Protein structure introduction

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Protein structure introduction Bioinformatics
genes, proteins and computers Orengo, Jones and
Thornton (2003).
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Secondary structure elements
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Tertiary structure protein fold
complete 3-dimensional structure
why is it interesting? isnt the sequence enough?

• the structure is more conserved!

• detection of distant evolutionary
• relationships

• a key to understand protein
• function

• Structure-based drug design

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Fold classification
classification clustering proteins into
structural families
motivation?

• profound analysis of evolutionary mechanisms

• constraints on secondary structure packing?

• classification at domain level

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CATH Protein Structure Classification

• hierarchical classification of protein domain
  structures in the Brookhaven Protein Databank
  (PDB).
• domains are clustered at four major levels
• Class
• Architecture
• Topology
• Homologous superfamily
• Sequence family

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CATH hierarchical classification

• Classsecondary structure content mainly
  a,mainly b,a b, low 2nd structure content.
• Architecturegross orientation of secondary
  structures, independent of connectivity.
• Topology ( fold)clusters structures according
  to their topological connections.

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CATH architectures
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CATH architectures (cont.)
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CATH hierarchical classification

• Homologous superfamily
• homologous domains identified by sequence
  similarity, and structure similarity
• Sequence family
• domains clustered in the same sequence
  families, with sequence identitygt35

• other classification schemes SCOP, FSSP
• partial disagreement between them.

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Growing demand for protein structures!

• PDB contains 20,868
• structures
• X-Ray and NMR have
• limitations.

WE NEED FASTER METHODS!
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Protein Structure Prediction

• Limited to very short peptides!

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Can known structures assist prediction?
the number of possible folds seems to be limited!

• CATH inspection more then 36,000 domains, but...
• only 800 topology groups

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Template-based prediction (fold recognition)
II) Comparative modeling (homology modeling) -
alignment with homologous sequence of known
structure. - high sequence identity areas
similar structure - variable areas must be built

• cant be used if no sequence similarity found!

• III) Threading
• - alignment with structure sequences in fold
  library
• - sophisticated scoring function finds most
  similar fold
• - Threading aligns target sequence onto
  template structure

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What are the baselines for protein fold
recognition? McGuffin, Bryson and Jones (2001)

• Goals
• what constitutes a baseline level of success for
  protein
• fold recognition methods, above random guesswork?

2. can simple methods that make use of 2nd
structure information assign folds more
reliably?

• how valuable might these methods be in the rapid
• construction of a useful hierarchical
  classification?

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The methods evaluated (ordered by complexity and
runtime)

• shorten 2nd structure stringsCCCHHHHCCCEEECCHHHC
  CC ? HCECH.
• pairwise alignment
• scoring function also considers length of elements

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A representative set of protein domains

• a set of 1087 domains representing different
• Sequence Families was selected from CATH.

• generate an informative file for each domain

1. gt1atx00 2. GAAaLbKSDGPNTRGNSMSGTIWVFGcPSGWNNbE
GRAIIGYacKQ 3.   EEE TTS S  TTSSEEEEEESS   TT
EEE  SSSSSEEEE 4. CEEEEEHHECEEEECCCECEEEECCCEECCE
ECEEECCEECEEEEC
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First evaluation true positive percentage
compare true positive percentage, at a fixed 3
false positive.
run each method on all possible pairs from the
1087 set (a,b) (a,c) (a,d) ... (g,d) (g,e)
... (k,f) ... (r,s) .... 590,000 pairs
STOP! 3 false positives reached. true positive
for this method 2
1
2
2
1
3
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We need lower,upper controls to compare with
lower control intelligent guesswork 1. randomly
assign CATH topology codes according to
frequency 2. calculate true positive, false
positive percentage
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Optimisation of similarity scoring methods
Class pre-filter
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• partial agreement between classification schemes
• FSSP compared with SCOP 61.1, FSSP compared
  with CATH 46.7

• most accurate is method number 5 Alignment of
  secondary structure
• elements without additional scoring, with
  27.18 true positive.

• accuracy ordering of methods doesnt correspond
  to their relative complexity

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Second evaluation CASP-like sensitivity
similarly to CASP we measure the sensitivity of
each method what is the probability of a method
correctly assigning a fold?
lower control a random proportional fold
assignment
upper control FSSP was used as a scoring method
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Sensitivity results

• method 5 wins again 31.8 sensitivity.
• other 2nd structure based methods with small gap.

• sensitivity order of the methods true positive
  percentage order.

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Similarity trees - can we construct
classification?
Best methods similarity scores for all pairs
were clustered into a tree.

• globin-like ltgt
• casein kinase

b. immunoglobulin-like ltgt thrombin
subunit H
whole tree generally disordered
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Conclusions

• Baseline level to be exceeded by fold
  recognition methods
• 27 true positive assignments allowing 3
  false positive
• sensitivity level of 32.

• methods which make use of 2nd structure
  information
• seem more accurate and sensitive than those who
  dont.

• simple 2nd structure alignments alone can not
  construct
• reliable classification hierarchy.

• the agreement between FSSP, SCOP and CATH
• classification schemes is surprisingly low.