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Transcript of Bms 2010
Philip E. BourneSkaggs School of Pharmacy and
Pharmaceutical [email protected]
http://www.sdsc.edu/pb
The BMS Bioinformatics Focus
Sept 27, 2010
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
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
Consider one Bioinformatics Growth Area Pioneered by a BMS Alumni
Sept 27, 2010
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
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
Sept 27, 2010
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
http://camera.calit2.net/
Sept 27, 2010
http://bioinformatics.ucsd.edu
• Emphasis on cross training and interdisciplinary activities
• Multiple departments• Over 40 faculty
Sept 27, 2010
Example Courses
http://bioinformatics.ucsd.edu/page/99/
Sept 27, 2010
Support Infrastructure
San Diego Supercomputer Center
California Institute for Telecommunications& Information Technology
Sept 27, 2010
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/
• 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/
Rotation Projects
http://bioinformatics.ucsd.edu/page/53/
Sept 27, 2010
Questions?
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
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
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
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
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