Protein Ligand Interactions: A Method and its Application to Drug Discovery PHAR 201/Bioinformatics I Philip E. Bourne Department of Pharmacology, UCSD.

PHAR201 Lecture 12 2012

Protein Ligand Interactions:

A Method and its Application to Drug Discovery

PHAR 201/Bioinformatics I

Philip E. Bourne

Department of Pharmacology, UCSD

[email protected]


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


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


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


• 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


• 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


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


What Methods Exist to Find Binding Sites?


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/


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


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

…



• 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

0.2

0.1)cos(

0.1

i

Di

PiPGP

neighbors

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 site

non-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


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


Nothing in Biology {including Drug Discovery} Makes Sense

Except in the Light of Evolution

Theodosius Dobzhansky (1900-1975)


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


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


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


This Approach is Called SMAPhttp://funsite.sdsc.edu


What Have These Off-targets and Networks Told Us So Far?Some Examples…

1. Nothing

2. 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


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

0.0

00

.02

0.0

40

.06

Score

De

nsi

ty

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


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)


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



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



1. Nothing







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


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



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



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


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


Off-target Interaction Network

Identified off-target

Intermediate protein

Pathway

Cellular effect

Activation

Inhibition

Possible Nelfinavir RepositioningPLoS 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 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


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



1. Nothing







As a High Throughput Approach…..


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


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

A Multi-target/drug Strategy

Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976


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%




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




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).


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

testosteronelevothyroxine

chenodiol

A Multi-target/drug Strategy Kinnings et al 2010 PLoS Comp Biol 6(11): e1000976


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


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


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



1. Nothing







In An Upcoming Lecture..

• Roger Chang will describe how systems Biology can be used to further model protein-drug interactions in a dynamic way.

Protein Ligand Interactions: A Method and its Application to Drug Discovery PHAR 201/Bioinformatics I Philip E. Bourne Department of Pharmacology, UCSD.

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