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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.

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Protein ligand interactions a method and its application to drug discovery

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
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
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
One Drug Binds to Multiple Targets Interactions But..

  • 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
What if… we take work –

  • 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
What Methods Exist to Find Binding Sites? we take work –

PHAR201 Lecture 12 2012


Template methods e g msdmotif
Template Methods e.g. we take work – 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: exploringprotein sites and motifs.

BMC Bioinformatics 2008, 9:312.

http://www.ebi.ac.uk/pdbe-site/pdbemotif/

PHAR201 Lecture 12 2012


Other methods
Other Methods we take work –

  • 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 MolRecognit2010 23: 209-219.

PHAR201 Lecture 12 2012


The method described here starts with a 3d drug receptor complex the pdb contains many examples
The Method Described Here Starts we take work – with a 3D Drug-Receptor Complex - The PDB Contains Many Examples

PHAR201 Lecture 12 2012


A reverse engineering approach to drug discovery across gene families
A Reverse Engineering Approach to we take work – 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

Xie and Bourne 2007 BMC Bioinformatics, 8(Suppl 4):S9

PHAR201 Lecture 12 2012


Discrimination power of the geometric potential
Discrimination Power of the Geometric Potential Potential

  • 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

Structure A

Structure B

L E R

V K D L

L E R

V K D L

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 Maximum-Weight Sub-Graph Algorithm{including Drug Discovery} Makes Sense Except in the Light of Evolution

Theodosius Dobzhansky (1900-1975)

PHAR201 Lecture 12 2012


Similarity Matrix of Alignment Maximum-Weight Sub-Graph Algorithm

  • 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

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
Lead Discovery from Fragment Assembly Maximum-Weight Sub-Graph Algorithm

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 thermophilus

1ZEF: Rio1 atypical serine protein kinase

from A. fulgidus

PHAR201 Lecture 12 2012


Lead optimization from conformational constraints
Lead Optimization from Maximum-Weight Sub-Graph AlgorithmConformational 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

from E. Coli

PHAR201 Lecture 12 2012


This approach is called smap http funsite sdsc edu
This Approach is Called SMAP Maximum-Weight Sub-Graph Algorithmhttp://funsite.sdsc.edu

PHAR201 Lecture 12 2012


What have these off targets and networks told us so far some examples
What Have These Off-targets and Networks Told Us So Far? Maximum-Weight Sub-Graph AlgorithmSome Examples…

Nothing

A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)

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 (TB-drugome 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. BruceiPLoS Comp Biol. 2010 6(1): e1000648)

PHAR201 Lecture 12 2012


Selective estrogen receptor modulators serm
Selective Estrogen Receptor Modulators (SERM) Maximum-Weight Sub-Graph Algorithm

  • 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
Adverse Effects of SERMs Maximum-Weight Sub-Graph Algorithm

cardiac abnormalities

loss of calcium

homeostatis

thromboembolic

disorders

?????

ocular toxicities

PLoS Comp. Biol., 2007 3(11) e217

PHAR201 Lecture 12 2012

Side Effects - The Tamoxifen Story


Ligand binding site similarity search on a proteome scale
Ligand Binding Site Similarity Search On a Proteome Scale Maximum-Weight Sub-Graph Algorithm

SERCA

ERa

  • 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
Structure and Function of SERCA Maximum-Weight Sub-Graph Algorithm

  • 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
Binding Poses of SERMs in SERCA from Docking Studies Maximum-Weight Sub-Graph Algorithm

  • 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
Off-Target of SERMs Maximum-Weight Sub-Graph Algorithm

cardiac abnormalities

loss of calcium

homeostatis

thromboembolic

disorders

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
The Challenge Maximum-Weight Sub-Graph Algorithm

  • 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

PHAR201 Lecture 12 2012

Side Effects - The Tamoxifen Story


What have these off targets and networks told us so far some examples1
What Have These Off-targets and Networks Told Us So Far? Maximum-Weight Sub-Graph AlgorithmSome Examples…

Nothing

A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)

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 (TB-drugome 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. BruceiPLoS Comp Biol. 2010 6(1): e1000648)

PHAR201 Lecture 12 2012


Nelfinavir
Nelfinavir Maximum-Weight Sub-Graph Algorithm

  • 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

PHAR201 Lecture 12 2012

PLoS Comp. Biol. 2011 7(4) e1002037

Possible Nelfinavir Repositioning


PHAR201 Lecture 12 2012 Maximum-Weight Sub-Graph Algorithm

Possible Nelfinavir Repositioning


drug Maximum-Weight Sub-Graph Algorithm

target

off-target?

structural proteome

binding site comparison

1OHR

protein ligand docking

MD simulation & MM/GBSA

Binding free energy calculation

network construction

& mapping

Clinical Outcomes

PHAR201 Lecture 12 2012

PLoS Comp. Biol. 2011 7(4) e1002037


Binding site comparison
Binding Site Comparison Maximum-Weight Sub-Graph Algorithm

  • 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

PHAR201 Lecture 12 2012

PLoS Comp. Biol. 2011 7(4) e1002037

Possible Nelfinavir Repositioning


Enrichment of protein kinases in top hits
Enrichment of Protein Kinases in Top Hits Maximum-Weight Sub-Graph Algorithm

  • 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

PHAR201 Lecture 12 2012

PLoS Comp. Biol. 2011 7(4) e1002037

Possible Nelfinavir Repositioning


Distribution of top hits on the human kinome
Distribution of Top Hits on the Human Kinome Maximum-Weight Sub-Graph Algorithm

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 benzamide

hydroxy O38

H-bond: Met793 with quinazoline N1

PHAR201 Lecture 12 2012

DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE


Off target interaction network
Off-target Interaction Network Receptor (EGFR) – 74% of binding site resides are comparable

Identified off-target

Pathway

Activation

Intermediate protein

Cellular effect

Inhibition

PHAR201 Lecture 12 2012

PLoS Comp. Biol. 2011 7(4) e1002037

Possible Nelfinavir Repositioning


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 kinasethat 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
Summary EGFR, IGF1R, CDK2 and Abl is Supportive

  • 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

PLoS Comp. Biol. 2011 7(4) e1002037

Possible Nelfinavir Repositioning


What have these off targets and networks told us so far some examples2
What Have These Off-targets and Networks Told Us So Far? EGFR, IGF1R, CDK2 and Abl is SupportiveSome Examples…

Nothing

A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)

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 (TB-drugome 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. BruceiPLoS Comp Biol. 2010 6(1): e1000648)

PHAR201 Lecture 12 2012


A s a high throughput approach

A EGFR, IGF1R, CDK2 and Abl is Supportives a High Throughput Approach…..

PHAR201 Lecture 12 2012


The problem with tuberculosis
The Problem with EGFR, IGF1R, CDK2 and Abl is SupportiveTuberculosis

  • 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
The TB-Drugome EGFR, IGF1R, CDK2 and Abl is Supportive

  • Determine the TB structural proteome

  • Determine all known drug binding sites from the PDB

  • Determine which of the sites found in 2 exist in 1

  • Call the result the TB-drugome

Kinnings et al 2010 PLoS Comp Biol6(11): e1000976

PHAR201 Lecture 12 2012

A Multi-target/drug Strategy


1 determine the tb structural proteome
1. Determine the TB Structural Proteome EGFR, IGF1R, CDK2 and Abl is Supportive

  • High quality homology models from ModBase (http://modbase.compbio.ucsf.edu) increase structural coverage from 7.1% to 43.3%

TB proteome

homology models

solved structures

2, 266

3, 996

284

1, 446

Kinnings et al 2010 PLoS Comp Biol6(11): e1000976

PHAR201 Lecture 12 2012

A Multi-target/drug Strategy


2 determine all known drug binding sites in the pdb
2. Determine all Known Drug Binding Sites in the PDB EGFR, IGF1R, CDK2 and Abl is Supportive

  • Searched the PDB for protein crystal structures bound with FDA-approved drugs

  • 268 drugs bound in a total of 931 binding sites

No. of drugs

Acarbose

Darunavir

Alitretinoin

Conjugated estrogens

Chenodiol

Methotrexate

No. of drug binding sites

Kinnings et al 2010 PLoS Comp Biol6(11): e1000976

PHAR201 Lecture 12 2012

A Multi-target/drug Strategy


Map 2 onto 1 – The TB-Drugome EGFR, IGF1R, CDK2 and Abl is Supportive

http://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


From a drug repositioning perspective
From a Drug Repositioning Perspective EGFR, IGF1R, CDK2 and Abl is Supportive

  • 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

conjugated estrogens &

methotrexate

No. of drugs

chenodiol

levothyroxine

testosterone

raloxifene

ritonavir

alitretinoin

No. of potential TB targets

PHAR201 Lecture 12 2012

A Multi-target/drug Strategy

Kinnings et al 2010 PLoS Comp Biol6(11): e1000976


Top 5 most highly connected drugs
Top 5 Most Highly Connected Drugs EGFR, IGF1R, CDK2 and Abl is Supportive

PHAR201 Lecture 12 2012


Vignette within vignette
Vignette within Vignette EGFR, IGF1R, CDK2 and Abl is Supportive

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
Summary from the TB Alliance – Medicinal Chemistry EGFR, IGF1R, CDK2 and Abl is Supportive

  • 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

PHAR201 Lecture 12 2012

Repositioning- The TB Story

Kinnings et al. 2009 PLoS Comp Biol 5(7) e1000423


What have these off targets and networks told us so far some examples3
What Have These Off-targets and Networks Told Us So Far? EGFR, IGF1R, CDK2 and Abl is SupportiveSome Examples…

Nothing

A possible explanation for a side-effect of a drug already on the market (SERMs - PLoS Comp. Biol., 2007 3(11) e217)

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 (TB-drugome 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. BruceiPLoS Comp Biol. 2010 6(1): e1000648)

PHAR201 Lecture 12 2012


In an upcoming lecture
In An Upcoming Lecture.. EGFR, IGF1R, CDK2 and Abl is Supportive

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

PHAR201 Lecture 12 2012


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