bms 2010
TRANSCRIPT
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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
Sept 27, 2010
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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)
Sept 27, 2010
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http://camera.calit2.net/
Sept 27, 2010
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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/
Sept 27, 2010
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Support Infrastructure
San Diego Supercomputer Center
California Institute for Telecommunications& Information Technology
Sept 27, 2010
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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
Sept 27, 2010
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
Sept 27, 2010
http://bioinformatics.ucsd.edu/faculty/
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Rotation Projects
http://bioinformatics.ucsd.edu/page/53/
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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
Sept 27, 2010
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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
Sept 27, 2010