Transcript CS790 – Bioinformatics

CS790 – Bioinformatics
Introduction and overview
CS 790 – Bioinformatics
What is Bioinformatics?
DNA (and RNA)
Course Overview
Proteins
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What is Bioinformatics?
Computational
Biology
Bioinformatics
Genomics
Functional
genomics
Proteomics
Structural
bioinformatics
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Why is Bioinformatics Important?
 Applications areas include
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Medicine
Pharmaceutical drug design
Toxicology
Molecular evolution
Biosensors
Biomaterials
Biological computing models
DNA computing
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The Role of Computational Biology
GenBank BASEPAIR GROWTH
Source: GenBank
3,841
Millions
4,000
3,500
3,000
2,009
2,500
2,000
1,160
1,500
1,000
652
1
2 3
5
10
16
24
35
49
72
101 157
217
385
500
0
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
3D Structures
Growth:
Source:
http://www.rcsb.org/pdb/
holdings.html
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Fighting Human Disease
 Genetic / Inherited
• Diabetes
 Viral
• Flu, common cold
 Bacterial
• Meningitis, Strep throat
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Drug Development Life Cycle
Discovery
(2 to 10 Years)
Preclinical Testing
(Lab and Animal Testing)
Phase I
(20-30 Healthy Volunteers used to
check for safety and dosage)
Phase II
(100-300 Patient Volunteers used to
check for efficacy and side effects)
Phase III
(1000-5000 Patient Volunteers
used to monitor reactions to
long-term drug use)
$600-700 Million!
FDA Review
& Approval
Post-Marketing
Testing
Years
0
2
4
6
8
10
7 – 15 Years!
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Drug lead screening
5,000 to 10,000
compounds screened
5 Drug Candidates
enter Clinical Testing;
80% Pass Phase I
250 Lead Candidates in
Preclinical
Testing
30%Pass Phase II
80% Pass Phase III
One drug approved by the FDA
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What are we going to learn?
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DNA, Proteins, life, and disease: an overview
Basic chemistry introduction/review
Basic biochemistry: proteins
Basic biochemistry: DNA, genes, and
molecular evolution (Dr. Dan Krane, Biological
Sciences)
 Drug docking and screening, dealing with water
molecules: Dr. Raymer
 Student presentations: techniques in
bioinformatics
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Student Presentations
 Students will each make 1 or 2 one-hour presentations
on topics in bioinformatics
• Tutorial
• Survey
• Research Paper
 Each class, we’ll turn in either:
• A one-page summary of the previous presentation, or
• A mini-project assigned as part of the presentation.
 We’ll talk more about this next time
 Web page
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DNA is the blueprint for life
 Every cell in
your body has
23
chromosomes
in the nucleus
 The genes in
these
chromosomes
determine all
of your
physical
attributes.
Image source: Crane digital, http://www.cranedigital.com/
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Mapping the Genome
 The human genome project has provided us
with a draft of the entire human genome.
• Four bases:
A, T, C, G
• 3.12 billion basepairs
• 99% of these are
the same
• Polymorphisms =
where they differ
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How does the code work?
 Template for construction of proteins
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Proteins: Molecular machinery
 Proteins in your muscles allows you to move:
myosin
and
actin
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Proteins: Molecular machinery
 Enzymes
(digestion, catalysis)
 Structure (collagen)
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Proteins: Molecular machinery
 Signaling
(hormones,
kinases)
 Transport
(energy,
oxygen)
Image source: Crane digital, http://www.cranedigital.com/
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Example Case: HIV Protease
1. Exposure &
infection
2. HIV enters your cell
3. Your own cell reads
the HIV “code” and
creates the HIV
proteins.
4. New viral proteins
prepare HIV for
infection of other
cells.
© George Eade, Eade Creative Services, Inc.
http://whyfiles.org/035aids/index.html
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HIV Protease & Inhibition
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HIV Protease as a drug target
 Many drugs bind to
protein active sites.
 This HIV protease
can no longer
prepare HIV
proteins for
infection, because
an inhibitor is
already bound in
its active site.
HIV Protease + Peptidyl inhibitor (1A8G.PDB)
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Drug Discovery
 Target Identification
• What protein can we attack to stop the disease from
progressing?
 Lead discovery & optimization
• What sort of molecule will bind to this protein?
 Toxicology
• Does it kill the patient?
• Does it have side effects?
• Does it get to the problem spots?
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Drug discovery: past & present
 Put some of the infectious agent into thousands
of tiny wells
 Add a known drug lead compound into each
well.
• Try nearly every drug lead known.
 See which ones kill the agent…
• To small to see, so we have to use chemical tests
called assays
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Finding drug leads
 Once we have a target, how do we find some
compounds that might bind to it?
 The old way: exhaustive screening
 The new way: computational screening!
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Drug Lead Screening & Docking
?
 Complementarity
• Shape
• Chemical
• Electrostatic
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Problems in Bioinformatcs
 Genomics
• Gene finding
• Annotation
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Sequence alignment and database search
• Functional genomics
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Microarray expression, “gene chips”
 Proteomics
• Structure prediction
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Comparative modeling
• Function prediction
 Structural bioinformatics
• Molecular docking, screening, etc.
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