Transcript Bioinformatics_DrugDesign

Case Study #1
Use of bioinformatics in drug
development and diagnostics
Ashok Kolaskar
University of Pune, Pune 411 007,
India.
[email protected]
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 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
0
2
4
6
8
Discovery and preclininal testing:
Compounds are identified and evaluated
in laboratory and animal studies for
safety, biological activity, and formulation.
10
Source: Tufts Center for the Study of Drug Development
12
14
Years
16
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
Rational Approach to
Drug Discovery
Identify target
Clone gene encoding target
Express target in recombinant form
Crystal
structures of
target and
target/inhibitor
complexes
Synthesize
modifications
of lead
compounds
Screen
recombinant
target with
available
inhibitors
Identify lead
compounds
Synthesize
modifications
of lead
compounds
Identify lead
compounds
Toxicity &
pharmacokinetic
studies
Preclinical trials
Case study: Malaria
• P. falciparum presents a fascinating model
system. The life-cycle complexity is both a
challenge and an opportunity.
• Unfortunately at the molecular level much
remains unknown.
• Its genome, currently being sequenced, is
already yielding valuable data.
Malarial Drugs
• Up until now, nearly all prophylaxis and
therapeutic intervention has been based on
traditional medicines or their derivatives eg
quinine, paraquine, chloroquine etc.
• Effective also have been type I antifolates eg
sulphones and sulphonamides which mimic
PABA, and the type II antifolates eg
pyrimethamine, trimethoprim and proguanil which
mimic dihydrofolate.
List of Drugs
• Chloroquine
– Avloclor ®; Nivaquine®
• Antifolate
– Pyrimethamine (Daraprim®)
– Proguanil (Paludrine®)
• Combination drugs
– Pyrimethamine & Sulfadoxine combination eg.:
Fansidar ®
Resistance to these drugs poses a threat to
control morbidity and mortality of malaria.
Drug Resistance (DR)
• Mechanisms of drug resistance
– Formation of altered target
• Target has decreased affinity for substrate/analogue
– Decreased access to target
• Mutations decrease membrane permeability
• Specific transport systems altered/deleted
– Increased level of enzymes which cleave substrates
• e.g. Increased expression of -lactamase
• Gene amplification
– Decreased activation of drug
• Drugs require activation by enzymes near site
• Activation pathway suppressed/deleted
Malaria: Novel drug combinations
Structural formula: Atovaquone & Proguanil
Drugs targeting Dihydrofolate reductase
and Dihydroopteroate synthethase
Structural formula of Lumefantrine (Benflumethol)
Artemisinin derivatives as drugs
Structural formula of Arteminsinins
Quinolines
Structural formula of Pyronaridine
Novel drug combinations
Structural formula of Chlorproguanil and Dapsone
Dihydrofolate reductase inhibitors
Structural formula of Pyrimethamine & Cycloguanil
Inhibitors of phospholipid metabolism
Structural formula of E13 and G23
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 of 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
Mosquito
What is Structural Genomics?
• Organise all known proteins into families.
• Determine structures of at least one member
of every family.
• Solve structures of more than 10,000 protein
in next 10 years.
• Generate knowledge and rules from known
protein structures.
• Apply this knowledge to predict the structure
of each and every protein of known
organisms.
Objectives of Structural Genomics
• Selection of Targets for structure
determination to obtain maximum
information return on total efforts
• Develop mechanism that facilitates
cooperation
and
prevent
work
duplication
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%
What is Homology or Comparative
Modeling?
• Comparisons of the tertiary structures of
homologous proteins have shown that 3-D
structures have been better conserved during
evolution than protein primary structures.
• In the absence of experimental data, modelbuilding on the basis of the known three
dimensional
structure
of
homologous
protein(s) is the only reliable method to obtain
structural information.
Difference between Homology and
Similarity
• Homology does not necessarily imply similarity.
• Homology has a precise definition: having a
common evolutionary origin.
• Since homology is a qualitative description of
the relationship, the term “% homology” has no
meaning.
• Supporting data for a homologous relationship
may include sequence or structural similarities,
which can be described in quantitative terms.
Sequence Identity  Model Accuracy
Rate limiting factors in modeling
100%
CPU time to model
75%
Quality of model & Loop modeling
50%
Errors in the sequence alignment
25%
Detection of homology
What is Remote Homology
Modeling?
• Modeling based on low levels of sequence
identity (<30%).
• Has 3 obstacles to overcome:
 the remote homology has to be detected;
 Q and T have to be aligned correctly;
 homology modeling procedure has to be
tailored to the harder problem of extremely
low sequence identity.
Steps in Homology Modeling
 template recognition
 alignment
 backbone generation
 generation of canonical loops (data based)
 side chain generation & optimisation
 model optimisation (energy minimisation)
 model verification
 Optional repeat of previous steps: Generating
more than one model.
STRUCTURE-BASED DRUG
DESIGN
Compound
databases,
Microbial broths,
Plants extracts,
Combinatorial
Libraries
Random
screening
synthesis
3-D ligand
Databases
Docking
Linking or
Binding
Receptor-Ligand
Complex
Lead molecule
3-D QSAR
Target Enzyme
OR Receptor
3-D structure by
Crystallography,
NMR, electron
microscopy OR
Homology Modeling
Testing
Redesign
to improve
affinity,
specificity etc.
Binding Site Analysis
• In the absence of a structure of Targetligand complex, it is not a trivial exercise to
locate the binding site!!!
• This is followed by Lead optimization.
Lead Optimization
Active site
Lead
Lead Optimization
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
• STRAIN IN THE LIGAND ( BOUND)
• STRAIN IN THE PROTEIN
DIFFERENCE BETWEEN AN INHIBITOR AND DRUG
Extra requirement of a drug compared to an inhibitor
•Selectivity
LIPINSKI’S RULE OF FIVE
Poor absorption or permeation are more
•Less Toxicity
likely when :
•Bioavailability
-There are more than five H-bond donors
•Slow Clearance
-The mol.wt is over 500 Da
•Reach The Target -The MlogP is over 4.15(or CLOG P>5)
•Ease Of Synthesis -The sums of N’s and O’s is over 10
•Low Price
•Slow Or No Development Of Resistance
•Stability Upon Storage As Tablet Or Solution
•Pharmacokinetic Parameters
•No Allergies
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.
•Enzymes prefer to bind transition states (reaction intermediates)
and may not optimally bind substrates as part of energy used for
catalysis.
•In contrast, inhibitors are designed to bind with higher affinity:
their affi nities 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,
•It is worthwhile considering what forces drive protein folding –they
share many common features.
•The observed structure of Protein is generally a consequence of the
hydrophobic effect!
•Secondary amides form much stronger H-bonds to water than to other
sec. Amides
hydrophobic collapse
•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:
•The desolvation penalty is paid for by favourable (hydrophobic)
interaction elsewhere in the structure.
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
• Speed vs Reliability
• Manual Interactive Docking
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.
Automated Docking Methods
• 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.
Folate Biosynthetic
pathway
DHFR
CLUSTAL W (1.81) multiple sequence alignment
chabaudi
vinckei
berghei
yoelii
vivax
falciparum
-----------------------E--KAGCFSNKTFKGLGNEGGLPWKCNSVDMKHFSSV
-----------AICACCKVLNSNE--KASCFSNKTFKGLGNAGGLPWKCNSVDMKHFVSV
MEDLSETFDIYAICACCKVLNDDE--KVRCFNNKTFKGIGNAGVLPWKCNLIDMKYFSSV
-----------AICACCKVINNNE--KSGSFNNKTFNGLGNAGMLPWKYNLVDMNYFSSV
MEDLSDVFDIYAICACCKVAPTSEGTKNEPFSPRTFRGLGNKGTLPWKCNSVDMKYFSSV
-------------------------KKNEVFNNYTFRGLGNKGVLPWKCNSLDMKYFCAV
*
*. **.*:** * **** * :**::* :*
35
47
58
47
60
35
chabaudi
vinckei
berghei
yoelii
vivax
falciparum
TSYVNETNYMRLKWKRDRYMEK---------NNVKLNTDGIPSVDKLQNIVVMGKASWES
TSYVNENNYIRLKWKRDKYIKE---------NNVKVNTDGIPSIDKLQNIVVMGKTSWES
TSYINENNYIRLKWKRDKYMEKHNLK-----NNVELNTNIISSTNNLQNIVVMGKKSWES
TSYVNENNYIRLQWKRDKYMGKNNLK-----NNAELNNGELN--NNLQNVVVMGKRNWDS
TTYVDESKYEKLKWKRERYLRMEASQGGGDNTSGGDNTHGGDNADKLQNVVVMGRSSWES
TTYVNESKYEKLKYKRCKYLNKET----------VDNVNDMPNSKKLQNVVVMGRTNWES
*:*::*.:* :*::** :*:
*
.:***:****: .*:*
86
98
113
100
120
85
chabaudi
vinckei
berghei
yoelii
vivax
falciparum
IPSKFKPLQNRINIILSRTLKKEDLAKEYN------NVIIINSVDDLFPILKCIKYYKCF
IPSKFKPLENRINIILSRTLKKENLAKEYS------NVIIIKSVDELFPILKCIKYYKCF
IPKKFKPLQNRINIILSRTLKKEDIVNENN--NENNNVIIIKSVDDLFPILKCTKYYKCF
IPPKFKPLQNRINIILSRTLKKEDIANEDNKNNENGTVMIIKSVDDLFPILKAIKYYKCF
IPKQYKPLPNRINVVLSKTLTKEDVK---------EKVFIIDSIDDLLLLLKKLKYYKCF
IPKKFKPLSNRINVILSRTLKKEDFD---------EDVYIINKVEDLIVLLGKLNYYKCF
** ::*** ****::**:**.**:.
* **..:::*: :*
:*****
140
152
171
160
171
136
chabaudi
vinckei
berghei
yoelii
vivax
falciparum
I----------------------------------------------------------IIGGASVYKEFLDRNLIKKIYFTRINNAYT-----------------------------IIGGSSVYKEFLDRNLIKKIYFTRINNSYNCDVLFPEINENLFKITSISDVYYSNNTTLD
IIGGSYVYKEFLDRNLIKKIYFTRINNSYN-----------------------------IIGGAQVYRECLSRNLIKQIYFTRINGAYPCDVFFPEFDESQFRVTSVSEVYNSKGTTLD
I----------------------------------------------------------*
141
182
231
190
231
137
chabaudi
vinckei
berghei
yoelii
vivax
falciparum
----------------FIIYSKTKE 240
--------FLVYSKVGG 240
---------
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
Prediction & Design of New Drugs
• Prediction of 3-D PfDHFR using bacterial DHFR
and homology modeling approach.
• Search for the compounds using bifunctional basic
groups that could form stable H-bonds in a plane
with carboxyl group.
• Optimize the structure of small molecules and
then dock them on PfDHFR model.
• Toyoda et. al. (1997). BBRC 235:515-519 could
identify two compounds.
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.
Additional Drug Target: glutathione-GR
Glutathione-GR
Additional Drug
Target:
Thioredoxin
reductase (TrxR)
Cancer cell growth appears to
be related to evolutionary
development of plump fruits
and vegetables
• Large tomatoes can evolve from wild, blueberry-size
tomatoes. The genetic mechanism responsible for this is
similar to the one that proliferates cancer cells in
mammalians.
• This is a case where we found a connection between
agricultural research, in how plants make edible fruit
and how humans become susceptible to cancer. That's a
connection nobody could have made in the past.
Cornell University News, July 2000
Size of Tomato Fruit
Single gene, ORFX, that is
responsible for QTL has a
sequence
and
structural
similarity to the human
oncogene c-H-ras p21.
Fruit size alterations, imparted
by fw2.2 alleles, are most likely
to be due to the changes in
regulation rather than in
sequence/structure of protein.
•fw2.2: A Quantitative Trait Locus (QTL) key to the Evolution of Tomato Fruit
Size. Anne Frary (2000) Science, 289: 85-88
Genome Update: Public
domain
• Published Complete Genomes: 59
– Archaeal
9
– Bacterial
36
– Eukaryal
14
• Ongoing Genomes: 335
– Prokaryotic 203
– Eukaryotic 132
Private sector holds
data of more than 100
finished & unfinished
genomes.
Challenges in Post-Genomic era: Unlocking
Secretes of quantitative variation
• For even after genomes have been sequenced
and the functions of most genes revealed, we
will have no better understanding of the
naturally occurring variation that determines
why one person is more disease prone than
another, or why one variety of tomato yields
more fruit than the next.
• Identifying genes like fw2.2 is a critical first
step toward attaining this understanding.
Value of Genome Sequence Data
• Genome sequence data provides, in a rapid
and cost effective manner, the primary
information used by each organism to carry
on all of its life functions.
• This data set constitutes a stable, primary
resource for both basic and applied research.
• This resource is the essential link required to
efficiently utilize the vast amounts of
potentially applicable data and expertise
available in other segments of the biomedical
research community.
Challenges
• Genome databases have individual genes
with
relatively
limited
functional
annotation
(enzymatic
reaction,
structural role)
• Molecular reactions need to be placed in
the context of higher level cellular
functions
The “omics” Series
• Genomics
– Gene identification & charaterisation
• Transcriptomics
– Expression profiles of mRNA
• Proteomics
– functions & interactions of proteins
• Structural Genomics
– Large scale structure determination
• Cellinomics
– Metabolic Pathways
– Cell-cell interactions
• Pharmacogenomics
– Genome-based drug design
Different levels of function
Typically present in
genome database
• Atomic: Binds ATP
• Molecular reaction: adds phosphate
(phosphofructokinase)
• Pathway: Gluconeogenesis
• Network: Energy metabolism
Typically, little informatics support
for making these connections
Data Mining: Finding the Needle in
the Haystack
• Data mining refers to the new genre of BI tools used to
sift through the mass of raw data.
• DM applications should be able to process – TEMPORAL (Time studied) and
– SPATIAL (Organism, organ, cell type etc) data.
– The gained ‘knowledge’ to reprocess data.
– Data using techniques beyond Bayesian (similarity
search) methods.
• An extension of DM is the concept of
‘KNOWLEDGE DISCOVERY’, which open up new
avenues of research with new questions and different
perspectives.
Commercial Structural Genomics
Initiatives
• IBM (Blue Gene project: 2000)
– Computational protein folding
• Geneformatics (1999)
– Modeling for identifying active sites
• Prospect Genomics (1999)
– Homology modeling
• Protein Pathways (1999)
– Phylogenetic profiling, domain analysis, expression
profiling
• Structural Bioinformatics Inc (1996)
– Homology modeling, docking