1. No category

Protein Ligand Interactions:
A Method and its Application to Drug
Discovery
PHAR 201/Bioinformatics I
Philip E. Bourne
Department of Pharmacology, UCSD
[email protected]
PHAR201 Lecture 12 2012
Today’s Lecture in Context
• Prof Abagyan provided an overview of tools and
considerations in looking at protein-ligand interactions
• Today we will explore only one methodology in structural
bioinformatics in some detail. A method for examining
protein-ligand interactions and its implications for drug
discovery
• In a forthcoming lecture Roger Chang will describe how
this approach can be extended into the realm of systems
biology, also for drug discovery
PHAR201 Lecture 12 2012
Drug Discovery is a Major Reason to Study
Protein-Ligand Interactions But..
Failure is telling us that Ehrlich’s idea of a
magic bullet ie a highly specific drug for a
known receptor is rarely the case
PHAR201 Lecture 12 2012
One Drug Binds to Multiple Targets
• Tykerb – Breast cancer
• Gleevac – Leukemia, GI
cancers
• Nexavar – Kidney and liver
cancer
• Staurosporine – natural product
– alkaloid – uses many e.g.,
antifungal antihypertensive
Collins and Workman 2006
Nature Chemical Biology 2 689-700
PHAR201 Lecture 12 2012
Further Motivators
• The truth is we know very little about how the
major drugs we take work
• We know even less about what side effects they
might have
• Drug discovery seems to be approached in a
very consistent and conventional way
• The cost of bringing a drug to market is huge
~$800M
• The cost of failure is even higher e.g. Vioxx $4.85Bn - Hence fail early and cheaply
PHAR201 Lecture 12 2012
Further Motivators
• The truth is we know very little about how the
major drugs we take work – receptors are
unknown
• We know even less about what side effects they
might have - receptors are unknown
• Drug discovery seems to be approached in a
very consistent and conventional way
• The cost of bringing a drug to market is huge
~$800M – drug reuse is a big business
• The cost of failure is even higher e.g. Vioxx $4.85Bn - fail early and cheaply
PHAR201 Lecture 12 2012
What if…
• We can characterize a protein-ligand
binding site from a 3D structure (primary
site) and search for that site on a
proteome wide scale?
• We could perhaps find alternative binding
sites for existing pharmaceuticals?
• We could use it for lead optimization and
possible ADME/Tox prediction
PHAR201 Lecture 12 2012
What Methods Exist to Find Binding
Sites?
PHAR201 Lecture 12 2012
Template Methods e.g. MSDmotif
• MSDsite queries descriptions of existing sites
e.g. all SHD sites
• MSDsite finds unknown sites based on motif
search – limited and sequence order dependent
• Pocketome – known to exist experimentally limited
• We describe here a method that finds unknown
sites based on structure and is sequence order
independent
Golovin A, Henrick K: MSDmotif: exploring protein sites and motifs.
BMC Bioinformatics 2008, 9:312.
http://www.ebi.ac.uk/pdbe-site/pdbemotif/
PHAR201 Lecture 12 2012
Other Methods
• 3D structure based methods
• Electrostatic potential based methods
• 4 point pharmacophore fingerprint and
cavity shape descriptors
Henrich S, Salo-Ahen OM, Huang B, Rippmann FF, Cruciani G, et al.
Computational approaches to identifying and characterizing protein binding sites for ligand design.
J Mol Recognit 2010 23: 209-219.
PHAR201 Lecture 12 2012
The Method Described Here Starts with a 3D
Drug-Receptor Complex - The PDB Contains
Many Examples
Generic Name
Other Name
Treatment
PDBid
Lipitor
Atorvastatin
High cholesterol
1HWK, 1HW8…
Testosterone
Testosterone
Osteoporosis
1AFS, 1I9J ..
Taxol
Paclitaxel
Cancer
1JFF, 2HXF, 2HXH
Viagra
Sildenafil citrate
ED, pulmonary
arterial
hypertension
1TBF, 1UDT,
1XOS..
Digoxin
Lanoxin
Congestive heart
failure
1IGJ
PHAR201 Lecture 12 2012
A Reverse Engineering Approach to
Drug Discovery Across Gene Families
Characterize ligand binding
site of primary target
(Geometric Potential)
Identify off-targets by ligand
binding site similarity
(Sequence order independent
profile-profile alignment)
Extract known drugs
or inhibitors of the
primary and/or off-targets
Search for similar
small molecules
…
Dock molecules to both
primary and off-targets
Statistics analysis
of docking score
correlations
PHAR201 Lecture 12 2012
Characterization of the Ligand Binding
Site - The Geometric Potential
 Conceptually similar to hydrophobicity
or electrostatic potential that is
dependant on both global and local
environments
• Initially assign Ca atom with a
value that is the distance to the
environmental boundary
• Update the value with those of
surrounding Ca atoms
dependent on distances and
orientation – atoms within a
10A radius define i
GP  P 
Pi
cos(ai)  1.0

2.0
neighbors Di  1.0

Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
PHAR201 Lecture 12 2012
Discrimination Power of the Geometric
Potential
4
binding site
non-binding site
3.5
• Geometric
potential can
distinguish
binding and
non-binding
sites
3
2.5
2
1.5
1
0.5
100
Geometric Potential
99
88
77
66
55
44
33
22
11
0
0
0
Geometric Potential Scale
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
Local Sequence-order Independent Alignment with
Maximum-Weight Sub-Graph Algorithm
Structure A
Structure B
LER
VKDL
LER
VKDL
Xie and Bourne 2008 PNAS, 105(14) 5441
• Build an associated graph from the graph representations of two
structures being compared. Each of the nodes is assigned with a weight
from the similarity matrix
• The maximum-weight clique corresponds to the optimum alignment of
the two structures
PHAR201 Lecture 12 2012
Nothing in Biology {including Drug
Discovery} Makes Sense
Except in the Light of Evolution
Theodosius Dobzhansky (1900-1975)
PHAR201 Lecture 12 2012
Similarity Matrix of Alignment
Chemical Similarity
• Amino acid grouping: (LVIMC), (AGSTP), (FYW), and (EDNQKRH)
• Amino acid chemical similarity matrix
Evolutionary Correlation
• Amino acid substitution matrix such as BLOSUM45
• Similarity score between two sequence profiles
d   f a Sb   f b S a
i
i
i
i
i
i
fa, fb are the 20 amino acid target frequencies of profile a
and b, respectively
Sa, Sb are the PSSM of profile a and b, respectively
Xie and Bourne 2008 PNAS, 105(14) 5441
PHAR201 Lecture 12 2012
Lead Discovery from Fragment
Assembly
• Privileged molecular moieties
in medicinal chemistry
• Structural genomics and high
throughput screening generate
a large number of proteinfragment complexes
• Similar sub-site detection
enhances the application of
fragment assembly strategies
in drug discovery
1HQC: Holliday junction migration motor protein
from Thermus thermophilus
1ZEF: Rio1 atypical serine protein kinase
from A. fulgidus PHAR201 Lecture 12 2012
Lead Optimization from
Conformational Constraints
• Same ligand can bind to
different proteins, but with
different conformations
• By recognizing the
conformational changes in the
binding site, it is possible to
improve the binding specificity
with conformational constraints
placed on the ligand
1ECJ: amido-phosphoribosyltransferase
from E. Coli
1H3D: ATP-phosphoribosyltransferase
PHAR201 Lecture 12 2012
from E. Coli
This Approach is Called SMAP
http://funsite.sdsc.edu
PHAR201 Lecture 12 2012
What Have These Off-targets and Networks
Told Us So Far?
Some Examples…
1.
2.
Nothing
A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007
3(11) e217)
3.
4.
5.
6.
A possible repositioning of a drug (Nelfinavir) to treat
a completely different condition (PLoS Comp. Biol. 7(4)
e1002037)
A multi-target/drug strategy to attack a pathogen (TBdrugome PLoS Comp Biol 2010 6(11): e1000976)
The reason a drug failed (Torcetrapib - PLoS Comp Biol
2009 5(5) e1000387)
How to optimize a NCE (NCE against T. Brucei PLoS Comp
Biol. 2010 6(1): e1000648)
PHAR201 Lecture 12 2012
Selective Estrogen Receptor
Modulators (SERM)
• One of the largest
classes of drugs
• Breast cancer,
osteoporosis, birth
control etc.
• Amine and benzine
moiety
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 2007 3(11) e217
Adverse Effects of SERMs
cardiac abnormalities
thromboembolic
disorders
loss of calcium
homeostatis
?????
ocular toxicities
PLoS Comp. Biol., 2007 3(11) e217
Side Effects - The Tamoxifen Story
PHAR201 Lecture 12 2012
0.02
Density
0.04
0.06
Ligand Binding Site Similarity Search
On a Proteome Scale
0.00
SERCA
ERa
0
20
40
Score
60
80
• Searching human proteins covering ~38% of the
drugable genome against SERM binding site
• Matching Sacroplasmic Reticulum (SR) Ca2+ ion
channel ATPase (SERCA) TG1 inhibitor site
• ERa ranked top with p-value<0.0001 from reversed
search against SERCA
PHAR201 Lecture 12 2012
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 2007 3(11) e217
Structure and Function of SERCA
• Regulating cytosolic
calcium levels in cardiac
and skeletal muscle
• Cytosolic and
transmembrane domains
• Predicted SERM binding
site locates in the TM,
inhibiting Ca2+ uptake
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 2007 3(11) e217
Binding Poses of SERMs in SERCA
from Docking Studies
• Salt bridge
interaction between
amine group and
GLU
• Aromatic
interactions for both
N-, and C-moiety
6 SERMS A-F (red)
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 2007 3(11) e217
Off-Target of SERMs
cardiac abnormalities
thromboembolic
disorders
loss of calcium
homeostatis
SERCA !
ocular toxicities
 in vivo and in vitro Studies
 TAM play roles in regulating calcium uptake activity of cardiac SR
 TAM reduce intracellular calcium concentration and release in the
platelets
 Cataracts result from TG1 inhibited SERCA up-regulation
 EDS increases intracellular calcium in lens epithelial cells by
inhibiting SERCA
 in silico Studies
 Ligand binding site similarity
 Binding affinity correlation
PLoS Comp. Biol., 2007 3(11) e217
PHAR201 Lecture 12 2012
The Challenge
• Design modified SERMs that bind as
strongly to estrogen receptors but do not
have strong binding to SERCA, yet
maintain other characteristics of the
activity profile
PLoS Comp. Biol., 2007 3(11) e217
Side Effects - The Tamoxifen Story
PHAR201 Lecture 12 2012
What Have These Off-targets and Networks
Told Us So Far?
Some Examples…
1.
2.
Nothing
A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007
3(11) e217)
3.
4.
5.
6.
A possible repositioning of a drug (Nelfinavir) to treat
a completely different condition (PLoS Comp. Biol. 7(4)
e1002037)
A multi-target/drug strategy to attack a pathogen (TBdrugome PLoS Comp Biol 2010 6(11): e1000976)
The reason a drug failed (Torcetrapib - PLoS Comp Biol
2009 5(5) e1000387)
How to optimize a NCE (NCE against T. Brucei PLoS Comp
Biol. 2010 6(1): e1000648)
PHAR201 Lecture 12 2012
Nelfinavir
• Nelfinavir may have the most potent antitumor
activity of the HIV protease inhibitors
Joell J. Gills et al, Clin Cancer Res, 2007; 13(17)
Warren A. Chow et al, The Lancet Oncology, 2009, 10(1)
• Nelfinavir can inhibit receptor tyrosine kinase(s)
• Nelfinavir can reduce Akt activation
• Our goal:
• to identify off-targets of Nelfinavir in the human
proteome
• to construct an off-target binding network
• to explain the mechanism of anti-cancer activity
Possible Nelfinavir Repositioning
PHAR201 Lecture 12 2012
PLoS Comp. Biol. 2011 7(4) e1002037
PHAR201 Lecture 12 2012
Possible Nelfinavir Repositioning
drug target
off-target?
structural proteome
binding site comparison
1OHR
protein ligand docking
MD simulation & MM/GBSA
Binding free energy calculation
network construction
& mapping
PLoS Comp. Biol. 2011 7(4) e1002037
Clinical
Outcomes
PHAR201 Lecture 12 2012
Binding Site Comparison
• 5,985 structures or models that cover approximately
30% of the human proteome are searched against the
HIV protease dimer (PDB id: 1OHR)
• Structures with SMAP p-value less than 1.0e-3 were
retained for further investigation
• A total 126 structures have significant p-values < 1.0e-3
Possible Nelfinavir Repositioning
PHAR201 Lecture 12 2012
PLoS Comp. Biol. 2011 7(4) e1002037
Enrichment of Protein Kinases in
Top Hits
• The top 7 ranked off-targets belong to the same EC
family - aspartyl proteases - with HIV protease
• Other off-targets are dominated by protein kinases (51
off-targets) and other ATP or nucleotide binding proteins
(17 off-targets)
• 14 out of 18 proteins with SMAP p-values < 1.0e-4 are
protein kinases
Possible Nelfinavir Repositioning
PHAR201 Lecture 12 2012
PLoS Comp. Biol. 2011 7(4) e1002037
Distribution of
Top Hits on the
Human Kinome
p-value < 1.0e-4
p-value < 1.0e-3
Manning et al., Science,
2002, V298, 1912
PHAR201 Lecture 12 2012
Possible Nelfinavir Repositioning
Interactions between Inhibitors and Epidermal Growth Factor
Receptor (EGFR) – 74% of binding site resides are comparable
1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of
inhibition)
2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other
residues
EGFR-DJK
Co-crys ligand
EGFR-Nelfinavir
H-bond: Met793 with quinazoline N1
PHAR201 Lecture 12 2012
DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
H-bond: Met793 with benzamide
hydroxy O38
Off-target Interaction Network
Identified off-target
Intermediate protein
Pathway
Cellular effect
PHAR201 Lecture 12 2012
Possible Nelfinavir Repositioning
Activation
Inhibition
PLoS Comp. Biol. 2011 7(4) e1002037
Other Experimental Evidence to Show Nelfinavir inhibition on
EGFR, IGF1R, CDK2 and Abl is Supportive
The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity
were detected by immunoblotting.
The inhibition of Nelfinavir on Akt activity is less than a
known PI3K inhibitor
Joell J. Gills et al.
Clinic Cancer Research September 2007 13; 5183
Nelfinavir inhibits growth of human melanoma cells
by induction of cell cycle arrest
Nelfinavir induces G1 arrest through inhibition
of CDK2 activity.
Such inhibition is not caused by inhibition of Akt
signaling.
Jiang W el al. Cancer Res. 2007 67(3)
BCR-ABL is a constitutively activated tyrosine kinase that causes chronic myeloid leukemia (CML)
Druker, B.J., et al New England Journal of Medicine, 2001. 344(14): p. 1031-1037
Nelfinavir can induce apoptosis in leukemia cells as a single agent
Bruning, A., et al. , Molecular Cancer, 2010. 9:19
Nelfinavir may inhibit BCR-ABL
PHAR201 Lecture 12 2012
Possible Nelfinavir Repositioning
Summary
• The HIV-1 drug Nelfinavir appears to be
a broad spectrum low affinity kinase
inhibitor
• Most targets are upstream of the
PI3K/Akt pathway
• Findings are consistent with the
experimental literature
• More direct experiment is needed
PHAR201 Lecture 12 2012
Possible Nelfinavir Repositioning
PLoS Comp. Biol. 2011 7(4) e1002037
What Have These Off-targets and Networks
Told Us So Far?
Some Examples…
1.
2.
Nothing
A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007
3(11) e217)
3.
4.
5.
6.
A possible repositioning of a drug (Nelfinavir) to treat
a completely different condition (PLoS Comp. Biol. 7(4)
e1002037)
A multi-target/drug strategy to attack a pathogen (TBdrugome PLoS Comp Biol 2010 6(11): e1000976)
The reason a drug failed (Torcetrapib - PLoS Comp Biol
2009 5(5) e1000387)
How to optimize a NCE (NCE against T. Brucei PLoS Comp
Biol. 2010 6(1): e1000648)
PHAR201 Lecture 12 2012
As a High Throughput
Approach…..
PHAR201 Lecture 12 2012
The Problem with Tuberculosis
•
•
•
•
•
One third of global population infected
1.7 million deaths per year
95% of deaths in developing countries
Anti-TB drugs hardly changed in 40 years
MDR-TB and XDR-TB pose a threat to
human health worldwide
• Development of novel, effective and
inexpensive drugs is an urgent priority
PHAR201 Lecture 12 2012
The TB-Drugome
1. Determine the TB structural proteome
2. Determine all known drug binding sites
from the PDB
3. Determine which of the sites found in 2
exist in 1
4. Call the result the TB-drugome
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
A Multi-target/drug Strategy
PHAR201 Lecture 12 2012
1. Determine the TB Structural
Proteome
3, 996
2, 266
284
1, 446
• High quality homology models from ModBase
(http://modbase.compbio.ucsf.edu) increase structural
coverage from 7.1% to 43.3%
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
PHAR201 Lecture 12 2012
A Multi-target/drug Strategy
2. Determine all Known Drug
Binding Sites in the PDB
• Searched the PDB for protein crystal structures
bound with FDA-approved drugs
• 268 drugs bound in a total of 931 binding sites
140
No. of drugs
120
Acarbose
Darunavir
Alitretinoin
Conjugated
estrogens
Chenodiol
100
80
60
40
Methotrexate
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
No. of drug binding sites
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
PHAR201 Lecture 12 2012
A Multi-target/drug Strategy
Map 2 onto 1 – The TB-Drugome
http://funsite.sdsc.edu/drugome/TB/
Similarities between the binding sites of M.tb proteins (blue),
PHAR201 Lecture 12 2012
and binding sites containing
approved drugs (red).
From a Drug Repositioning Perspective
• Similarities between drug binding sites and
TB proteins are found for 61/268 drugs
• 41 of these drugs could potentially inhibit
more than one TB protein
20
No. of drugs
18
16
chenodiol
14
12
testosterone
10
conjugated
estrogens &
methotrexate
raloxifene
ritonavir
8
levothyroxine
alitretinoin
6
4
2
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
No. of potential TB targets
A Multi-target/drug Strategy
PHAR201 Lecture 12 2012
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
Top 5 Most Highly Connected
Drugs
Drug
Intended targets
Indications
levothyroxine
transthyretin, thyroid
hormone receptor α & β-1,
thyroxine-binding globulin,
mu-crystallin homolog,
serum albumin
hypothyroidism, goiter,
chronic lymphocytic
thyroiditis, myxedema coma,
stupor
alitretinoin
conjugated
estrogens
retinoic acid receptor RXR-α,
β & γ, retinoic acid receptor cutaneous lesions in patients
α, β & γ-1&2, cellular retinoic with Kaposi's sarcoma
acid-binding protein 1&2
estrogen receptor
menopausal vasomotor
symptoms, osteoporosis,
hypoestrogenism, primary
ovarian failure
dihydrofolate reductase,
serum albumin
gestational choriocarcinoma,
chorioadenoma destruens,
hydatidiform mole, severe
psoriasis, rheumatoid arthritis
methotrexate
No. of
TB proteins
connections
14
adenylyl cyclase, argR, bioD,
CRP/FNR trans. reg., ethR,
glbN, glbO, kasB, lrpA, nusA,
prrA, secA1, thyX, trans. reg.
protein
13
adenylyl cyclase, aroG,
bioD, bpoC, CRP/FNR trans.
reg., cyp125, embR, glbN,
inhA, lppX, nusA, pknE, purN
10
acetylglutamate kinase,
adenylyl cyclase, bphD,
CRP/FNR trans. reg., cyp121,
cysM, inhA, mscL, pknB, sigC
10
acetylglutamate kinase, aroF,
cmaA2, CRP/FNR trans. reg.,
cyp121, cyp51, lpd, mmaA4,
panC, usp
raloxifene
estrogen receptor, estrogen
receptor β
osteoporosis in postmenopausal women
PHAR201 Lecture 12 2012
9
adenylyl cyclase, CRP/FNR
trans. reg., deoD, inhA, pknB,
pknE, Rv1347c, secA1, sigC
Vignette within Vignette
• Entacapone and tolcapone shown to have potential
for repositioning
• Direct mechanism of action avoids M. tuberculosis
resistance mechanisms
• Possess excellent safety profiles with few side effects
– already on the market
• In vivo support
• Assay of direct binding of entacapone and tolcapone
to InhA reveals a possible lead with no chemical
relationship to existing drugs
PHAR201 Lecture 12 2012
Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
Summary from the TB Alliance –
Medicinal Chemistry
• The minimal inhibitory concentration (MIC)
of 260 uM is higher than usually
considered
• MIC is 65x the estimated plasma
concentration
• Have other InhA inhibitors in the pipeline
Repositioning - The TB Story
PHAR201 Lecture 12 2012
Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
What Have These Off-targets and Networks
Told Us So Far?
Some Examples…
1.
2.
Nothing
A possible explanation for a side-effect of a drug
already on the market (SERMs - PLoS Comp. Biol., 2007
3(11) e217)
3.
4.
5.
6.
A possible repositioning of a drug (Nelfinavir) to treat
a completely different condition (PLoS Comp. Biol. 7(4)
e1002037)
A multi-target/drug strategy to attack a pathogen (TBdrugome PLoS Comp Biol 2010 6(11): e1000976)
The reason a drug failed (Torcetrapib - PLoS Comp Biol
2009 5(5) e1000387)
How to optimize a NCE (NCE against T. Brucei PLoS Comp
Biol. 2010 6(1): e1000648)
PHAR201 Lecture 12 2012
In An Upcoming Lecture..
• Roger Chang will describe how systems
Biology can be used to further model
protein-drug interactions in a dynamic way.
PHAR201 Lecture 12 2012