Download Computational Pharmacology

Computational Pharmacology
Judith Klein-Seetharaman
Assistant Professor, Department of Pharmacology
High Points of the Case Study: The
Development of Cox-2 Inhibitors
• High points regarding the success of the
drugs…
• High points regarding drug discovery
principles…
High Points of the Case Study: The
Development of Cox-2 Inhibitors
• High points regarding the success of the drugs:
– Isoforms with differential properties
– Differential expression
• High points regarding drug discovery principles:
– Anomalous drug effects (don’t ignore compounds that have
therapeutic profiles that do not “fit”)
– Molecular biology in isozyme discovery and characterization
– Transgenic disease models were not useful here
– Importance of differential expression for drug action and sideeffects
– Significance of structural data (importance of sequence to
structure mapping)
Case study COX
A Wonder Drug: What is the most commonly-taken drug today?
It is an effective painkiller.
It reduces fever and inflammation when the body gets overzealous in
its defenses against infection and damage.
It slows blood clotting, reducing the chance of stroke and heart attack
in susceptible individuals.
It may be an effective addition to the fight against cancer.
Aspirin has been used professionally for a century, and
traditionally since ancient times. A similar compound
found in willow bark, salicylic acid, has a long history of
use in herbal treatment. But only in the last few decades
have we understood how aspirin works, and how it might
be improved
http://www.rcsb.org/pdb/molecules/pdb17_1.html
Prostaglandins
As you might expect from a drug with such diverse actions, aspirin
blocks a central process in the body: Aspirin blocks the production
of prostaglandins, key hormones that are used to carry local
messages.
Unlike most hormones, which are produced in specialized glands and
then delivered throughout the body by the blood, prostaglandins are
created by cells and then act only in the surrounding area before they
are broken down.
Prostaglandins control many of these neighborhood processes,
including the constriction of muscle cells around blood vessels,
aggregation of platelets during blood clotting, and constriction of the
uterus during labor.
Prostaglandins also deliver and strengthen pain signals and induce
inflammation.
These many different processes are all controlled by different
prostaglandins, but all created from a common precursor molecule.
http://www.rcsb.org/pdb/molecules/pdb17_1.html
Arachidonic Acid and COX
What does COX do?
COX = Cyclooxygenase (PDB
entry 1prh) performs the first step
in the creation of prostaglandins
from a common fatty acid.
It adds two oxygen molecules to
arachidonic acid, beginning a set of
reactions.
Aspirin blocks the binding of
arachidonic acid in the
cyclooxygenase active site. The
normal messages are not
delivered, so we don't feel the pain
and don't launch an inflammation
response.
http://www.rcsb.org/pdb/molecules/pdb17_1.html
Two different active sites,
collectively prostaglandin
synthase: 1, the
cyclooxygenase active site
discussed; 2, is has an entirely
separate peroxidase site,
which is needed to activate the
heme groups that participate in
the cyclooxygenase reaction.
Structural
Organization of COX
Dimer of identical subunits (two
cyclooxygenase active sites
and two peroxidase active sites
in close proximity)
Each subunit has a small
carbon-rich knob, pointing
downward anchoring the
complex to the membrane of
the endoplasmic reticulum,
shown in light blue.
The cyclooxygenase active site
is buried deep within the
protein, and is reachable by a
tunnel that opens out in the
middle of the knob. This acts
like a funnel, guiding
arachidonic acid out of the
membrane and into the
enzyme for processing.
PDB entry 4cox
http://www.rcsb.org/pdb/molecules/pdb17_1.html
Why is there a COX-1 and COX-2?
COX-1 and COX-2 are made for different purposes.
COX-1 is built in many different cells to create
prostaglandins used for basic housekeeping messages
throughout the body.
COX-2 is built only in special cells and is used for signaling
pain and inflammation.
Aspirin attacks both. Since COX-1 is targeted, aspirin can
lead to unpleasant complications, such as stomach
bleeding.
Needed: specific compounds that block just COX-2, leaving
COX-1 to perform its essential jobs. These drugs are
selective pain-killers and fever reducers, without the
unpleasant side-effects.
Active site
1pth
COX-2 Active Site (1pxx)
Arg120
Val523 (Ile in COX-1)
Tyr355
Difference between COX-1 and COX-2
Meaning of this case study for
future drug discovery
• If one could predict the structure of proteins from
sequence, one could discover new drugs at a
fast pace
• If one could predict the relationship between
isozyme and tissue expression, one could
design drugs specific to certain tissues
• If one could predict the interactions of proteins in
different protein networks, one could interpret
complex data such as animal models
• If one could…
What is computational pharmacology?
• Use of bioinformatics and computational
biology with relevance to pharmacology,
including understanding of
– drug action
– drug side effects
– identification of drug targets
– drug design
Background
• View of living organisms
as molecular circuitry:
– intended modes of
operation = healthy
state
– aberrant modes of
operation = disease
state
• Diagnosis:
– identify the molecular
basis of disease
• Therapy:
– guide biochemical
circuitry back to
healthy state
• Molecular circuitry:
– biochemical
processes, that form
and recycle molecules
in a coordinated and
balanced fashion
Where is
COX?
Position of COX in the pathway
Information Sources
• New technology generates massive amounts of
data (often stored in publicly accessible
databases): Genomics and Proteomics
– Protein and DNA sequences / Whole genome
sequences
– Protein structure data
– Protein pathways and networks
– Protein interaction data
– Expression data
• How is this data useful for drug discovery?
– Data needs to be organized, mined and visualized to
allow scientific discovery
Basis for a role of computational
approaches in drug discovery
• help with the information in the databases and infer
information that is not provided directly by genomics and
proteomics data: higher level information
=> piece together all available information
1. To get detailed picture of a molecular process (or disease)
2. From 1, to identify new protein targets
3. From 2, to develop drugs
• based on chemical similarity of known drugs
• rational (structure-based) drug design interactively on
computer screen
• molecular docking (automatic, systematic computer-based
prediction of structure and binding affinity of complex)
• high-throughput screening and combinatorial chemistry
Areas of application: 7 hierarchical layers
Layer 5. Predicting functional
• Layer 1. Sequencing support
structures (DNA - RNA – (physical mapping, fragment assembly
outcome: raw genome sequence)
proteins - lipids • Layer 2. DNA sequence analysis
carbohydrates)
1. Gene finding
1. Homology modeling
2. non-coding sequences
2. ab initio
3. regulatory sequences finding
3. templates
4. orthologous and paralogous sequences
4. partial information
5. Evolution
1. overall architecture
• Layer 3. Protein sequence analysis
2. binding pocket
1. homology detection
3. protein backbone
2. alignment
Layer 6. Molecular
3. functional annotation
interactions
4. cellular localization
(Protein-ligand, -protein, -DNA, • Layer 4. From linear sequence to threeRNA, -lipid, -carbohydrate)
dimensional shapes
Layer 7. Gene expression,
– conformational space
metabolic and regulatory
– models for protein (mis)folding
networks
– discriminating structures
– conformational ambiguity
General challenges
Linking variety of databases
Linking the different layers
Interpretation
Understanding drug action
Drug discovery
Meaning of this case study for
future drug discovery
• If one could predict the structure of proteins from
sequence, one could discover new drugs at a
fast pace
• If one could predict the relationship between
isozyme and tissue expression, one could
design drugs specific to certain tissues
• if one could predict the interactions of proteins in
different protein networks, one could interpret
complex data such as animal models
Computational Approaches to
Sequence-Structure Mapping in
Drug Discovery
• How would you go about comparing the
sequences of COX-1 and COX-2?
– Pairwise sequence alignment methods
• Are there other isoforms?
– Multiple sequence alignment
– Protein family profiles and databases
BLAST is a heuristic search
method that seeks words of
length W (default = 3 in
blastp) that score at least T
when aligned with the query
and scored with a
substitution matrix. Words in
the database that score T or
greater are extended in both
directions in an attempt to
fina a locally optimal
ungapped alignment or HSP
(high scoring pair) with a
score of at least S or an E
value lower than the
specified threshold. HSPs
that meet these criteria will
be reported by BLAST,
provided they do not exceed
the cutoff value specified for
number of descriptions
and/or alignments to report.
BLAST (Basic Local
Alignment Search Tools)
BLOSUM62 Substitution Scoring Matrix. The BLOSUM
62 matrix shown here is a 20 x 20 matrix of which a section
is shown here in which every possible identity and
substitution is assigned a score based on the observed
frequencies of such occurences in alignments of related
proteins. Identities are assigned the most positive scores.
Frequently observed substitutions also receive positive
scores and seldom observed substitutions are given
negative scores.
Scoring matrices
The PAM family PAM matrices are based on global
alignments of closely related proteins. The PAM1 is the
matrix calculated from comparisons of sequences with no
more than 1% divergence. Other PAM matrices are
extrapolated from PAM1.
The BLOSUM family BLOSUM matrices are based on
local alignments. BLOSUM 62 is a matrix calculated from
comparisons of sequences with no less than 62%
divergence. All BLOSUM matrices are based on observed
alignments; they are not extrapolated from comparisons of
closely related proteins. BLOSUM 62 is the default matrix in
BLAST 2.0. Though it is tailored for comparisons of
moderately distant proteins, it performs well in detecting
closer relationships. A search for distant relatives may be
more sensitive with a different matrix.
The relationship between BLOSUM and PAM
substitution matrices. BLOSUM matrices with higher
numbers and PAM matrices with low numbers are both
designed for comparisons of closely related sequences.
BLOSUM matrices with low numbers and PAM matrices
with high numbers are designed for comparisons of
distantly related proteins. If distant relatives of the query
sequence are specifically being sought, the matrix can be
tailored to that type of search.
http://www.ncbi.nlm.nih.gov/Education/
• Show blast result
Structure Analysis
• How does one determine structures?
– Experimentally (X-ray, NMR)
– Using computational methods (ab initio, Rosetta,
threading, homology models)
• How does one access structures?
– pdb
– SCOP/CATH
• How does one analyze structural features?
– Visualization programs (chime, rasmol, molmol, etc.)
– Demo with COX
Modeling Methods
A. When no information but sequence and physical principles are used
= ab initio structure prediction (Blue Gene IBM )
B. When other information is used (Survey of "ab initio" methods that use pdb information and their relation to protein folding)
"fold recognition":
requires a method for evaluating the compatibility of a given sequence with a given folding pattern
B0. 3D profiles
B1. Rosetta: conformations from short segments in pdb
B2. Including experimental structural constraints
B3. Threading (=sequence-structure alignment),
B4. Inverse threading and folding experiments Reference Ivet
B4a. using short-range information
B4b. using short- and long-range information
B4. Predicting structural class only Reference Ivet
B5. Predicting active site only?
B6. Predicting protein-protein interaction sites?
B7. Predicting surface shape?
C. When a template with known structure must be available
homology modeling
D. Modeling structures based on experimental data
Both NMR and X-ray underdetermine the protein structure. To solve a structure one must minimize a combination of the
deviation from the experimental data and the conformational energy:
D1. NMR (set of constraints on distances and angles)
D2. X-ray crystallography (Fourier transform of the electron density)
Evaluating structure prediction
• Use rmsd to known structures - defines
structural similarity
• Critical Assessment of Structure
Predictions (CASP) competitions
• EVA, EVA submits sequences
automatically to different prediction
servers shortly before structures are
published in pdb
Structures of High Sequence
Similarity
• Homology modeling
Basic Steps in Homology Modeling
• Database searching for homologous proteins
( Blast the query sequence towards the pdb
database )
• Alignment (Pairwise/ Multiple Alignments)
– needs minimum 30% sequence identity, but to be useful
usually need 40-50%
– note that ~30% of genomes have sequence identity of 20%
• Model Building
– Modeller , Composer etc
• Model Refinement and Evaluation
– Joy
– Procheck etc
Model Building
• Modeller (freeware,
http://www.salilab.org/modeller/modeller.html)
• Spdbviewer Swissmodel–module (freeware,
http://us.expasy.org/spdbv/)
• Composer (module of InsightII, commercial
version of Modeller)
Model Building Principles
• Sequentially go from amino acid position to next
position
– if same amino acid, copy the coordinates
– If different amino acid, if the new amino acid has
atoms in common with the template, those atoms will
be copied, and the rest are computed
• At every step, check for steric clashes with
previous amino acids
– Minimization allowing the position of new amino acid
to change
– Only at the final stage, bond energy is minimized
Model Refinement and Evaluation
http://cgat.ukm.my/spores/Predictory/evaluation.html
• Verify3D (based on
surface accessibility)
• Procheck (based on
phi/psi angle, rmsd
deviations)
• Joy (based on
secondary structure
assignments)
• WHAT IF (bond length,
bond angles, chi values,
etc.)
WHAT IF Checklist
A WHAT IF check report: what does it mean?
General points
Administrative checks
Nomenclature
Chain name
Weights (occupancy)
Missing atoms and C-terminal oxygens
Symmetry
Consistency
Cell conventions
Matthews' Coefficient
Higher symmetry
Non crystallographic symmetry
Geometry
Chirality
Bond lengths
Bond angles
Torsion Angles: "Evaluation"; "Ramachandran"; "omega"; "Chi1/2"
Rings and planarity: "Planarity"; "Proline Puckering"
Structure
Inside/outside profile
Bumps
Packing quality
Backbone: "number of hits"; "backbone normality"; "peptide flips"
Sidechain rotamers
Water molecules: "floating clusters"; "symmetry relations"
B-factors: "average"; "low B-factors"; "B-factor distribution"
Hydrogen bonds: "Flip check"; "HIS assignments"; "Unsatisfied"
Collection of homology models
• MODBASE
– uses PSI-BLAST plus MODELLER to model
and stores coordinates in this database
• SWISS-MODEL
– automatic structure prediction
Similarity Measures
• Sequence similarity
– Alignment scores, identity, similarity,
substitution matrices
• Structural similarity
– secondary structure
– rmsd and alternative methods
Differences in Degrees of Similarity
• High sequence similarity but structural
differences
– Impact on drug design : specificity of drugs for
different isozymes (Example COX1/2)
• Low sequence similarity but structurally
related
– Impact on drug design: design based on
existing drugs (Example GPCR)
GPCR Family and Their Ligands
Class A: Rhodopsin-like Family
Opsins, Odorants, Monoamines, Lipid messengers, Purines, Neuropeptides, Peptide
hormones (e.g. platelet activating factor, gonadotropin -releasing hormone, th yrotropin
releasing hormone & melatonin), Glycoprotein hormones, Chemokines, Proteases,
Cannabis, Viral
Class B: Secretin-like Family
Glucagon, Calcitonin, parathyroid hormone, secretin
Class C: Metabotropic glutamate and Chemosensor Family
mGluR 1-7, Calcium sensors, GABA-B
Class D: Fungal pheromone Family
Class E: c-AMP receptor ( Dictyostelium) Family
Class F: Frizzled/Smoothened family
Putative families:
Ocular albinism proteins, Drosophila odorant receptors, Plant Mlo receptors,Nematode
chemoreceptors, Vomeronasal receptors
Putative/ unclassified orphans
Structures of Low Sequence
Similarity
C-tail
• Only one structure
known, but serves
as model for other
pharmacologically
important GPCR
C-II
C-I
C-III
VIII
VI
VII
V
IV
II
I
III
Disulfide Bond
Cys110-Cys187
4
3
E-II
E-I
2
E-III
1
N-tail
• Demo if time permits
Conserved Features
100%
90%
80%
70%
% none missing
60%
% both missing
50%
% 187 missing
40%
30%
% 110 missing
20%
10%
Pu
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at
rp
ha
ns
O
s
Fa
m
ilie
iv
e
Fa
m
ily
E
oo
th
en
ed
C
la
ss
D
C
la
ss
Fr
iz
ze
d/
Sm
C
C
la
ss
B
C
la
ss
C
la
ss
A
0%
The Disulfide
Bond is highly
conserved
across families,
but not in
putative and
orphan
receptors
Sequence Alignment when
homology is low
• Secondary structure prediction methods
• Membrane protein prediction
• Novel alignment method developed by us
using phi/psi angles
• Sequence conservation based on property
conservation
Take home messages
• Structural and functional effects of small changes in
sequences
• Conservation of structure despite large differences in
sequences
• Prediction of structural and functional effects using
computational pharmacology to understand disease
mechanisms and drug action with the goal of identifying
targets and designing drugs against them
– Example: Specific Structure of COX and of GPCR
– Current hot topics: Complex interactions of proteins within their
environment, differences between individuals
Play with homology models
• www.cs.cmu.edu/~blmt/Seminar/SeminarMaterials/COX
COX 2 Modelling :
Template structure : 1PTH.pdb (cox1 in ovis aries)
query seq:sequence of 1PXX.pdb (cox2 in mus musculus)
model generated using modeller: 2cox.pdb
COX 1 Modelling:
Template structure : 1PXX.pdb (cox2 in mus musculus)
query seq:sequence of 1PTH.pdb (cox1 in ovis aries)
model generated using modeller: 1cox.pdb
• Rasmol is also in this directory, just click on the raswin
icon to start program
A protein conformation has to satisfy three conditions:
1. Only stereochemically allowed conformations of all residues are acceptable (=avoid steric clashes).
Model system: dialanine peptide
Rotation of the polypeptide chain is permitted around the N-Calpha (angle Phi) and Calpha-C (angle Psi) bonds (except Proline)
and the peptide bond (angle omega), which is either trans in most cases (omega=180o) or cis (omega=0o) in rare cases, i.e. at
Proline residues. These angles define the backbone conformation, and specific conformations are allowed, as described by the
Ramachandran plot.
2. The folded state must be energetically favorable
The native state of globular protein is only 20-60 kJ mol-1 (5-15kcal/mol) more stable than the denatured state. This is the
equivalent of one or two water-water hydrogen bonds. It is unclear why this is the case, because the stability of proteins can be
increased by adding stabilizing contacts. The main problem in achieving the native state is the loss of conformational freedom
(entropy reduction), when going from many unfolded to a single folded conformation. This process is therefore thermodynamically
unfavorable. Why does it still occur? Because the loss in entropy arising from conformational restriction is compensated by an
increase in entropy arising from the hydrophobic effect. The fact that native protein structures are more stable than unfolded protein
by 1-2 H-bonds, means that 1-2 unsatisfied H-bonds in a protein can make the native state unstable.
3. The folded state must be tightly packed.
How tightly packed is the interior of a protein? In theory, relatively loose packing would ensure exclusion of water, since a <1.4Å
radius (=size of water molecule) hole is acceptable. However, attraction between atoms (van der Waals forces) cause closer
packing than theoretically required by the hydrophobic effect alone. Thus, a protein is like a jigsaw puzzle, except that the pieces in
a jigsaw puzzle are rigid, while the side chains in proteins are dynamic and can adopt many conformations.
More details on requirements 2 and 3: The folded state must be energetically favorable and the folded state must be tightly packed.
Terms used in the evaluation of the energy of a conformation (see page 253 in chapter 5, Ref_Lesk for equations):
1. Bond stretching
2. Bond angle bend
3. deviations from planarity and enforcement of correct chirality
4. Torsion angle
5. van der Waals interactions
6. Hydrogen bonds
7. Electrostatics
8. solvent
=> set of conformational energy potentials that fine tune these parameter sets "Potential functions"
The potential functions satisfy necessary but not sufficient conditions for successful structure prediction. Multiple local minima
cannot be distinguished reliably from the correct one on the basis of calculated conformational energies. (What does this mean in
practice? How many possible structures are there as opposed to the real structures? How different are the structures?)