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
Feb 10, 2016
PHAR201 Lecture 12 2012
Protein Ligand Interactions:
A Method and its Application to Drug Discovery
PHAR 201/Bioinformatics IPhilip E. Bourne
Department of Pharmacology, [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
• 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
Further Motivators
PHAR201 Lecture 12 2012
• 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
Further Motivators
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.
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 FamiliesCharacterize 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
PHAR201 Lecture 12 2012
• 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
0.20.1)cos(
0.1
iDiPiPGP
neighbors
a
Conceptually similar to hydrophobicity or electrostatic potential that is dependant on both global and local environments
Characterization of the Ligand Binding Site - The Geometric Potential
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
Discrimination Power of the Geometric Potential
0
0.5
1
1.5
2
2.5
3
3.5
4
0 11 22 33 44 55 66 77 88 99
Geometric Potential
binding sitenon-binding site
• Geometric potential can distinguish binding and non-binding sites
100 0
Geometric Potential Scale
Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9
PHAR201 Lecture 12 2012
Local Sequence-order Independent Alignment with Maximum-Weight Sub-Graph Algorithm
L E R
V K D L
L E R
V K D L
Structure A Structure B
• 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
Xie and Bourne 2008 PNAS, 105(14) 5441
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
ia
i
ib
ib
i
ia SfSfd
fa, fb are the 20 amino acid target frequencies of profile a and b, respectivelySa, 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 protein-fragment complexes
• Similar sub-site detection enhances the application of fragment assembly strategies in drug discovery
1HQC: Holliday junction migration motor protein from Thermus thermophilus1ZEF: 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. Coli1H3D: ATP-phosphoribosyltransferase from E. Coli
PHAR201 Lecture 12 2012
This Approach is Called SMAPhttp://funsite.sdsc.edu
PHAR201 Lecture 12 2012
What Have These Off-targets and Networks Told Us So Far?Some Examples…
1. Nothing2. A possible explanation for a side-effect of a drug already
on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e1002037)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. How to optimize a NCE (NCE against T. Brucei PLoS Comp Biol. 2010 6(1): e1000648)
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
PHAR201 Lecture 12 2012
Adverse Effects of SERMs
cardiac abnormalities
thromboembolic disorders
ocular toxicities
loss of calcium homeostatis ?????
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 2007 3(11) e217
Ligand Binding Site Similarity Search On a Proteome Scale
• 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
ERa
0 20 40 60 80
0.00
0.02
0.04
0.06
Score
Den
sity
SERCA
Side Effects - The Tamoxifen Story PLoS Comp. Biol., 2007 3(11) e217PHAR201 Lecture 12 2012
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 SERMscardiac abnormalities
thromboembolic disorders
ocular toxicities
loss of calcium homeostatis SERCA !
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
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
Side Effects - The Tamoxifen Story
PLoS Comp. Biol., 2007 3(11) e217
PHAR201 Lecture 12 2012
What Have These Off-targets and Networks Told Us So Far?Some Examples…
1. Nothing2. A possible explanation for a side-effect of a drug already
on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e1002037)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. 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 PLoS Comp. Biol. 2011 7(4) e1002037
Possible Nelfinavir RepositioningPHAR201 Lecture 12 2012
binding site comparison
protein ligand docking
MD simulation & MM/GBSABinding free energy calculation
structural proteome
off-target?
network construction & mapping
drug target
Clinical Outcomes
1OHR
PHAR201 Lecture 12 2012PLoS Comp. Biol. 2011 7(4) e1002037
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 PLoS Comp. Biol. 2011 7(4) e1002037
PHAR201 Lecture 12 2012
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 PLoS Comp. Biol. 2011 7(4) e1002037
PHAR201 Lecture 12 2012
p-value < 1.0e-3
p-value < 1.0e-4
Distribution of Top Hits on the Human
Kinome
Manning et al., Science, 2002, V298, 1912
Possible Nelfinavir Repositioning
PHAR201 Lecture 12 2012
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
H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamidehydroxy O38
EGFR-DJKCo-crys ligand
EGFR-Nelfinavir
Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable
DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
PHAR201 Lecture 12 2012
Off-target Interaction Network
Identified off-target
Intermediate protein
Pathway
Cellular effect
Activation
Inhibition
Possible Nelfinavir RepositioningPLoS Comp. Biol. 2011 7(4) e1002037
PHAR201 Lecture 12 2012
Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive
The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activitywere 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 cellsby induction of cell cycle arrest
Nelfinavir induces G1 arrest through inhibitionof CDK2 activity.
Such inhibition is not caused by inhibition of Aktsignaling.
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 agentBruning, A., et al. , Molecular Cancer, 2010. 9:19
Nelfinavir may inhibit BCR-ABLPossible Nelfinavir Repositioning
PHAR201 Lecture 12 2012
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
Possible Nelfinavir RepositioningPLoS Comp. Biol. 2011 7(4) e1002037
PHAR201 Lecture 12 2012
What Have These Off-targets and Networks Told Us So Far?Some Examples…
1. Nothing2. A possible explanation for a side-effect of a drug already
on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e1002037)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. 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-Drugome1. 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
A Multi-target/drug Strategy
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
PHAR201 Lecture 12 2012
1. Determine the TB Structural Proteome
284
1, 446
3, 996 2, 266
TB proteome
homology models
solved structu
res
• High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%
A Multi-target/drug Strategy
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
PHAR201 Lecture 12 2012
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 36 370
20
40
60
80
100
120
140
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
No. of drug binding sites
No.
of d
rugs
MethotrexateChenodiol
AlitretinoinConjugated estrogens
DarunavirAcarbose
A Multi-target/drug Strategy
Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
PHAR201 Lecture 12 2012
Map 2 onto 1 – The TB-Drugomehttp://funsite.sdsc.edu/drugome/TB/
Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
PHAR201 Lecture 12 2012
1 2 3 4 5 6 7 8 9 10 11 12 13 140
2
4
6
8
10
12
14
16
18
20
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
No. of potential TB targets
No.
of
drug
s raloxifenealitretinoin
conjugated estrogens &methotrexate
ritonavir
testosterone levothyroxinechenodiol
A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976
PHAR201 Lecture 12 2012
Top 5 Most Highly Connected Drugs
Drug Intended targets Indications No. of connections TB proteins
levothyroxine transthyretin, thyroid hormone receptor α & β-1, thyroxine-binding globulin, mu-crystallin homolog, serum albumin
hypothyroidism, goiter, chronic lymphocytic thyroiditis, myxedema coma, stupor
14
adenylyl cyclase, argR, bioD, CRP/FNR trans. reg., ethR, glbN, glbO, kasB, lrpA, nusA, prrA, secA1, thyX, trans. reg. protein
alitretinoin retinoic acid receptor RXR-α, β & γ, retinoic acid receptor α, β & γ-1&2, cellular retinoic acid-binding protein 1&2
cutaneous lesions in patients with Kaposi's sarcoma 13
adenylyl cyclase, aroG, bioD, bpoC, CRP/FNR trans. reg., cyp125, embR, glbN, inhA, lppX, nusA, pknE, purN
conjugated estrogens
estrogen receptormenopausal vasomotor symptoms, osteoporosis, hypoestrogenism, primary ovarian failure
10acetylglutamate kinase, adenylyl cyclase, bphD, CRP/FNR trans. reg., cyp121, cysM, inhA, mscL, pknB, sigC
methotrexatedihydrofolate reductase, serum albumin
gestational choriocarcinoma, chorioadenoma destruens, hydatidiform mole, severe psoriasis, rheumatoid arthritis
10acetylglutamate kinase, aroF, cmaA2, CRP/FNR trans. reg., cyp121, cyp51, lpd, mmaA4, panC, usp
raloxifeneestrogen receptor, estrogen receptor β
osteoporosis in post-menopausal women 9
adenylyl cyclase, CRP/FNR trans. reg., deoD, inhA, pknB, pknE, Rv1347c, secA1, sigC
PHAR201 Lecture 12 2012
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
Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
PHAR201 Lecture 12 2012
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 Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423
PHAR201 Lecture 12 2012
What Have These Off-targets and Networks Told Us So Far?Some Examples…
1. Nothing2. A possible explanation for a side-effect of a drug already
on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)
3. A possible repositioning of a drug (Nelfinavir) to treat a completely different condition (PLoS Comp. Biol. 7(4) e1002037)
4. A multi-target/drug strategy to attack a pathogen (TB-drugome PLoS Comp Biol 2010 6(11): e1000976)
5. The reason a drug failed (Torcetrapib - PLoS Comp Biol 2009 5(5) e1000387)
6. 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.