Proteiinianalyysi 6

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Proteiinianalyysi 6

• Sekundaarirakenteen ennustaminen
• http//www.bioinfo.biocenter.helsinki.fi/downloads
  /teaching/spring2006/proteiinianalyysi

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Secondary structure

• Amino acid sequence gt secondary structure
• Conformational preferences of amino acids
• 13-17 residue window
• Correlations between positions gt neural networks
• Biophysical background
• http//www.chembio.uoguelph.ca/educmat/phy456/456l
  ec01.htm

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Appendix A
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DSSP algorithm to define secondary structure

• Dictionary of Protein Secondary Structure
  Pattern Recognition of Hydrogen-Bonded and
  Geometrical Features
• W. Kabsch C. Sander
• Biopolymers 22, 2577-2637 (1983)

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Hydrogen bonds
0.20e
-0.20e
0.42e
-0.42e
E q1 q2 1/r(ON) 1/r(CH) 1/r(CN)
1/r(OH) Ideal H-bond is co-linear, r(NO)2.9 A
and E-3.0 kcal/mol Cutoffs in DSSP allow 2.2 A
excess distance and 60º angle
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Elementary H-bond patterns

• n-turn(i) Hbond(i,in), n3,4,5
• Parallel bridge(i,j)
• Hbond(i-1,j) AND Hbond(j,i1) OR
• Hbond(j-1,i) AND Hbond(i,j1)
• Antiparallel bridge(i,j)
• Hbond(i,j) AND Hbond(j,i) OR
• Hbond(i-1,j1) AND Hbond(j-1,i1)

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N-turns
-N-C-C--N-C-C--N-C-C--N-C-C- H O H O H O
H O
3-turn
-N-C-C--N-C-C--N-C-C--N-C-C--N-C-C- H O H O
H O H O H O
4-turn
-N-C-C--N-C-C--N-C-C--N-C-C-N-C-C-N-C-C- H O
H O H O H O H O H O
5-turn
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Parallel bridge
-N-C-C--N-C-C--N-C-C--N-C-CN-C-C- H O H O
H O H O H O
H O H O H O H O H
O -N-C-C--N-C-C--N-C-C--N-C-CN-C-C-
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Antiparallel bridge
-N-C-C--N-C-C--N-C-C--N-C-C- H O H O H O
H O
O H O H O H O H
-C-C-N--C-C-N--C-C-N--C-C-N-
Antiparallel beta-sheet is significantly more
stable due to the well aligned H-bonds.
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Cooperative H-bond patterns

• 4-helix(i,i3) 4-turn(i-1) AND 4-turn(i)
• 3-helix(i,i2) 3-turn(i-1) AND 3-turn(i)
• 5-helix(i,i4) 5-turn(i-1) AND 5-turn(i)
• Longer helices are defined as overlaps of minimal
  helices

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Beta-ladders and beta-sheets

• Ladder set of one or more consecutive bridges
  of identical type
• Sheet set of one or more ladders connected by
  shared residues
• Bulge-linked ladder two ladders or bridges of
  the same type connected by at most one extra
  residue on one strand and at most four extra
  residues on the other strand

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3-state secondary structure

• Helix
• Strand
• Loop
• Quoted consistency of secondary structure state
  definition in structures between sequence-similar
  proteins is 70
• Richer descriptions possible
• E.g. phi-psi regions

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Amino acid preferences for different secondary
structure

• Alpha helix may be considered the default state
  for secondary structure. Although the potential
  energy is not as low as for beta sheet, H-bond
  formation is intra-strand, so there is an
  entropic advantage over beta sheet, where H-bonds
  must form from strand to strand, with strand
  segments that may be quite distant in the
  polypeptide sequence.
• The main criterion for alpha helix preference is
  that the amino acid side chain should cover and
  protect the backbone H-bonds in the core of the
  helix. Most amino acids do this with some key
  exceptions.
• alpha-helix preference
• Ala,Leu,Met,Phe,Glu,Gln,His,Lys,Arg

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• The extended structure leaves the maximum space
  free for the amino acid side chains as a result,
  those amino acids with large bulky side chains
  prefer to form beta sheet structures
• just plain largeTyr, Trp, (Phe, Met)
• bulky and awkward due to branched beta
  carbonIle, Val, Thr
• large S atom on beta carbonCys
• The remaining amino acids have side chains which
  disrupt secondary structure, and are known as
  secondary structure breakers
• side chain H is too small to protect backbone
  H-bondGly
• side chain linked to alpha N, has no N-H to
  H-bondrigid structure due to ring restricts to
  phi -60 Pro
• H-bonding side chains compete directly with
  backbone H-bonds Asp, Asn, Ser
• Clusters of breakers give rise to regions known
  as loops or turns which mark the boundaries of
  regular secondary structure, and serve to link up
  secondary structure segments.

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Secondary structure prediction

• GOR method
• Visual, expert assessment
• Neural networks
• Nearest neighbour assignment
• consensus filters

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GOR method

• State of central residue is influenced by
  adjacent positions in a window
• A...X.
• A...Q.
• AXL..
• Superseded by more accurate methods

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Structure parsing

• Multiple alignment
• Conservation gt core elements
• Gaps, Pro, Gly, polar stretch gt loops
• 3.5 periodicity gt amphiphilic helix
• 2 periodicity gt amphiphilic strand
• Row of hydrophobics gt buried strand

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What are neural networks?

• Parallel, distributed information processing
  structures which draw their ultimate inspiration
  from neurons in the brain
• Main class feed-forward network alias
  multi-layer perceptron
• Paradigm for tackling pattern classification and
  regression tasks

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Why (not) use neural networks?

• Efficient at secondary structure prediction
• Black boxes
• Can deal with non-linear combination of multiple
  factors
• Rule-based explanation can over-simplify and
  mislead

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• Neural networks are made of units that are often
  assumed to be simple in the sense that their
  state can be described by a single numbers, their
  "activation" values. Each unit generates an
  output signal based on its activation. Units are
  connected to each other very specifically, each
  connection having an individual "weight" (again
  described by a single number). Each unit sends
  its output value to all other units to which they
  have an outgoing connection. Through these
  connections, the output of one unit can influence
  the activations of other units. The unit
  receiving the connections calculates its
  activation by taking a weighted sum of the input
  signals (i.e. it multiplies each input signal
  with the weight that corresponds to that
  connection and adds these products). The output
  is determined by the activation function based on
  this activation (e.g. the unit generates output
  or "fires" if the activation is above a threshold
  value). Networks learn by changing the weights of
  the connections.

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Feed-forward architecture
Typical output 1.0 for all patterns
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Output of each node in the network, for a given
pattern p
Squashing function f(x) is typically a sigmoid
or logistic function
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A two-layer neural network capable of calculating
XOR. The numbers within the neurons represent
each neuron's explicit threshold (which can be
factored out so that all neurons have the same
threshold, usually 1). The numbers that annotate
arrows represent the weight of the inputs. This
net assumes that if the threshold is not reached,
zero (not -1) is output.
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A two-layer neural network capable of calculating
XOR. The numbers within the neurons represent
each neuron's explicit threshold (which can be
factored out so that all neurons have the same
threshold, usually 1). The numbers that annotate
arrows represent the weight of the inputs. This
net assumes that if the threshold is not reached,
zero (not -1) is output.
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Training a feed-forward net

• Supervised learning
• Training pattern and associated target training
  pair
• Input patterns in training set must have the same
  number of elements as the net has input nodes
• Every target must have the same number of
  elements as the net has output nodes

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Ability to generalise

• The number of training patterns versus the number
  of network weights
• Rule of thumb need at least 20 times as many
  patterns as network weights
• The number of hidden nodes
• Too few nodes impedes learning
• Too many nodes impedes generalisation
• The number of training iterations

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Number of training iterations
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Basic approach

• Each training pair is of the form
• Pattern LSADQISTVQASFDK
• Target H
• Three target classes
• DSSP classes Prediction class
• H, G helix
• E strand
• B, I, S, T, e, g, h coil
• Encoding
• Alanine 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0
• Helix 1 0 0

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Back-propagation algorithm

• Gradient descent
• wij wij n d E / d wij m (1)
• Partial derivative of error E with respect to
  weights
• E / d wij (si di) si (1-si) sj (2)
• Si signal emitted by hidden node
• di desired value of output
• N rate of training (typical value 0.03)
• m smoothing factor (typical value 0.2)
• Example
• signal sj sent from Hj to Oi 0.2 desired
  output 1
• d E / d wij (0.2-1) x 0.2 x 0.8 x 0.2
  -0.0256
• so wij will be increased according to (1)

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Typical numbers

• Training set
• Several hundred non-homologous protein chains
• Total number of residues number of training
  patterns
• Architecture
• Fully-connected 17(21)-5-3
• 357 input nodes
• 1,808 weights
• Prediction
• winner-takes-all

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Performance measures

• Q3
• Three-state residue prediction
• Correlation coefficient
• SOV
• Segment overlap
• Reliability index

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Improvements on basic approach

• Using evolutionary information
• Up 6 -points
• Balanced training
• Equal representation of H, E, L patterns
• Increase the amount of training data
• Up 4 -points training on 128 / 318 proteins
• Post-processing and filtering
• Use an ensemble of networks
• Jury of 10 nets up 2 -points

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PredictProtein server
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PSIPRED
PSI-Blast multiple alignment analysed by two
feed-forward neural networks
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Prediction of secondary structure by nearest
neighbor analysis

• Examples of two of the most accurate nearest
  neighbor prediction programs
• (1) NNSSP (accuracy to 73.5) program chosing the
  PSSP / NNSSP option. The output probabilities Pa
  and Pb give a normalized score by co0nverting the
  values of fa, fb and fcoils to a scale of 0-9.
• (2) Predator (accuracy 75) using the FSSP
  assignments of secondary structure to the
  training sequences. Predator does not provide a
  normalized score. Predator predictions are shown
  below NNSP prediction on each line. The input
  sequence was the a subunit of S. typhimurium
  tryptophan synthase, Swiss-prot ID TRPA_SALTY,
  accession P00929, which is in the training
  sequences since the 3D structure is known.

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10 20 30
40 50 PredSS aaaaaaaaaaaaa bbbbbb
aaaaaaaaaaaaaaaaaaaaa AA seq
MERYESLFAQLKERKEGAFVPFVTLGDPGIEQSLKIIDTLIEAGADALEL
Prob a 9999999999997421110000001000168889999
9999974578863 Prob b 000000000000000012777887
41000100000000000000001122 Predator
___HHHHHHHHHHHHHH_EEEEEE_______HHHHHHHHHHH________
60 70 80
90 100 PredSS aaaaaaaaaa
aaaaaaaaaaaaa bbba AA seq
GIPFSDPLADGPTIQNATLRAFAAGVTPAQCFEMLALIRQKHPTIPIGLL
Prob a 1111111110012456889988731105889999999
9852000111133 Prob b 232211011000121100000011
11000000000000000002335544 Predator
______________HHHHHHHHH______HHHHHHHHHHH______HHHH
110 120 130
140 150 PredSS aaaaaaa aaaaaaaaaaa
bbbbb aaaaaaa AA seq
MYANLVFNKGIDEFYAQCEKVGVDSVLVADVPVEESAPFRQAALRHNVAP
Prob a 5455445344789999998840010000011122223
4788998731111 Prob b 321122110000000000000111
68986322110100000000000123 Predator
HHHHH______HHHHHHHHH____EEEEEE________HHHHHHHH___E
160 170 180
190 200 PredSS bbb aaaaaaaaa
bbbb aaaaaaaaaaaaaaaa AA seq
IFICPPNADDDLLRQIASYGRGYTYLLSRAGVTGAENRAALPLNHLVAKL
Prob a 0000000015899999973111121223521112555
6654388899999 Prob b 898520000000000001101136
77531112211100112200000000 Predator
EEE_______HHHHHHHH_____EEEEE______HHHHH_____HHHHHH
210 220 230
240 250 PredSS aaa
aaaaaaaaa aaaaaaaaaaa aaa AA seq
KEYNAAPPLQGFGISAPDQVKAAIDAGAAGAISGSAIVKIIEQHINEPEK
Prob a 8863210011110111478999998745312222687
8888997542588 Prob b 000000001334332000000000
00000122111010000000000000 Predator
HHH_______________HHHHHHH___________HHHHHHHHH__HHH
260 PredSS
aaaaaaaaaaaaaaaaa AA seq MLAALKVFVQPMKAATRS
Prob a 989999998878898663 Prob b
000000000000000011 Predator HHHHHHHH__________
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Paracelsuksen haaste

• Paracelsus oli 1500-luvulla vaikuttanut alkemisti
• protein design -haaste suunnittele
  aminohapposekvenssi, jolla on vähintään 50
  identtisiä aminohappoja tunnetun proteiinin
  kanssa, mutta joka laskostuu toisenlaiseksi
  rakenteeksi.
• Ensimmäinen haasteen täyttänyt keinotekoinen
  sekvenssi, nimeltään Janus (Dalal et al. 1997,
  Nat. Struct. Biol. 4, 548-552), muuntaa
  B1-domeenin beta-rakenteesta (bbabb)
  alfa-helikaaliseksi rakenteeksi (aa).
• Janus on rakenteeltaan Rop-proteiinin kaltainen.
  Rop-monomeeri muodostaa kahden vastakkaissuuntaise
  n heliksin hiusneulan. Luonnossa Rop dimerisoituu
  ja muodostaa neljän heliksin kimpun.

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(a) B1-domeenin rakenne. Januksen sekvenssissä
säilytetyt aminohapot on merkitty punaisella. (b)
ROP-dimeerin rakenne. Januksen sekvenssissä
esiintyvät aminohapot on merkitty sinisellä.
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(a) Laske B1-domeenin, Januksen ja Ropin
parittaiset sekvenssi-identtisyydet

• B1-Janus 27/56
• B1-Rop 3/56
• Janus-Rop 23/56

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1 2 3 4
5 . 0 . 0 . 0 . 0
. 0 . CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHH
HHTTCCEEEEECCCEEEEEECC MTYKLILNGKTLKGETITEAVDAAT
AEKVFKQYANDNGVDGEWTYDDATKTFTVTE B1-domeeni

MTKKAILALNTAKFLRTQAAVLAAKLEKLGAQEANDNAVDLEDTADDL
YKTLLVLA Janus
GTKQEKTALNMARFIRSQTLTLLEKLNEL
DADEQADICESLHDHADELYRSCLARF Rop-monomeeri CCHHHHHH
HHHHHHHHHHHHHHHHHHHHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
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(b) Merkitse linjaukseen identtisten
aminohappojen lisäksi substituutiot, joiden
pistemäärä on positiviinen BLOSUM62-matriisissa.

• Ei yhtään B1n ja Januksen välillä.
• Kahdeksan Januksen ja Ropin välillä.

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(c) Esiintyykö B1-perheessä tai Rop-perheessä
luonnostaan Janukseen valittuja mutaatioita?

• B1/Janus-mutaatioista mikään ei esiinny
  B1-perheessä.
• Janus/Rop-mutaatioista 7 esiintyy muissa
  Rop-perheen jäsenissä. 5 näistä mutaatioista on
  yhteisiä B1-sekvenssin kanssa.
• B1stä on muutettu ytimen aminohappoja, kun taas
  Ropin ydin on säilytetty Januksessa.

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(d) Miten Januksen beta-tendenssiä on heikennetty
ja alfa-tendenssiä vahvistettu?
52
Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSQTLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
Valine and isoleucine side chains
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Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSTQLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
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Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSQTLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
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Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSQTLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
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Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSQTLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
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Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSQTLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
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Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSQTLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
CA
N
CO
Glycine side chain
Proline
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Sekvenssilinjaus
(c) B1-domeenin, Januksen ja ROP-monomeerin
sekvenssilinjaus. Identtiset aminohapot on
merkitty pystyviivalla. 1 2
3 4 5 . 0 . 0
. 0 . 0 . 0 .
CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCE
EEEEECC B1-domeenin sekundaarirak.
MTYKLILNGKTLKGETITEAVDAATAEKVFKQYANDNGVDGEWTYDDAT
KTFTVTE B1-domeeni
MTKKAILALNTAKFLRTQAAVLAAKL
EKLGAQEANDNAVDLEDTADDLYKTLLVLA Janus

GTKQEKTALNMARFIRSQTLTLLEKLNELDADEQADICESLHDHADELY
RSCLARF Rop (monomeeri) CCHHHHHHHHHHHHHHHHHHHHHHHH
HHTTCHHHHHHHHHHHHHHHHHHHHHHHHH
NH2
O
Asparagine side chain
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(d) Miten Januksen beta-tendenssiä on heikennetty
ja alfa-tendenssiä vahvistettu?

• Kuvaan on merkitty strong beta former BIMV, beta
  former bCTY, strong alpha former AAEL, alpha
  former aHQ, beta breaker iKNPS, strong alpha
  breaker IG. Januksen sekvenssissä on suosittu
  heliksin muodostajia ja beta-rikkojia.

CEEEEECCCSSCEEEEECCCSCHHHHHHHHHHHHHHTTCCEEEEECCCEE
EEEECC B1-domeenin MTYKLILNGKTLKGETITEAVDAATAEKVFK
QYANDNGVDGEWTYDDATKTFTVTE B1-domeeni BbbiABAiIibAi
IAb_bAAB_AAbAAi__i_bA__iIB_IA_bb__Ab_b_bBbA

MTKKAILALNTAKFLRTQAAVLAAKLEKLGAQEANDNAVDLEDTADDL
YKTLLVLA Janus BbiiABAAAibAi_A_b_AABAAAiAAiAIA_AAi
_iAB_AA_bA__AbibAABAA
GTKQEKTALNMARFIRSQTLTL
LEKLNELDADEQADICESLHDHADELYRSCLARF
Rop-monomeeri Ib__A_bAA_BA__B___bAbAAA_A_AA_A_A_A_
BbA_A___A_AAb__bAA__ CCHHHHHHHHHHHHHHHHHHHHHHHHHHT
TCHHHHHHHHHHHHHHHHHHHHHHHHH
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(e) Pistemutaation D30G on havaittu lisäävän
luonnollisen Ropin termodynaamista
stabiliisuutta. Miten muuten tämä mutaatio
edesauttaa Janus-proteiinin laskostumista
Chou-Fasman-luokittelun perusteella?

• Glysiini tuhoaa heliksejä. Januksessa tälle
  kohdalle halutaan tiukka käännös.