bayesian nets, SVMs, Kahonen nets
◦ Train with compounds of known activity
◦ Predict activity of “unknown” compounds
 Scoring methods
◦ Profile compounds based on properties related to target
 Fast Docking
◦ Rapidly “dock” 3D representations of molecules into 3D representations of proteins,
and score according to how well they bind

• 3D Visualization of interactions between compounds and proteins
• “Docking” compounds into proteins computationally

 X-ray crystallography and NMR Spectroscopy can reveal 3D structure
of protein and bound compounds
 Visualization of these “complexes” of proteins and potential drugs can
help scientists understand the mechanism of action of the drug and to
improve the design of a drug
 Visualization uses computational “ball and stick” model of atoms and
bonds, as well as surfaces
 Stereoscopic visualization available

 Traditionally, animals were used for pre-human testing. However,
animal tests are expensive, time consuming and ethically undesirable
 ADME (Absorbtion, Distribution, Metabolism, Excretion) techniques
help model how the drug will likely act in the body
 These methods can be experemental (in vitro) using cellular tissue, or
in silico, using computational models

 Computational methods can predict compound properties important to
ADME, e.g.
◦ LogP, a lipophilicity measure
◦ Solubility
◦ Permeability
◦ Cytochrome p450 metabolism
 Means estimates can be made for millions of compouds, helping
reduce “atrittion” – the failure rate of compounds in late stage

 Millions of entries in databases
◦ CAS : 23 million
◦ GeneBank : 5 million
 Total number of drugs worldwide: 60,000
 Fewer than 500 characterized molecular targets
 Potential targets : 5,000-10,000

• SWISS-PROT: Annotated Sequence Database
• TrEMBL: Database of EMBL nucleotide translated sequences
• InterPro:Integrated resource for protein families, domains
 and functional sites.
• CluSTr:Offers an automatic classification of SWISS-PROT
 and TrEMBL.
• IPI: A non-redundant human proteome set constructed from
 SWISS-PROT, TrEMBL, Ensembl and RefSeq.
• GOA: Provides assignments of gene products to the Gene
 Ontology (GO) resource.
• Proteome Analysis: Statistical and comparative analysis of
 the predicted proteomes of fully sequenced organisms
• Protein Profiles: Tables of SWISS-PROT and TrEMBL entries
 and alignments for the protein families of the Protein Profile.
• IntEnz: The Integrated relational Enzyme database (IntEnz) will
 contain enzyme data approved by the Nomenclature Committee.
 Reference site : www.ebi.ac.uk/Databases/protein.html

• MSD:The Macromolecular Structure Database –
 A relational database representation of clean Protein Data Bank (PDB)
 3DSeq: 3D sequence alignment server- Annotation of the
 alignments between sequence database and the PDB
• FSSP: Based on exhaustive all-against-all 3D structure comparison of
protein structures currently in the Protein Data Bank (PDB)
• DALI: Fold Classification based on Structure-Structure
 Assignments
• 3Dee: Database of protein domain definitions where in the domains have
been clustered on sequence and structural similarity
• NDB: Nucleic Acid Structure Database

Thank you

Role of bioinformatics of drug designing

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