Chattanooga Research Institute Presentation

Worldwide Protein Data Bankwww.wwpdb.org

Computational BiologyPhilip E. Bourne PhD

[email protected]

http://www.sdsc.edu/pb

02/07/2011 Computational Biology Summit Chattanooga

Thanks to Howard Asher for some slides

mailto:[email protected]

http://www.sdsc.edu/pb

Agenda

Computational Biology

– What is it?

– What are some success stories to date

– What pitfalls can be avoided based on the experiences of others

– What opportunities does it afford the region from a research, education and hence economic perspective?


Biology has moved from being an observational to an analytical science


Biology covers vast scales



Comparative Genomics

Neural Circuits

Biochemical pathways underlying drug addiction

Adaptive fast walking

Object recognition

Biologics


What drove these research directions?


Technologies

producing

digital

Information


Num

ber

of r

elea

sed

entr

ies

Year

Its not just about numbers its about complexity

Courtesy of the RCSB Protein Data Bank


June 26, 2000 was a milestone with the mapping of the human genome

U.S. President Bill Clinton listens to British Prime Minister Tony Blair as Celera President J Craig Venter looks on during a joint teleconference announcement in the East Room of the White House, June 26, 2000. Venter's company Celera Genomics Corporation participated in a publicly financed Human Genome Project with private efforts and have both completed the first rough map of the human genome, the working blueprint for human beings. The discovery is seen as one of history's great scientific milestones.

British Prime Minister Tony Blair

Announced by then US president Bill Clinton and the British Prime Minister Tony Blair

02/07/2011

Today informatics ≥ 2 x data ≅3 months

Subdisciplines of “matics”

Computational Biology Summit Chattanooga

PharmacyInformatics

BiomedicalInformatics

Bioinformatics

Drug dosingPharmacokineticsPharmacy InformationSystems

Electronic HRDecision support

AlgorithmsGenomicsProteomicsSystems

Note: These are only representative examples

Agenda


– What is it?





Metagenomics

New type of genomics New data (and lots of

it) and new types of data– 17M new (predicted

proteins!) 4-5 x growth in just few months and much more coming

– New challenges and exacerbation of old challenges

Metagenomics: early results

More then 99.5% of DNA in every environment studied represent unknown organisms

Most genes represent distant homologs of known genes, but there are thousands of new families

Environments being studied:– Water (ocean, lakes)– Air– Soil– Human body (gut, oral

cavity, human microbiome)

Metagenomics new discoveriesEnvironmental (red) vs. Currently Known PTPases (blue)

Higher eukaryotes

1

23

4

Personalized medicine

Personalized medicine

Right Therapeutic—Right Dose—Right Time—Right Place—Right Person& Understanding of genetic contributions to disease and to its treatment…


Your health—optimized to you



We know very little about how the major drugs we take work

We know even less about their side effects Drug discovery seems not to have moved into

the omics era The cost and time of bringing a drug to market

is huge ~ $1 Bn The cost of failure is even higher e.g., Vioxx

~ $5 Bn Fatal diseases are neglected because they do

not make money

Drug discovery – the sad truth



Why the failure?


Polypharmacology - 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


Why the failure?

What can be done about it?


High-throughput computational drug discovery can Be applied on three axes

Target

Disease

Drug

Cheminfomatics

HTS

Docking

One to Multiple TargetsBioinformatics

Associative Transfer of Indications


Consider one example of using the corpus as a whole from our own research – high throughput hypothesis generation for use in drug discovery


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

Computational Biology Summit Chattanooga

02/07/2011

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

drug

s

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

02/07/2011

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


Agenda


– What is it?





1985 1990 1995 2000 2005 2010

Cray-XMP48220 Mflops

Cray C905 Gflops

Cray T3E1 Tflops

SDSC Abbreviated Timeline

http://www.sdsc.edu/News%20Items/PR101510_25years.html

SDSC Lessons Learned

There is a tension between engineering and science – you need people at the interface

People and the services they provide are what make the difference

Fundamental shift from computer speed to data storage

The future is open with provenance Beware the cloud


SDSC lessons learned

More focus on education

More focus on integration with surrounding communities

More focus on outreach


Agenda


– What is it?





From the Art of Medicine to the Science of Health…

Genomic Era

Data Information Knowledge Wisdom

Art Transformation Science

2010 the Tipping Point

20202000Art ofMedicineat theorgan level

Science ofHealthat the

molecular level 2010


How to take advantage of this change?

Pick important problems and become a source of valuable data and human resources for solving those problems

Depositor locations

Download locations

RCSB PDB

PDBe

PDBj

Depositions since 2000



How to take advantage of this change?

Enable the use of those data and resources in ways that the next generation will build upon

www.scivee.tv


Enjoy the ah ha moments

Acknowledgements

Funding Agencies: NSF, NIGMS, DOE, NLM, NCI, NCRR, NIBIB, NINDS, NIDDK


Questions?

[email protected]


mailto:[email protected]

What is the Protein Data Bank (PDB)?

The single community owned worldwide repository on the structures of publically accessible biological macromolecules

A resource used by ~ 200,000 individuals per month

A resource distributing equivalent to ¼ the National Library of Congress each month


Chattanooga Research Institute Presentation

Education

nature chemical biology

computational biologyphilip

drug binds

known drug binding sites

human microbiome

human beings

human genome project

research directions