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Title: CS177 Lecture 10 Experimental Methods PCR, Xray crystallography, Microarrays


1
CS177 Lecture 10 Experimental Methods(PCR,
X-ray crystallography, Microarrays)
  • Tom Madej 11.21.05

2
Lecture overview
  • Polymerase chain reaction (PCR) and its
    applications.
  • X-ray crystallography and the Protein Data Bank
    (PDB).
  • Microarrays and applications.

3
Polymerase Chain Reaction (PCR)
  • A method that allows us to generate a large
    amount (relatively) of a particular DNA sequence
    even from an extremely small sample.
  • Exquisitely sensitive even the DNA from a single
    cell may suffice!
  • Numerous applications in biotechnology.

4
PCR main ideas
  • You need to know what you are looking for, e.g.
    the DNA sequence for a particular gene (the
    target).
  • Sample, primers, nucleotides to build new DNA
    strands, and Taq polymerase mixed together.
  • Mixture is subjected to cycles of heating,
    cooling, reheating, on the order of a few
    minutes.
  • If the target is present in the initial sample,
    the amount of it in the mixture will grow
    exponentially with the number of cycles.

5
ds-DNA target
primers
primers are complementary to opposite ends of
target seq.
6
PCR cycle
  • Mixture is heated to 90ºC for 1-2 minutes to
    separate the DNA strands (denature).
  • Temperature is dropped to 50º-60ºC so that
    primers can anneal to complementary regions.
  • Temperature is raised to 70ºC for 1-2 minutes to
    allow Taq polymerase to synthesize new DNA
    strands, starting at the primers this goes from
    5 to 3 for both strands.
  • Note The Taq polymerase is a DNA polymerase from
    Thermus aquaticus, a bacteria that lives in hot
    springs.

7
Polymerase Chain Reaction (PCR)
8
PCR notes
  • Primer selection is critical. The primers should
    be at least 15-20 bases to ensure specificity.
  • If you are unsure of the exact sequence, you can
    use degenerate primers, i.e. a mixture of
    primers (vary at third codon position).
  • Note that almost all of the product is exactly
    the target sequence you want, i.e. with flush
    ends.

9
PCR applications
  • Making a lot of protein! Use RT-PCR, reverse
    transcriptase PCR, to create DNA with introns
    removed and then insert it into bacteria to clone
    the gene. E.g. to make proteins for X-ray
    crystallography.
  • Medical diagnosis e.g. detect HIV viral proteins
    long before AIDS symptoms arise or rapid
    tuberculosis test.
  • Forensics detect trace amounts of DNA at a crime
    scene.

10
Methods to determine protein structures
  • X-ray crystallography (most important, over 80
    of structures in the PDB are obtained this way).
  • NMR spectroscopy (Nuclear Magnetic Resonance).
  • Electron microscopy uses a beam of electrons to
    create images (maybe issues with sample
    preparation and resolution in regards to
    applications to protein structure determination).

11
Protein crystallography steps
  • Grow crystals of the protein that diffract well
    (a difficult step, can take from weeks to
    years!).
  • Obtain the X-ray diffraction data.
  • Compute electron density maps.
  • Refinement calculate an atomic model to fit
    electron density compare the diffraction data
    computed from the model with the actual data
    refine the model to fit the data (iterate).

12
Protein crystals
http//www-structure.llnl.gov/crystal_lab/Crys_lab
.html
13
Protein crystal
molecule
crystal
The unit cell is the basic unit of symmetry in
the crystal.
14
Facts about protein crystals
  • In contrast e.g. to salt or quartz crystals,
    protein crystals are mostly water (due to the
    irregular shape of the molecule) and therefore
    fragile.
  • Since they are mostly water, the actual protein
    structures obtained must be similar to their
    conformations in vivo.
  • To preserve the crystal in the X-ray beam, it is
    kept at a very low temperature (100ºK).

15
X-ray diffraction
  • The incident beam of X-rays is diffracted by the
    electrons in the protein molecules in the
    crystal.
  • Some of the diffracted waves will interfere
    constructively, and others will interfere
    destructively.
  • This results in a diffraction pattern of spots of
    varying intensity on the detector.

16
Illustration of diffraction
http//www.eserc.stonybrook.edu/ProjectJava/Bragg/
index.html
17
X-ray diffraction pattern
18
Analysis of the diffraction pattern
  • The diffraction pattern is analyzed by
    mathematical/computation methods (Fourier
    analysis) to produce an electron density map.
  • This gives a 3-dimensional image of the molecule
    that will be subjected to further processing and
    analysis.

19
Electron density maps at different resolutions
http//www-structure.llnl.gov/Xray/101index.html
20
Refinement
  • Refinement is an iterative process one
    constructs an atomic model based on the electron
    density, then computes diffraction data from the
    model, which is compared to the actual
    diffraction data.
  • The crystallographic R-factor is a measure of how
    well the model fits the diffraction data.
  • Can be subject to error! The electron density
    for certain pairs of amino acid residues is
    extremely similar.

21
Fitting amino acid residues into the electron
density map
22
X-ray crystallography summary
http//www.bnl.gov/discover/Spring_04/crystallogra
phy.asp
23
NMR
  • Based on magnetic moments of atomic nuclei.
  • NMR spectra give information about distances
    between atoms in the molecule.
  • Applied to protein molecules in solution (no
    crystals needed!).
  • Only works well for smaller proteins, e.g. 100
    residues or less (or so).
  • A different set of mathematical/computational
    tools is involved.
  • Note The different models represent different
    structures compatible with the distance
    contraints, not actual conformations of the
    molecule.

24
PDB
25
PDB File Header
HEADER ISOMERASE/DNA
01-MAR-00 1EJ9 TITLE CRYSTAL STRUCTURE OF
HUMAN TOPOISOMERASE I DNA COMPLEX
COMPND MOL_ID 1
COMPND 2
MOLECULE DNA TOPOISOMERASE I
COMPND 3 CHAIN A

COMPND 4 FRAGMENT C-TERMINAL DOMAIN, RESIDUES
203-765 COMPND 5 EC
5.99.1.2
COMPND 6 ENGINEERED YES

COMPND 7 MUTATION YES
COMPND 8
MOL_ID 2
COMPND 9 MOLECULE DNA (5'-

COMPND 10 D(CAPAPAPAPAPGPAPCPTPCPAP
GPAPAPAPAPAPTP COMPND 11
TPTPTPT)-3')
COMPND 12 CHAIN C

COMPND 13 ENGINEERED YES
COMPND 14
MOL_ID 3
COMPND 15 MOLECULE DNA (5'-

COMPND 16 D(CAPAPAPAPAPTPTPTPTPTPCP
TPGPAPGPTPCPTP COMPND 17
TPTPTPT)-3')
COMPND 18 CHAIN D

COMPND 19 ENGINEERED YES
SOURCE MOL_ID
1
SOURCE 2 ORGANISM_SCIENTIFIC HOMO
SAPIENS
SOURCE 3 EXPRESSION_SYSTEM_COMMON BACULOVIRUS
EXPRESSION SYSTEM SOURCE 4
EXPRESSION_SYSTEM_CELL SF9 INSECT CELLS
SOURCE 5 MOL_ID 2

SOURCE 6 SYNTHETIC YES
SOURCE 7
MOL_ID 3
SOURCE 8 SYNTHETIC YES

KEYWDS PROTEIN-DNA COMPLEX, TYPE I
TOPOISOMERASE, HUMAN
REMARK 1
REMARK 2

REMARK 2 RESOLUTION. 2.60
ANGSTROMS.
REMARK 3
REMARK 3
REFINEMENT.
REMARK 3 PROGRAM
X-PLOR 3.1
REMARK 3 AUTHORS BRUNGER
REMARK 280

REMARK 280 CRYSTALLIZATION
CONDITIONS 27 PEG 400, 145 MM MGCL2, 20
REMARK 280 MM MES PH 6.8, 5 MM TRIS PH 8.0,
30 MM DTT REMARK 290
...
26
From Coordinates to Models
1EJ9 Human topoisomerase I
27
Annotating Secondary Structure
1EJ9 Human topoisomerase I
a-Helices ß-strands coils/loops
28
Creating 3D Domains
3D Domain 0 1EJ9A0 entire polypeptide
29
Creating 3D Domains
1EJ9A1
1EJ9A4
1EJ9A3
1EJ9A5
1EJ9A2
lt 3 Secondary Structure Elements
30
Microarrays
  • Used to study gene expression levels in cells.
  • Cells can differ dramatically in the amounts of
    various proteins that they synthesize e.g. due
    to different cell types or different
    external/internal conditions.
  • In fact, in higher level organisms only a
    fraction of the genes in a cell are expressed at
    a given time, and that subset depends on the cell
    type.
  • Via microarrays it is possible to study the
    expression levels of tens of thousands of genes
    simultaneously.

31
Microarray technology
  • Physically, a microarray is just a glass slide
    with spots of DNA on it each spot is a probe (or
    target).
  • The DNA is single-stranded cDNA (complementary)
    and may consist of an entire gene or part of one
    (an oligonucleotide consisting of 50 bases or
    so).
  • If the microarray is exposed to a solution
    containing mRNA, then the mRNA molecules will
    bind to those probes to which they are
    complementary.

32
Microarray probes
ssDNA gene sequences or oligos
33
Microarray technology
  • Thousands of probes can fit on a single slide.
  • The slides can be spotted by robots.
  • Of course, what genes you can study with a given
    microarray depends on the collection of probes on
    it.
  • There are a number of commercial manufacturers
    e.g. Affymetrix, Agilent, Amersham.
  • Theyre expensive!

34
Microarray experiments
  • Start with two cell types, e.g. healthy and
    diseased.
  • Isolate mRNA from each cell type, generate cDNA
    with fluorescent dyes attached, e.g. green for
    healthy and red for diseased.
  • Mix the cDNA samples and incubate with the
    microarray.
  • After incubation the cDNA in the samples has had
    a chance to bind (hybridize) with the probes on
    the chip.
  • The chip is read by a scanner that uses lasers to
    excite the fluorescent tags the intensity levels
    of the dyes are recorded for each probe gene and
    stored in a computer.

35
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36
Microarray data representation
  • There is a standard color scale representation,
    as follows.
  • Red means the gene produced more mRNA in the
    experimental condition green means the gene
    produced more mRNA in the control.
  • Black means equal amounts of mRNA for both
    experiment and control.
  • If e.g. there were 5 times as much mRNA for the
    experimental condition compared to the control,
    we would say there was a 5-fold induction 1/5 as
    much would be 5-fold repression.
  • The data is recorded numerically as the log base
    2 of the expression ratio.

37
Microarray data
38
Microarray data analysis
  • Since there are typically so many genes, it is
    useful to cluster the genes based on similar
    expression patterns.
  • Different clustering algorithms may be used, e.g.
    hierarchical with different metrics, or k-means,
    k-medians.
  • It may also be useful to cluster the samples
    (well see this shortly).
  • Other statistical methods may be useful, e.g.
    support vector machines (SVM).

39
Acute Lymphoblastic Leukemia (ALL)
  • Constitutes 75 of annual diagnoses of childhood
    leukemia.
  • Long-term outlook has improved dramatically since
    about 1970. At that time the long term disease
    free survival rate (LTDFS) was under 10 at
    present it is over 80.
  • There is still a risk of relapse in 20 of
    patients.

40
ALL (cont.)
  • The LTDFS rate improved because it was recognized
    that ALL is heterogeneous, and the therapy should
    be tailored to the subtype so as to improve the
    odds of a successful treatment (e.g. bone marrow
    transplant vs. chemotherapy).
  • Important subtypes include T-ALL, E2A-PBX1,
    BCR-ABL, TEL-AML1, MLL rearrangement, and
    hyperdiploid gt 50 chromosomes.

41
Cancer Cell, March 2002, v. 1 133-143.
42
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43
Cancer Cell, March 2002, v. 1 133-143.
44
Cancer Cell, March 2002, v. 1 133-143.
45
Cancer Cell, March 2002, v. 1 133-143.
46
Science v. 306, Oct. 22, 2004 630-631.
47
Science v. 306, Oct. 22, 2004 630-631.
48
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49
Abstract from S.A. Mitchell et al.
50
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