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Lecture: DNA nanostructuresGoals: show strategies
and examples
of
bottom-up
assembly
of
DNA-based
nanostructures
Construction with “Smart Bricks”
The bricks
that
make
a nano-object
can already
have
the information necessary
to
self- assemble
bottom-up if
placed
in the right conditions, without
any
other
external
intervention
by
some ‘large
scale’
agent.
Static structures based on the DNA linear helixThese
are structures
made
by
design. They
exploit the Watson-Crick
pairing
to
make
a doube-helix
from
separate parts.
For
example, there
are techniques
that
can attach
new
objects
to
DNA, allowing to
assemble
objects
with
funcions
that
are not
normally
assembled.
DNA serves
as
the coupling
signal.
Tomkins
et
al. ChemBioChem
2001
“Asymmetrical
Protein-DNA
dumbbells”
organic
synthesis
of modfied
oligonucleotides that
can be
derivatized
with
proteins
Christof Niemeyer: the preparation
of
DNA-protein
hybrids
and the creation
of objects.
Exploiting
the tetravalent Streptavidin, it
is
possible
to
attach
more oligonucleotide molecules
to
the same
bridging
streptavidin. Different
geometries
can be
obtained
(a small
ring or branched
structures). As
another
example, streptavidin can be
immobilized
on a long
RNA via a complementary probing
oligonucleotide
bound
to
the streptavidin.
Building on a surfaceSurface
localization
is
a facile method
to
imobilize
a nanoobject
in a precise
location in space. You
can then
study
the object, or use
it.
The presence
of
the surface
leads
to
a serious
change
in the behavior
of
molecules, as
these
are not
free to
move around
any
more and they
have
a wall
next
to
them
(excluded
volume effecs). The reduced
dimensionality
also
increases
the effective
molecular concentrations.
Two examples of the use of DNA to make protein layers or to localize proteins specificallyin order to make a proten microarray.
DNA detection thanks to the formation of large adducts beginning from colloidal nanoparticles (Chad Mirkin and Paul Alivisatos)The color of
colloidal
particles
depends
on their
size
(other
parameters
left
untouched) due to
plasmon resonance. Solutions
of
10-20
nm
gold
nanoparticles
are red, while
larger
aggregates
are bluish.This
provides
with
a strategy
towards
a colorimetric
assay
of
the presence
of
a sequence
of
DNA.
Scanometric detection of DNA hybridization (Chad Mirkin)According
to
the same
strategy, colloidal
gold
nanoparticles can be
tethered
to
a probe sequence
on the surface
only
if
a DNA sequence
complementary
both
to
the surface
bound
probe and to
the nanoparticle-bound
probe is
present. A ‘sandwich’
is
obtained. If
nanoparticles of
different
size
are used, a colorimetric
response
can be
obtained
also
for
signals
such
as
sequence
polymorphism
and they
can be
localized
on only
one
spot on the surface.
Afer
binding, the gold nanoparticles can be
developed through
Ag+
reduction: Silver nanopaticles
then
fuse and lead
to
a visible
stain
on the surface. A common and cheap flatbed
scanner can be
used
to
read
the signal
of
only
a few
analyte
molecules
recognized
by
the DNA microarray.
DNA Computing: DNA vs. Silicon(http://arstechnica.com/reviews/2q00/dna/dna-2.html)
Transistor-based computers typically handle operations in a sequential manner. Of
course
there
are multi-processor
computers, and modern
CPUs
incorporate some parallel
processing, but
in general, in the basic
von Neumann architecture
computer, instructions
are handled
sequentially. Typically, increasing
performance of
silicon
computing
means
faster
clock cycles
(and larger
data paths), where
the emphasis
is
on the speed
of
the CPU and not
on the size
of
the
memory.
For
DNA computing, though, the power
comes
from
the memory
capacity
and parallel processing. For
example, let's look at the read
and write
rate of
DNA. In
bacteria, DNA can be
replicated
at a rate of
about
500 base pairs
a second. But this
is
only
1000 bits/sec, which
is
a snail's pace when
compared
to
the data
throughput
of
an
average
hard drive. But
you
can allow
many
copies
of
the replication
enzymes
to
work on DNA in parallel. First of
all, the replication
enzymes
can start on the second
replicated
strand
of
DNA even
before
they're finished copying
the first one. So already
the data rate jumps
to
2000 bits/sec. But
look
what
happens
after
each
replication
is
finished
-
the number
of
DNA strands increases
exponentially
(2^n after
n iterations). With
each
additional
strand, the
data rate increases
by
1000 bits/sec. So after
10 iterations, the DNA is
being replicated
at a rate of
about
1Mbit/sec; after
30 iterations
it
increases
to
1000
Gbits/sec. This
is
beyond
the sustained
data rates
of
the fastest
hard drives.
The Adleman experiment
Suppose that
I live in LA, and need
to
visit
four
cities: Houston, Chicago, Miami, and NY, with
NY being
my
final
destination. The airline
I’m taking
has
a specific
set of
connecting
flights
that
restrict
which
routes
I can take (i.e. there
is
a flight from
L.A. to
Chicago, but
no flight from
Miami to
Chicago). What
should
my
itinerary
be
if
I
want
to
visit
each
city only
once?
Starting
from
L.A.
you
need
to
fly
to
Chicago, Dallas, Miami and then
to
N.Y.
Any
other
choice
of
cities
will
force
you
to
miss a destination, visit
a city twice, or not make
it
to
N.Y.
For
six, seven, or even
eight
cities, the problem
is
still
manageable. However, as the number
of
cities
increases, the problem
quickly
gets
out of
hand. Assuming
a
random
distribution
of
connecting
routes, the number
of
itineraries
you
need
to check
increases
exponentially. So you
will
need
a computer ...or perhaps
DNA.
Adleman
first generated
all
the possible
itineraries
and then
selected
the correct itinerary. This
is
the advantage
of
DNA. It’s small
and there
are combinatorial
techniques
that
can quickly
generate many
different
data strings. Since
the enzymes
work on many
DNA molecules
at once, the selection
process
is
massively
parallel.
Specifically, the method
based
on Adleman’s experiment
would
be
as
follows:1 Generate all
possible
routes.
2 Select
itineraries
that
start with
the proper
city and end with
the final
city.
3 Select
itineraries
with
the correct
number
of
cities.4 Select
itineraries
that
contain
each
city only
once.
All
of
the above
steps
can be
accomplished
with
standard molecular
biology techniques.
Part I: Generate all possible routes
Strategy: Encode
city names
in short DNA sequences. Encode
itineraries
by connecting
the city sequences
for
which
routes
exist.
DNA can simply
be
treated
as
a string
of
data. For
example, each
city can be represented
by
a "word" of
six
bases:
Los Angeles: GCTACGChicago: CTAGTADallas: TCGTACMiami: CTACGGNew York: ATGCCG
The entire
itinerary
can be
encoded
by
simply
stringing
together
these
DNA sequences
that
represent
specific
cities. For
example, the route
from
L.A
-> Chicago -
> Dallas -> Miami -> New York would
simply
be GCTACGCTAGTATCGTACCTACGGATGCCG, or equivalently
it
could
be
represented
in double
stranded
form
with
its
complement
sequence.
Itineraries
can then
be
produced
from
the city encodings
by
linking
them
together
in proper
order. For
example, you
can encode the routes between cities by
encoding
the compliment
of
the second
half
(last three
letters) of
the departure
city and the first half
(first three
letters) of
the arrival
city. For
example
the route
between
Miami (CTACGG) and NY (ATGCCG) can be
made
by
taking
the second half
of
the coding
for
Miami (CGG) and the first half
of
the coding
for
NY (ATG).
This
gives
CGGATG. By
taking
the complement
of
this
you
get, GCCTAC, which not only uniquely represents the route from Miami to NY, but will connect the DNA representing Miami and NY by hybridizing itself to the second half of the code representing Miami (...CGG) and the first half of the code representing NY (ATG...). For
example:
Random
itineraries
can be
made
by
mixing city encodings
with
the route
encodings. Finally, the DNA strands can be connected together by an enzyme called ligase. What
we
are left
with
are strands
of
DNA representing
itineraries
with
a
random
number
of
cities
and random
set of
routes. For
example:
We
can be
confident
that
we
have
all
possible
combinations
including
the correct one
by
using
an
excess
of
DNA encodings, say
1013
copies
of
each
city and each route
between
cities.
Part II: Select itineraries that start and end with the correct cities
Strategy: Selectively
copy and amplify
only
the section
of
the DNA that
starts
with
LA and ends
with
NY by
using
the Polymerase
Chain Reaction.
After
Part I, we
now
have
a test tube full of
various
lengths
of
DNA that
encode possible
routes
between
cities. What
we
want
are routes
that
start with
LA and end
with
NY. To
accomplish
this
we
can use
a technique
called
Polymerase
Chain Reaction
(PCR), which
allows
you
to
produce many
copies
of
a specific
sequence
of
DNA. So to
selectively
amplify
the itineraries
that
start and stop with
our
cities
of interest, we
use
primers
that
are complimentary
to
LA and NY. What
we
end up with
after
PCR is
a test tube full of
double
stranded
DNA of
various
lengths, encoding itineraries
that
start with
LA and end with
NY.
Part III: Select itineraries that contain the correct number of cities.
Strategy: Sort
the DNA by
length
and select
the DNA whose
length
corresponds
to 5 cities.
Our
test tube is
now
filled
with
DNA encoded
itineraries
that
start with
LA and end with
NY, where
the number
of
cities
in between
LA and NY varies. We
now
want
to
select
those
itineraries
that
are five
cities
long. To
accomplish
this
we
use
Gel Electrophoresis
We
can then
simply
cut out the band of
interest to
isolate DNA of
a specific
length. Since
we
known
that
each
city is
encoded
with
6 base pairs
of
DNA, knowing
the
length
of
the itinerary
gives
us
the number
of
cities. In this
case we
would
isolate the DNA that
was
30 base pairs
long (5 cities
times
6 base pairs).
Part IV: Select itineraries that have a complete set of cities
Strategy: Successively
filter
the DNA molecules
by
city, one
city at a time
by
affinity purification: the compliment
of
the sequence
in question
to
a substrate
like
a
magnetic
bead.
The beads
are then
mixed
with the DNA. DNA, which
contains
the sequence
you're after
then hybridizes
with
the complement
sequence
on the beads. These beads
can then
be
retrieved
and the DNA isolated.
So we
now
affinity
purify
fives
times, using
a different
city complement
for
each
run. If an
itinerary
is
missing
a city, then
it
will
not
be
"fished
out" during
one
of
the runs
and
will
be
removed
from
the candidate pool. What
we are left with are the are itineraries that start in LA, visit each city once, and end in NY. This
is
exactly
what
we
are
looking
for. If
the answer
exists
we
would
retrieve
it
at this
step.
Reading out the answer: simply
sequence
the DNA strands.
However, since
we
already
have
the sequence
of
the city encodings
we
can use
an
alternate method
called
graduated PCR. Here
we
do a series
of
PCR amplifications
using
the primer
corresponding
to
L.A., with
a different
primer
for
each
city in succession. By
measuring
the various
lengths
of
DNA for
each
PCR product
we
can piece
together
the final
sequence
of
cities
in our
itinerary. For
example, we
know
that
the DNA itinerary
starts
with
LA and is
30 base pairs
long, so if
the PCR product
for
the LA and Dallas primers
was
24 base pairs
long, you
know
Dallas is
the fourth
city in the itinerary
(24 divided
by
6). Finally, if
we
were
careful
in our
DNA manipulations
the only
DNA left
in our
test tube should
be
DNA itinerary
encoding
LA, Chicago, Miami, Dallas, and NY. So if
the succession
of
primers
used
is
LA & Chicago, LA & Miami, LA & Dallas, and LA & NY, then
we
would
get
PCR products
with
lengths
12, 18, 24, and 30 base pairs.
CaveatsAdleman's experiment
solved
a seven
city problem, but
there
are two
major
shortcomings
preventing
a large
scaling
up of
his
computation. The complexity
of the traveling
salesman problem
simply
doesn’t disappear
when
applying
a different
method
of
solution
-
it
still
increases
exponentially. For
Adleman’s method, what scales
exponentially
is
not
the computing
time, but
rather
the amount
of
DNA: more
than
a few
people have
pointed
out that
using
his
method
to
solve a 200 city HP problem
would
take an
amount
of
DNA that
weighed
more than
the earth. Another
factor
that
places
limits
on his
method
is
the error
rate for
each
operation. Since these
operations
are not
deterministic
but
stochastically
driven
(we
are doing
chemistry
here), each
step
contains
statistical
errors, limiting
the number
of iterations
you
can do successively
before
the probability
of
producing
an
error
becomes
greater
than
producing
the correct
result. For
example
an
error
rate of
1% is
fine for
10 iterations, giving
less
than
10% error, but
after
100 iterations
this
error
grows
to
63%.
CONCLUSION:
So will
DNA ever
be
used
to
solve a traveling
salesman problem
with
a higher number
of
cities
than
can be
done
with
traditional
computers? Well, considering
that
the record is
a whopping
13,509 cities, it
certainly
will
not
be
done
with
the procedure described
above. It
took
this
group
only
three
months, using
three
Digital
AlphaServer
4100s (a total of
12 processors) and a cluster of
32 Pentium-II
PCs. The solution
was possible
not
because
of
brute force
computing
power, but
because
they
used
some
very
efficient
branching rules. This
first demonstration
of
DNA computing
used
a rather
unsophisticated
algorithm, but
as
the formalism
of
DNA computing
becomes
refined, new
algorithms
perhaps
will
one
day
allow
DNA to
overtake
conventional computation
and set a new
record.
On the side of
the "hardware" (or should
I say
"wetware"), improvements in biotechnology are happening at a rate similar
to
the advances
made
in the
semiconductor industry. For
instance, look at sequencing; what
once took
a graduate student
5 years
to
do for
a Ph.D
thesis
takes
Celera
just one
day. With
the
amount
of
government
funded
research
dollars
flowing
into
genetic-related
R&D
and with
the large
potential
payoffs
from
the lucrative pharmaceutical
and medical-related
markets, this
isn't surprising. Just look at the number of advances in DNA-related technology that happened in the last five years: "DNA chips," the Human
Genome
Project is
producing
rapid
innovations
in sequencing
technology. The future of
DNA manipulation
is
speed, automation, and miniaturization.
Static structures based on branched forms of DNA
“DNA is every designer’s dream, being at the same time the blueprint of the structure and the structure itself”
[N.C. Seeman]
Structural DNA nanotechnology (as Ned Seeman puts it)“A key motivation
for
constructing
objects
from
DNA is
to
generate rational
means
for
constructing
periodic
matter. At least
three
properties
are necessary
for
the components
of systems
where
this
is
possible: (a) The predictable
specificity
of intermolecular
interactions
between
components; (b) the local
structural
predictability
of intermolecular
products; and (c) the structural
rigidity
of the components”
The Holliday
junction
Making
things
with
the blocked Holliday
junction C
hurc
h of
S. F
ranc
is -
Evo
ra, P
ortu
gal
Making
non- natural
objects
with
nantural materials
“The nucleic-acid ‘system’ that operates in terrestrial life is optimized (through evolution) chemistry incarnate. Why not use it ... to allow human beings to sculpt something new, perhaps beautiful, perhaps useful, certainly unnatural.” Roald
Hoffmann, su American Scientist, 1994
DNA is
perfect
as
a nanotech
brick: 2 nm
diameter, 3.4 nm
pitch, 50 nm
persistence length, a fully
nanoscale object
.
Sticky
ends
cohesion
is
probably
the best example
of
programmable
molecular
recognition: you
can have
a lot
of
different
possible
sticky
ends
(4N
for
N-long
ends) and the cohesion
product
is
the normal
double
helix
(structural
predictability). Solid
phase
oligonucleotide synthesis
makes
molecular
programming
attainable. Molecular
interaction
can be
programmed
to
be
specific.
WHY NUCLEIC ACIDS? Can you
achieve
the same
using
antigens and antibodies, for
instance? You
could
probably
get
a similar
affinity, but
the relative orientation
of
partners
would
have
to
be
defined
for
each
partner. This
is
why
nucleic
acids
are quite
unique, they
provide
a readily
available
programmable
system for
organizing
molecular
assembly.
Why building with DNA?
A general
route
towards
crystallization
of
molecules?
From linear to branched DNA: the Holliday juncion as a nanoconstruction brick
DNA Duplex, most
of
the nucleic
acids
is
in linear
form
An intermediate in recombination
J1 JUNCTIONJ1 = Holliday
junction
with
a
modified
sequence
that
prevents the branch-point
migration
that
is
fundamental
in biological recombination
A STABLE STRUCTURE
Important
for:
1-
studying
the structure
of
the Holliday
junction
2-
a brick
for
structural
DNA nanotechnology
Nadrian
C. Seeman
(Seeman, N. C. (1982) J. Theor. Biol. 99, 237-247)
Physical ligation
Designing a parallelogram
Informatics ligation
Nadrian
C. Seeman
also
developed
sequence
selection
software
•10% non-denaturing
gel• 4°C
1.1+2+3+4+5+62.1+2+3+4+53.1+2+3+44.1+2+35.1+56.4M. pBR322 marker
434
267234213192184
123
1048980
645751
6 5 4 3 2 1 M
PAGE characterization of a parallelogram
2
1
3
4
5
6
375-350
1 2 3 4 5 6M32P labelling
•10% native PAGE, RT
•Lanes
1-6: only
one
oligo
is labelled
in each
•Lane M: HOT marker –
25 bp ladder
HOT ATP + strand
HOT (5’) strand
+ ADP
T4 DNA kinasi
Do the 6 oligos assemble in one object?
A bidimenional array1D –
2D by
changing
sticky-ends
(Mao e Seeman, JACS 1999)
AFM of
the 2D array of parallelograms
Design and preparation of supramolecular constructs
Double-crossover structures (introduced in 1993): the design and realization
of
rigid
tiles
for
making
DNA mosaics.These
structures, inspired
from
meiosis
intermediates, are made
by
two
parallel
double helices
rigidly
joined
thanks
to
the exchange
of
strands. Some strands
belong
to
one
helix
at the beginning
and then
switch
helix.
There
are different
types
of
DX as
a function
of
the geometry
and topology
of
the strand
exchange.
Some of
the DX possible
on paper
are not
stable, though.By
equipin
DX with
sticky
ends, they
can be
assembled
into
1D or 2D polymeric
structures.
Similarly, 3 double
helices
can be
paired, making
2 reciprocal
exchanges
between
each
couple
of
helices. These
are named triple crossover (TX), and are bigger
than
DX. Other
possible
variations: DX+J, where
an
hairpin
sticks
out from
the center of
a DX and it
can be
made
perpendicular
to
the DX
plane.
A scheme
for
the TX
Periodic structures with controlled spacing can be made by assembling DX tiles through sticky ends. They can also extend out of the plane.Tiles are assembled in a non-computational manner to make simple and repetitive structures with a small number of different tiles. Alternatively, a larger variety of tiles can be assembled in an ‘algorithmic’
manner thus making computation while they
assemble. The computational rules are defined in the base sequence of the sticky ends..
This is an intrinsically more efficient method to do computation than the one proposed by Adleman: by proper coding of the sticky ends, only the assembly that follows the coded rules can take place (and not a myriad of products). Once tiles are formed and assembled, they are ligated. Ligation generates long oligonucleotides that contain the solution to the problem (that you read by sequencing the oligo).
(Li et al. JACS 2003)
Very thin arrays of constant thickness
These TX linear arrangements can be functionalized at desired locations and serve as spacing templates for proteins or nanoparticles. Such molecular wire is mechanically very rigid.
You can achieve strings of gold beads by using streptavidin-
coated gold nanoparticles.
Making tubes with DNA tiles
The group
of
Reif made
the assembly
of
TX tiles
with
a diedral angle different
from
180°: a curvature in the tile
plane
is
produced
ad eventally
a DNA tube
will
emerge. (Reif, PNAS 2004)
An alternative way to make tubes (N. Seeman)
Seeman
could
design and make
a large
hollow
tile
of
DNA made
by
a helix bundle. This
can be
assembled
along
its
axis through
stiky
ends
to
make
a tube. (Seeman, 2005 NanoLetters).
Ribbonds or 2D lattices of DNA with the 4-by-4 junction
The base tile
is
made
of
4 J1 junctions
joined
with
a central
strand
that
participates
in each
junction. 5
oligos
make
the trick.
Sticky
ends
are located
at the ends
of
the chains, so self-assembly
is
possible.
Giunzioni J1
(Yan
et
al. Science 2003)
nastri
Array 2D
Depending
on the way to
join the tiles, the assembly
results
in a ribbon/tubeor
in a flat
2D lattice: one
joining
strategy
sums
the small
deformation
of
the tile
and the assembly
plane
curves
in a tube (A), while
the alternative assembly
cancels
the deformations
out and makes
a large
flat
2D array
of
tiles
(B)
A
B
Proteins can be specificaly located on the arrays (you could make nano- circuits)
Biotins
are bound
at the center of
the oligonucleotide that
participates
in all
4 J1.
Streptavidin is
added
in solution
so i can locate of
the biotins.
The DNA ribbons
can be
metallized
to
make conductive
nanowires
with
a diameter
of
about
40 nm
and the length
of
a few
micrometers.
One
4-by-4 silver-coated
ribbon
laid
on top of
microelectrodes
in an
attempt
to
measure
its
electrical
properties.
(Shih
et
al. Nature 2004)
A self-assembling octahedron made with a 1.7 kb DNA moleculeA 1669 nt single-stranded
DNA molecule
self-assembles
together
with
5
40 n long oligonucleotides to
make
an
octahedron. The three- dimensional
object
can be
assembled
with
a piece
of
Dna that
can be
replicated
with
a DNA polymerase! It
could
host
a 14 nm
sphere; from
the opening of
its
faces, a 8 nm
sphere
could
enter.
DX
PX12
PX
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A recent examle
shows the degree of complexity that can be achieved:A molecular fabric made through the selfA molecular fabric made through the self--assembly of crossovers on a assembly of crossovers on a natural DNAnatural DNAPaul Rothemund, Nature 16 March 2006 (vol
440, pages 297-302 )
According to this approach, a DNA molecule can be sewn together to make any desired form through a certain number of synthetic oligonucleotides.
Results are astonishing: DNA origami can reproduce any desired shape in the nanoscale!
Rothemund, Nature, vol
440, pages 297-302
100 nm
100 nm
1 µm 1 µmscale 1:2x1014
The first DNA motor: Bernard Yurke
e Andrew Turberfield
(2000)A motor made
of
oligonucleotides that
reorganize
as
a response
to
the introduction
of
an
oligonucleotide in solution. It
is
the opening and closing
of
a molecular
tweezer. The movement
is
visualized
thanks
to
FRET.
The strategy
to
extract
a component
from
the structure
is
worth
noticing
and has
been
re-used
many
times
since. A protruding
single-stranded
tail
is
exploited
in order
to
nucleate a new, more stable
double
helix
that
then
strips
off one
oligo
from
the nanostructure
(recycling
the motor). This
takes
place
at room
temperature, without
the neeed
of
disassembling
anything
else.
Main
disadvantages
of
the most
common DNA nanodevices
1.
Production of
waste
DNA•
Degradation
of
the performance over
time
2.
Need
of
bi-macromolecular
events•
Concentration-dependent
performance
•
Performance depends
on bulky
molecules•
Opening and closing
signals
through
expensive
molecules
Design of
a triplex-based
DNA motor
Marco Brucale et
al.
Design / 2
•
the generated waste(salt) does not interfere with the functioning of the motor up to 1M or more.
Static
characterizations
CD
spectrum
at different
pH.
Absorbance
at 260nm
at different pH.
Electrophoretic
mobility
for a construct
with
or without
the TFO
Dynamic
characterizations
/ 1
Fluorescnece emission of A+B* alternating the pH between 5 and 9
•
The emission
intensity
depends
exclusively
on the separation
between donor
and acceptor
• No degradation
of
the performance with
the successive additions
E
Q
EQ
Dynamic
characterizations
/ 2
At high dilution
• Same performance!
Putting the device on a surface
Si
O
O
O SH
R1
SS(L)-Oligo
A
pH 9.0 buffer16h
Si
O
O
O SS(L)
glas
s
glas
s
Si
O
O
O SS(L)
Oligo
B*
pH 5.0 buffer16h gl
ass
MPTS
95% EtOH5% H2
O pH 4.516h
2h 150°C0.05 torr
MPTS gas phase
a)
b)
glas
s
Single molecule characterization by scanning confocal fluorescence microscopy on the surface
Dynamic Single-Molecular DNA structures
Single molecule fluorescence studies confirm that the structure can assume both conformations when immobilized on a surface.
Take-home message: with DNA you can design and make nanostructures with the desired shape and mechanical properties. They can be static or dynamic, can be made of DNA only or decorated with a large variety of other functional nano-objects.
Seeman e coll.: a molecular machine based on the B → Z transition.A DNA segment
of
particular
sequence
can
have
a B to
Z transition. If
this
is
located between
two
DX (which
carry
fluorophors)
then
their
motion
can be
studied. Disadvantages: it
is
difficult
to
cycle
the motor
back and forth.
A molecular machine based on a DNA quadruplex
[ W. Tan et al, 2002]
A DNA biped
that
walks
along
a sidewalk
Using
the same
strategy
that
Yurke
and turberfield
employed
to
strip off oligos
from
a structure, Ned
Seeman
implemented
a walker
that
can move
controllably
along
a track, by
sticking
and releasing
its
feet
from
posts. Each
motion
requires
the addition
of
an
oligonucleotide. The waste
can be
removed
by
proper
oligonucleotide functionalization.
(Sherman e Seeman, NanoLetters 2004)[animazione]
An autonomous DNA motor: it runs as long as there is fuel
An autonomous molecular machine: a DNAzyme cuts the RNA oligonucleotide that keeps a structure extended after it binds to it. After the cut, the fragments separated and the structure is ready to host another copy of the same RNA oligo, effectively ‘breathing’ as long as there are oligos that can bind and get cut.
[ C. Mao et al, 2004]
Interesting
videos:
DNA structural
nanotechHao
Yan
Paul Rothemund