Bms 2010

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Philip E. Bourne Skaggs School of Pharmacy and Pharmaceutical Sciences [email protected] http://www.sdsc.edu/pb The BMS Bioinformatics Focus Sept 27, 2010

Transcript of Bms 2010

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Philip E. BourneSkaggs School of Pharmacy and

Pharmaceutical [email protected]

http://www.sdsc.edu/pb

The BMS Bioinformatics Focus

Sept 27, 2010

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The Bioinformatics/Comp. Biol. Distinction

• Bioinformatics – New tools and algorithms for the analysis and use of high throughput data

• See journal Bioinformatics or BMC Bioinformatics

• Computational Biology – Application of computational techniques to make new discoveries about living systems

• See journal PLoS Computational Biology

There are opportunities to study bothSept 27, 2010

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Bioinformatics In General

Biological Experiment Data Information Knowledge Discovery

Collect Characterize Compare Model Infer

Sequence

Structure

Assembly

Sub-cellular

Cellular

Organ

Higher-life

Year90 05

Computing Power

SequencingTechnology

Data

1 10 100 1000 100000

95 00

E.ColiGenome

C.ElegansGenome

ESTs

YeastGenome

Gene Chips

Virus Structure Ribosome

Metaboloic Pathway of E.coli

Complexity Technology

Brain Mapping

Neuronal Modeling

Cardiac Modeling

Human Genome

# People/Web Site

(C) Copyright Phil Bourne 1998

106 102 1

10

1000000

.1

GWAS

4th Gen

Translational Medicine

Meta-genomics

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Consider one Bioinformatics Growth Area Pioneered by a BMS Alumni

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Metagenomics: First Look at the Challenges

• 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

• PLoS Biology 2007 5(3) e74

http://plos.cnpg.com/lsca/webinar/venter/20070306/index.htmlSept 27, 2010

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What is Metagenomics?

• Technology– Sequencing DNA

extracted directly from the environment

– No cultures, no PCR

– Short reads• 500-800 bp• 80-100 bp (454)

– No assembly

• Concept– Direct study of

microbial communities– Minimal perturbation –

no cultures, no assumptions

– Fragmentary data, sampling rather than assembling

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Metagenomics: first results

• More then 99.5% of DNA in every environment studied represent unknown organisms– Culturable organisms are

exceptions, not the rule

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

• Everything we touch turns out to be a gold mine

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

cavity, human microbiome)

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http://camera.calit2.net/

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http://bioinformatics.ucsd.edu

• Emphasis on cross training and interdisciplinary activities

• Multiple departments• Over 40 faculty

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Example Courses

http://bioinformatics.ucsd.edu/page/99/

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Support Infrastructure

San Diego Supercomputer Center

California Institute for Telecommunications& Information Technology

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Sample Mentors & Project Areas

• Phil Bourne – Drug discovery, evolution, structure and function of signaling molecules

• Ruben Abagyan – Molecular Biophysics

• Steve Briggs – Stem Cells

• Bing Ren – Gene regulatory networks

• Palmer Taylor – structure and function of molecules involved in neurotransmission

• Terry Gaasterland – Microbial Genomics

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http://bioinformatics.ucsd.edu/faculty/

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• Trey Ideker – Network construction and analysis• Pavel Pevzner – Genome rearrangements• J Andrew McCammon – Electrostatic interactions• Wei Wang – Inference of gene regulatory

networks• Bernhard Palsson – Systems biology• Shankar Subramaniam – Functional genomics

Sample Mentors & Project Areas

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http://bioinformatics.ucsd.edu/faculty/

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Rotation Projects

http://bioinformatics.ucsd.edu/page/53/

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Questions?

[email protected]

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Example Projects from My Labhttp://www.sdsc.edu/pb/projects.htm

• Pharmaceutical Sciences - Competitive Binding of Major Pharmaceuticals

• From Physical Model of Nucleosome Organization Towards Genome Annotation

• Earth Sciences Meets Life Sciences• Scholarly Communication• Exploring the Flexibility versus Designability of

Protein Folds• What Makes Some Introns’ Positions Ultra-conserved? • Building a Meta-method for Assignment of Structural

Domains in Proteins

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A Reverse Engineering Approach to 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

Computational MethodologySept 27, 2010

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Repositioning TB

• TB Infects 6M people and kills 2M people per year

• Entacapone and tolcapone shown to have potential as InhA inhibitors

• Direct mechanism of action avoids M.tuberculosis resistance mechanisms

• Possess excellent safety profiles with few side effects

• Commercially available and easy to make

• Further in vitro, in vivo and clinical studies required

• Can potentially be applied to clinical practice directly

S. Kinnings, L. Xie N. Buchmeier and P.E. BourneSept 27, 2010

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A Systems Biology Approach to Explaining & Subsequently Minimizing Side Effects

PNAS Submitted

Strong BindingMedium Binding

Weak Binding

Positive Regulation

Negative Regulation

Positive & Negative RegulationSept 27, 2010

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Bioinformatics Final Examples..

• Donepezil for treating Alzheimer’s shows positive effects against other neurological disorders

• Orlistat used to treat obesity has proven effective against certain cancer types

• Ritonavir used to treat AIDS effective against TB

• Nelfinavir used to treat AIDS effective against different types of cancers

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