stephen friend ibc next generation sequencing & genomic medicine 2011-08-03
DESCRIPTION
Stephen Friend Aug 1-6, 2011 IBC Next Generation Sequencing & Genomic Medicine San Francisco, CATRANSCRIPT
Arch2POCM A Drug Development Approach
from Disease Targets to their Clinical Validation
Stephen Friend Sage Bionetworks
(a non-profit foundation)
IBC San Francisco Aug 3, 2011
Alzheimers Diabetes
Depression Cancer
Treating Symptoms v.s. Modifying Diseases
Will it work for me?
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Personalized Medicine 101: Capturing Single bases pair mutations = ID of responders
Reality: Overlapping Pathways
WHY NOT USE “DATA INTENSIVE” SCIENCE
TO BUILD BETTER DISEASE MAPS?
Equipment capable of generating massive amounts of data
“Data Intensive Science”- “Fourth Scientific Paradigm” For building: “Better Maps of Human Disease”
Open Information System
IT Interoperability
Evolving Models hosted in a Compute Space- Knowledge Expert
It is now possible to carry out comprehensive monitoring of many traits at the population level
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trait trait trait trait trait trait trait trait trait trait trait trait trait
How is genomic data used to understand biology?
!Standard" GWAS Approaches Profiling Approaches
!Integrated" Genetics Approaches
Genome scale profiling provide correlates of disease Many examples BUT what is cause and effect?
Identifies Causative DNA Variation but provides NO mechanism
Provide unbiased view of molecular physiology as it
relates to disease phenotypes
Insights on mechanism
Provide causal relationships and allows predictions
RNA amplification Microarray hybirdization
Gene Index
Tum
ors
Tum
ors
Constructing Co-expression Networks
Start with expression measures for genes most variant genes across 100s ++ samples
Note: NOT a gene expression heatmap
1 -0.1 -0.6 -0.8
-0.1 1 0.1 0.2
-0.6 0.1 1 0.8
-0.8 0.2 0.8 1 1
2
3
4
1 2 3 4
Correlation Matrix Brain sample
expr
essi
on
1 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 1
2
3
4
1 2 3 4
Connection Matrix
1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1
2
4
3
1 2 4 3
4 1
3 2
4 1
2
Establish a 2D correlation matrix for all gene pairs
Define Threshold eg >0.6 for edge
Clustered Connection Matrix
Hierarchically cluster
sets of genes for which many pairs interact (relative to the total number of pairs in that
set)
Network Module
Identify modules
Integration of Genotypic, Gene Expression & Trait Data
Causal Inference
Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009)
Chen et al. Nature 452:429 (2008) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005)
Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)
“Global Coherent Datasets” • population based
• 100s-1000s individuals
Preliminary Probabalistic Models- Rosetta /Schadt
Gene symbol Gene name Variance of OFPM explained by gene expression*
Mouse model
Source
Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg
Mirochnitchenko (University of Medicine and Dentistry at New Jersey, NJ) [12]
Lactb Lactamase beta 52% tg Constructed using BAC transgenics Me1 Malic enzyme 1 52% ko Naturally occurring KO Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple
(UCLA) [13] Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg
(Columbia University, NY) [11] C3ar1 Complement component
3a receptor 1 46% ko Purchased from Deltagen, CA
Tgfbr2 Transforming growth factor beta receptor 2
39% ko Purchased from Deltagen, CA
Networks facilitate direct identification of genes that are
causal for disease Evolutionarily tolerated weak spots
Nat Genet (2005) 205:370
50 network papers http://sagebase.org/research/resources.php
List of Influential Papers in Network Modeling
(Eric Schadt)
Requires Data driven Science
Lots of data, tools, evolving models of disease
Requires Scientists Clinicians & Citizens to link in different ways
Sage Mission
Sage Bionetworks is a non-profit organization with a vision to create a “commons” where integrative bionetworks are evolved by
contributor scientists with a shared vision to accelerate the elimination of human disease
Sagebase.org
Data Repository
Discovery Platform
Building Disease Maps
Commons Pilots
Sage Bionetworks Collaborators
Pharma Partners Merck, Pfizer, Takeda, Astra Zeneca, Amgen
20
Foundations CHDI, Gates Foundation
Government NIH, LSDF
Academic Levy (Framingham) Rosengren (Lund) Krauss (CHORI)
Federation Ideker, Califarno, Butte, Schadt
Model of Alzheimer’s Disease Bin Zhang Jun Zhu
AD
normal
AD
normal
AD
normal
Cell cycle
http://sage.fhcrc.org/downloads/downloads.php
Breast Cancer Bayesian Network Conserved Super-modules
mR
NA
proc
.
Chr
omat
in
Pathways & Regulators (Key drivers=yellow; key drivers validated in siRNA screen=green)
Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red)
Extract gene:gene relationships for selected super-modules from BN and define Key Drivers
Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)
Bin Zhang Xudong Dai Jun Zhu
Model of Breast Cancer: Integration
= predictive of survival
Clinical Trial Comparator Arm Partnership (CTCAP)
Description: Collate, Annotate, Curate and Host Clinical Trial Data with Genomic Information from the Comparator Arms of Industry and Foundation Sponsored Clinical Trials: Building a Site for Sharing Data and Models to evolve better Disease Maps.
Public-Private Partnership of leading pharmaceutical companies, clinical trial groups and researchers.
Neutral Conveners: Sage Bionetworks and Genetic Alliance [nonprofits].
Initiative to share existing trial data (molecular and clinical) from non-proprietary comparator and placebo arms to create powerful new tool for drug development.
Federation s Genome-wide Network and Modeling Approach to Warburg Effect
Califano group at Columbia Sage Bionetworks Butte group at Stanford
THE FEDERATION Butte Califano Friend Ideker Schadt
vs
Software Tools Support Collaboration
Biology Tools Support Collaboration
Potential Supporting Technologies
Taverna
Addama
pb�s � � � � � �
Platform for Modeling
SYNAPSE
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INTEROPERABILITY
RULES GOVERN
Engaging Communities of Interest
PLAT
FORM
NEW
MAP
S NEW MAPS
Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...)
PLATFORM Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)
RULES AND GOVERNANCE Data Sharing Barrier Breakers-
(Patients Advocates, Governance and Policy Makers, Funders...)
NEW TOOLS Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape,
Clinical Trialists, Industrial Trialists, CROs…)
PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....) Arch2POCM
Arch2POCM
A Fundamental Systems Change for Drug Discovery
Stephen Friend Aled Edwards Chas Bountra Lex vander Ploeg, Thea Norman, Keith Yamamoto
“Absurdity” of Current R&D Ecosystem
• $200B per year in biomedical and drug discovery R&D • Handful of new medicines approved each year • ProducJvity in steady decline since 1950 • 90% of novel drugs entering clinical trials fail • NIH and EU just started spending billions to duplicate process
• 98% of pharma revenues from compounds approved more than 5 years ago (average patent life 11 years)
• >50,000 pharma employees fired in each of last three years • Number of R&D sites in Europe down from 29 to 16 in 2009
What is the problem? • Regulatory hurdles too high? • Low hanging fruit picked? • Payers unwilling to pay? • Genome has not delivered? • Valley of death? • Companies not large enough to execute on strategy? • Internal research costs too high? • Clinical trials in developed countries too expensive?
• In fact, all are true but none is the real problem
What is the problem? • The current system is designed as if every new program is desJned to deliver an approved drug
• Each new therapy is pursued through use of proprietary compounds moving in parallel with no data being shared (oden 5-‐10 companies at a Jme)
• Therefore it makes complete sense to maintain secrecy
• But we have no clue what we’re doing • Alzheimer’s, cancer, schizophrenia, auJsm.….
The current pharma model is redundant
50% 10% 30% 30% 90%
Negative POC information is not shared Attrition
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
Target ID/ Discovery
Hit/Probe/Lead ID
Clinical Candidate
ID
Toxicology/
Pharmacology
Phase I Phase IIa/IIb
What is the problem? The real problem is that the current system is unable to provide the needed insights into human biology and disease required
• It has been assumed that academia’s role is to provide fundamental biological insights into disease, but we know now that virtually all experiments used to understand disease in test tubes and animals are not predicJve of what happens in people
We need to develop a mechanism to be8er understand disease biology before tes:ng compe::ve compounds on sick people
Let’s imagine….
• A pool of dedicated, stable funding
• A process that attracts top scientists and clinicians
• A process in which regulators can fully collaborate to solve key scientific problems
• An engaged citizenry that promotes science and acknowledges risk
• Mechanisms to decrease bureaucratic and administrative barriers
• Sharing of knowledge to more rapidly achieve understanding of human biology
• A steady stream of targets whose links to disease have been validated in humans
Basic Concept of Arch2POCM
• Use NME’s to understand the biology of human diseases
• Focus on targets that are deemed too risky for either public or private sectors
• Share the risk by pooling public and private resources
• Open access to data produces stream of informaJon about human biology
• NME, not burdened by IP, expands the number of ideas that can be pursued, without addiJonal funds
Entry Points For Arch2POCM Programs Requires two compounds if first fails
Lead identification Phase I Phase II Preclinical
Lead optimisation
Assay in vitro probe
Lead Clinical candidate
Phase I asset
Phase II asset
- genomic/ genetic Pioneer target sources - disease networks
- academic partners - private partners - Sage Bionetworks, SGC,
Early Discovery
Arch2POCM and the Power of Crowdsourcing
• “Crowdsourcing:” the act of outsourcing tasks traditionally performed by an employee to a large group of people or community
• By making Arch2POCM’s clinically characterized probes available to all, Arch2POCM will seed independently funded, crowdsourced experimental medicine
• Crowdsourced studies on Arch2POCM probes will provide clinical information about the pioneer targets in MANY indications
Why have a “Common Stream” where there is No-‐IP on Compounds thru PhIIb?
• Broader data disseminaJon • Far fewer legal agreements to negoJate • Generates FTO • Only way to access KOL without hassle • More cost-‐effecJve • Not constrained by market consideraJons or
biased internal thinking
Why now?
• Economics • Genomics is creaJng many high-‐risk, high
opportunity targets in all diseases but no has the resources to take the risks
• Increased level of interest by public and private
• Health care costs bringing drug discovery to front of public policy
General Benefits of Arch2POCM For The Pharmaceutical Industry
1. Arch2POCM’s use of test compounds to de-risk previously unexplored biology enables pharma to initiate proprietary drug development with an array of unbiased, clinically validated targets Arch2POCM operates without patents and advances pairs of test
compounds through Ph II Test compounds are used by Arch2POCM and the crowd to define
clinical mechanisms for epigenetic targets impacting oncology
2. Arch2POCM crowdsourced research and trials provides “parallel shots on goal: by aligning test compounds to most promising unmet medical need”
3. Negative clinical trial information generated by Arch2POCM and the crowd will increase clinical success rates (as one can pick targets and indications more smartly)
4. Build methods to track and visualize crowd sourced data, clinical safety profiles and reporting of potential adverse event
Why Should Pharmaceutical Companies Invest in Arch2POCM? Some Possible Answers
1. Directly shape the Arch2POCM scientific strategy around how to best interrogate the high risk high opportunity epigenetics targets for Oncology and Immunology
2. Chose the components and where they are to be executed: i.e., targets, lead compounds, clinical indications and trial design
3. Potential to realize value for existing pharmaceutical assets that are not currently getting developed
- Contribute these compounds at Gate 2 of Arch2POCM (i.e., pre-IND)
4. Partnering with Regulatory Sciences groups at the FDA and EMEA and real time open dialogs on safety issues around the compounds being used to understand biology as opposed to make products
5. Partnerships with academics and patient groups improve the understanding around the importance of the Arch2POCM industry partners in the full value chain and enable building of trust with key leaders in both communities
Why Should Pharmaceutical Companies Invest in Arch2POCM? Some Possible Answers (cont)
6. Real time access to the live stream of project information during the Arch2POCM oncology projects: projected to provide lead time on key information vs non-participating pharma
– Information on the leads
– Information from the preclinical studies
– Information from the Arch2POCM tirals in real time before aggregated
– Information from the crowd-sourced trials in real time before aggregated
7. Direct/rapid access to epigenetic academic experts and their tools
8. Opportunity to take the Validated targets forward to approval or share in auctioning of these assets
• Funding to pursue and publish disruptive discovery research
• Sharing of resources and data among private and academic partners
• Development of basic discoveries toward therapies and cures
• Collaboration with private sector and regulatory scientists
• Education of students and public about nature and process of discovery, and understanding disease
• Exit options: extend/branch studies beyond arch2POCM
Arch2POCM Value Propositions For Academia
• The benefits of public funding investment in Arch2POCM are compounded by the efforts of the crowd.
• Public funding will support experimental medicine studies.
• Public funding will invest in a drug development model that protects paJent safety by eliminaJng redundant negaJve trials
Arch2POCM Value Propositions For Public Funders
• Opportunity to facilitate a greater number of trials on pioneer targets.
• Opportunity to leverage exisJng legacy clinical trial data to support improved drug development strategies. – By focusing on pioneer targets instead of compounds, regulators can
parJcipate pro-‐acJvely in Arch2POCM POCM development.
• Opportunity to play a leadership role in the development of regulatory and legal collaboraJons that would incenJvize the pharmaceuJcal industry to parJcpate in a pre-‐compeJJve clinical validaJon model
Arch2POCM Value Propositions For Regulators
• Arch2POCM and the crowd will accelerate the development of NCEs for unmet medical needs
• Crowdsourced research and trials represent an opportunity for “mulJple shots on goal” that will serve to align test compounds to most promising unmet medical need
• PaJent safety is protected because Arch2POCM will release negaJve clinical trial data so that redundant trials can stop
Arch2POCM Value Propositions For Patients
Epigene:cs/ Chroma:n Biology
A pioneer area of drug discovery
Histone
DNA
Lysine Lysine
How We Define Epigenetics
Modification Write Read Erase
Acetyl HAT Bromo HDAC Methyl HMT MBT DeMethyl
The Case for Epigenetics/Chromatin Biology
1. There are oncology drugs on the market (HDACs)
2. A growing number of links to oncology, notably many geneJc links (i.e. fusion proteins, somaJc mutaJons)
3. A pioneer area: More than 400 targets amenable to small molecule intervenJon -‐ most of which only recently shown to be “druggable”, and only a few of which are under acJve invesJgaJon
4. Open access, early-‐stage science is developing quickly – significant collaboraJve efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starJng points
Some Examples of Links to Cancer
• Ezh2 methyltransferase (enhancer of zeste homolog 2) – SomaJc mutaJons in B-‐cell lymphoma
• JARID1B demethylase (jumonji, AT rich interac:ve domain 2 ) – Linked to malignant transformaJon: expressed at high levels in breast and prostate
cancers; Knock-‐down inhibits proliferaJon of breast cancer lines and tumor growth
• G9A methyltransferase (euchroma:c histone-‐lysine N-‐methyltransferase 2) – Expressed in aggressive lung cancer cells: high expression correlates to poor
prognosis; G9a knockdown inhibits metastasis in vivo.
• MLL: myeloid/lymphoid or mixed-‐lineage leukemia – MulJple chromosomal transloca,ons involving this gene are the cause of certain
acute lymphoid leukemias and acute myeloid leukemias
• Brd4: (Bromodomain-‐containing protein 4)
– Implicated in t(15; 19) aggressive carcinoma: Chromosome 19 transloca,on breakpoint interrupts the coding sequence of a bromodomain gene, BRD4
• CBP bromodomain
– Oncogeneic fusions – Mutated in relapsing AML
Domain Family Typical substrate class* Total Targets
Histone Lysine demethylase
Histone/Protein K/R(me)n/ (meCpG) f q�
Bromodomain Histone/Protein K(ac) k/ �
R O Y A L
Tudor domain Histone Kme2/3 - Rme2s kC�
Chromodomain Histone/Protein K(me)3 f :�
MBT repeat Histone K(me)3 C�
PHD finger Histone K(me)n C/ �
Acetyltransferase Histone/Protein K W/�
Methyltransferase Histone/Protein K&R Eq�
PARP/ADPRT Histone/Protein R&E W/�
MACRO Histone/Protein (p)-ADPribose Wk�
Histone deacetylases Histone/Protein KAc WW�
f Ck�
The Epigenetics Universe (2009)
Druggable
Domain Family Typical substrate class* Total Targets
Histone Lysine demethylase
Histone/Protein K/R(me)n/ (meCpG) f q�
Bromodomain Histone/Protein K(ac) k/ �
R O Y A L
Tudor domain Histone Kme2/3 - Rme2s kC�
Chromodomain Histone/Protein K(me)3 f :�
MBT repeat Histone K(me)3 C�
PHD finger Histone K(me)n C/ �
Acetyltransferase Histone/Protein K W/�
Methyltransferase Histone/Protein K&R Eq�
PARP/ADPRT Histone/Protein R&E W/�
MACRO Histone/Protein (p)-ADPribose Wk�
Histone deacetylases Histone/Protein KAc WW�
f Ck�
The Epigenetics Universe (2011)
Now known to be amenable to small molecule inhibition
JQ1distributed to more than 200 labs world wide
Bromodomains Can Be Targeted by Small Molecules (2010)
Anti-tumour activity (FDG-PET)
4 days 50mg/kg IP
Selectivity Potency
40 nM (BRD4)
Co-crystal (JQ1 + BRD4)
Methyltransferases Can be Selectively Inhibited (Nature Chem Biol, in press 2011)
Structure based-design of a potent and selective G9a/GLP HMTase antagonist
• Global reduction in H3K9me2 levels • Phenocopies shRNA knockdown • Well tolerated by > 7 cell lines • Cell line specific reduction in clonogenicity • Reactivation of endogenous & exogenous genes in mouse iPS and ES cells • Differential effects on DNA methylation between mouse ES cells and human adult
cancer cells • modestly facilitates efficiency of iPS formation
• Alphascreen assay • Validation by global H3K27me3 demethylation
assay in JMJD3 transfected cells • 1.8 Å structure of JMJD3 catalytic domain with
8-hydroxyquinoline-5-carboxylic acid Overall fold of JMJD3 Binding mode of 8-hydroxyquinoline-5-carboxylate
8-hydroxyquinoline-5-carboxylic acid tested in HEK293 cells
Alphascreen assays used for identifying small molecule binders
Histone Demethylases Can be Targeted (JMJD3, unpublished)
• First structure of small molecule inhibitor bound to methyl-lysine binding domain.
• Potential relevance to reprogramming and regenerative medicine
Methyl-lysine-binding Proteins Can be Targeted by Small Molecules
Herold et al., J Med Chem (2011)
Bromodomain Field is Poised For Rapid Progress
• 31 crystal structures • 45 purified Bromo domains • Tm shift assay panel >30
bromodomains • 5 Alpha binding assays
across 4 subfamilies
As are Human Methyltransferases
• Purifiable HMTs: – In general, all the targets can be
purified to >90% purity in milligram quanJJes
– AcJvity of most of the proteins have been tested
As are Human Demethylases
SGC structure
Non SGC structure
Alphascreen in place
Catalytic domain purified
Assay Development
In vitro assayavailable
Cell assayavailable
Open Access Chemical Starting Points Will Be Available Sc
reen
ing
/ Ch
emis
try
Me Lys Binders
BRD
HMT
KDM HAT
Kd < 500 nM Selectivity > 30x
Active Kd < 5 µM
Weak
None
SUV39H2 EP300
L3MBTL1 L3MBTL3
WDR5
MYST3 DOT1L
UHRF1 SMYD3 JMJD2A Tu EZH2
HAT1 PRMT3
SETD8 CREBBP FALZ
BAZ2B PB1
GCN5L2 JMJD3
SETD7
BET 2nd PHF8
JMJD2 FBXL11
G9a/GLP BET
JARID1C
Probe Criteria
Kd < 100 nM Selectivity > 30x Cell IC50 < 1 µM
Aided By an Archipelago of Scientists Chemistry • GlaxoSmithKline • University of North Carolina
(CICBDD) • NovarJs • Pfizer Nov, 2010 • Eli Lilly Dec 2010 • Broad InsJtute Feb 2011 • James Bradner, Harvard • NCGC • Julian Blagg, ICR • Chris Abell, Cambridge • Ulrich Guenther, Birmingham • Stefan Lauffer, Tuebingen
Biology • Assam El-Osta, Baker Inst, Australia
– SETD7, Diabetes • Or Gozani, Stanford
– Biochemistry • James Ellis, Hospital for Sick Children,
Toronto – induced pluripotent stem cells
• Thea Tlsty, UCSF – breast epithelial models
• Broad Institute, Harvard – cancer cell line panel
• Rossi, U British Columbia – mouse leukemia model/G9a inhibitor
• James Bradner Harvard – DOT1L in leukemia – BET family
• David Wilson, Kevin Lee, GSK – Assays in disease tissue from RA and OA patients – Cell based assays
• Roland Schuele, Freiburg – JMJD2C in prostate cancer
• Juerg Schwaller, Basel – Bromo domains in MLL
Biological Reagents • Anthony Kossiakov, Chicago
– renewable binders • Alan Sawyer, EMBL
– monoclonal Antibodies • Karen Kennedy, Bill Skarnes
Sanger – knockout mice, knockout Embryonic
Stem cells
• Identification of Macro Domain Proteins as Novel O-Acetyl-ADP-Ribose Deacetylases. J Biol Chem (2011)
• UNC0638: Potent, Selective, and Cell-penetrant Chemical Probe of Protein Lysine Methyltransferases G9a and GLP. Nature Chem Biol. in press
• Recognition and specificity determinants of the human Cbx chromodomains. J Biol Chem. (2011)
• Animal Models of Epigenetic Regulation in Neuropsychiatric Disorders. Curr Top Behav Neurosci. (2011)
• A Selective Inhibitor and Probe of the Cellular Functions of Jumonji C Domain-Containing Histone Demethylases. J Am Chem Soc. Epub ahead of print (2011)
• Inhibition of the histone demethylase JMJD2E by 3-substituted pyridine 2,4-dicarboxylates. Org Biomol Chem. (2011)
• Inhibition of histone demethylases by 4-carboxy-2,2'-bipyridyl compounds. ChemMedChem. 6(5):759-64. (2011)
• Recognition of multivalent histone states associated with heterochromatin by UHRF1. J Biol Chem. (2011)
• Small-molecule ligands of methyl-lysine binding proteins. J Med Chem. 54(7):2504-11 (2011)
• The Replication Focus Targeting Sequence (RFTS) Domain Is a DNA-competitive Inhibitor of Dnmt1. J Biol Chem. 286(17):15344-51 (2011)
• Structural Chemistry of the Histone Methyltransferases Cofactor Binding Site. J Chem Inf Model. 51(3):612-623. (2011)
• The structural basis for selective binding of non-methylated CpG islands by the CFP1 CXXC domain. Nature Commun. Epub ahead of print (2011)
• Somatic mutations at EZH2 Y641 act dominantly through a mechanism of selectively altered PRC2 catalytic activity, to increase H3K27 trimethylation. Blood. 117(8):2451-9. (2011)
• Structural basis for the recognition and cleavage of histone H3 by cathepsin L. Nature Commun. (2011)
Epigenetics Archipelago Publications (2011)
The Benefits of the Arch2POCM Pre-competitive Model: Crowdsourced Studies
The Crowd
Clin
Arch2POCM Compounds And Data
Crowdsourced data on Arch2POCM test compound
• SAR med chem • Best indicaJon • Clinical data