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3D Structures of Biological Macromolecules Part
4 Protein Structure Prediction - I
Jürgen Sühnel jsuehnel_at_imb-jena.de
Institute of Molecular Biotechnology, Jena Centre
for Bioinformatics Jena / Germany
Supplementary Material http//www.imb-jena.de/www
_bioc/3D/
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Protein Structure
D. W. Mount Bioinformatics, Cold Spring Harbor
Laboratory Press, 2001.
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Protein Structure
Sequence
Protein-protein or protein-nucleic acid complexes
Secondary structure elements
Domains/Folds
Chains
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Protein Structure Domains
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Protein Structure Domains
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Protein Structure Domains
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Protein Structure Domains
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Protein Structure Prediction
http//speedy.embl-heidelberg.de/gtsp/flowchart2.h
tml
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A Good Protein Structure

• Minimizes disallowed torsion angles
• Maximizes number of hydrogen bonds
• Minimizes interstitial cavities or spaces
• Minimizes number of bad contacts
• Minimizes number of buried charges

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Protein Structure Prediction CAFASP Contest
http//www.cs.bgu.ac.il/dfischer/CAFASP3/
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Protein Structure Prediction CASP Contest
http//predictioncenter.llnl.gov/
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• Secondary structure
• 3D structure
• Modeling by homology (Comparative modeling)
• Fold recognition (Threading)
• Ab initio prediction
• Rule-based approaches
• Lattice models
• Simulating the time dependence of folding
• Refinement
• Exploring the effect of single amino acid
  substitutions
• Ligand effects on protein structure and dynamics
  (induced fit)

Protein Structure Prediction
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Lysozyme
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Lysozyme 5lyz
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Lysozyme 5lyz Information from Jena Image
Library Atlas Page
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PROMOTIF Secondary Structure Analysis 5lyz
. .
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Protein Backbone Torsion Angles
D. W. Mount Bioinformatics, Cold Spring Harbor
Laboratory Press, 2001.
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PROMOTIF Secondary Structure Analysis 5lyz
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PROMOTIF Secondary Structure Analysis 5lyz
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PROMOTIF Secondary Structure Analysis 5lyz
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Atom Names and Torsion Angles in Amino Acid
Sidechains (Lys)
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Chou-Fasman Secondary Structure Prediction
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Amino Acid Propensities
From a database of experimental 3D structures,
calculate the propensity for a given amino acid
to adopt a certain type of secondary structure

• Example
• N(Ala)2,000 N(tot)20,000 N(Ala, helix)500
  N(helix)4,000.
• P(Ala,helix) N(Ala,helix)/N(helix) /
  N(Ala)/N(tot)
• P(Ala,helix) 500/4,000/2,000/20,000 1.25
• Used in Chou-Fasman algorithm

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Chou-Fasman Secondary Structure Prediction

• Assign all of the residues in the peptide the
  appropriate set of parameters.
• Scan through the peptide and identify regions
  where 4 out of 6 contiguous residues have
  P(a-helix) gt 100.
• That region is declared an alpha-helix. Extend
  the helix in both directions until a set of four
  contiguous
• residues that have an average P(a-helix) lt 100
  is reached. That is declared the end of the
  helix.
• If the segment defined by this procedure is
  longer than 5 residues and the average
• P(a-helix) gt P(b-sheet) for that segment, the
  segment can be assigned as a helix.
• Repeat this procedure to locate all of the
  helical regions in the sequence.
• Scan through the peptide and identify a region
  where 3 out of 5 of the residues have a value of
• P(b-sheet) gt 100. That region is declared as a
  beta-sheet. Extend the sheet in both directions
• until a set of four contiguous residues that
  have an average P(b-sheet) lt 100 is reached.
• That is declared the end of the beta-sheet. Any
  segment of the region located by this procedure
• is assigned as a beta-sheet if the average
  P(b-sheet) gt 105 and the average P(b-sheet) gt
  P(a-helix)
• for that region.
• Any region containing overlapping alpha-helical
  and beta-sheet assignments are taken to be
  helical if the
• average P(a-helix) gt P(b-sheet) for that region.
  It is a beta sheet if the average
• P(b-sheet) gt P(a-helix) for that region.
• To identify a bend at residue number j, calculate
  the following value
• p(t) f(j)f(j1)f(j2)f(j3)

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Lysozyme 5lyz Chou-Fasman Secondary Structure
Prediction
http//fasta.bioch.virginia.edu/fasta_www/chofas.h
tm
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Lysozyme 5lyz Chou-Fasman Secondary Structure
Prediction
GRCE (0.570.980.701.39) 0.91 RCEL
(0.980.701.391.41) 1.12 CELA
(0.701.391.411.42) 1.23 ELAA
(1.391.411.421.42) 1.41
http//fasta.bioch.virginia.edu/fasta_www/chofas.h
tm
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Lysozyme 5lyz PhD/PROF Structure Prediction
PROF_sec PROF predicted secondary structure
Hhelix, Eextended (sheet), blankother
(loop) PROF PROF Profile network prediction
Heidelberg Rel_sec reliability index for
PROF_sec prediction (0low to 9high)
SUB_sec subset of the PROFsec prediction, for
all residues with an expected average accuracy gt
82 (tables in header) NOTE for this subset the
following symbols are used L is loop (for
which above ' ' is used) . means that no
prediction is made for this residue, as the
reliability is Rel lt 5 O3_acc observed
relative solvent accessibility (acc) in 3 states
b 0-9, i 9-36, e 36-100. P3_acc PROF
predicted relative solvent accessibility (acc) in
3 states b 0-9, i 9-36, e
36-100. Rel_acc reliability index for PROFacc
prediction (0low to 9high) SUB_acc subset of
the PROFacc prediction, for all residues with an
expected average correlation gt 0.69 (tables in
header) NOTE for this subset the following
symbols are used I is intermediate (for which
above ' ' is used) . means that no prediction
is made for this residue, as the reliability is
Rel lt 4
http//cubic.bioc.columbia.edu/predictprotein/subm
it_def.htmltop
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Lysozyme 5lyz PhD/PROF Structure Prediction,
BLAST
http//cubic.bioc.columbia.edu/predictprotein/subm
it_def.htmltop
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Lysozyme 5lyz PhD/PROF Structure Prediction,
BLAST
http//cubic.bioc.columbia.edu/predictprotein/subm
it_def.htmltop
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Lysozyme 5lyz PhD/PROF Structure Prediction

• Perform BLAST search to find local alignments
• Remove alignments that are too close
• Perform multiple alignments of sequences
• Construct a profile (PSSM) of amino-acid
  frequencies at each residue
• Use this profile as input to the neural network
• A second network performs smoothing
• The third level computes jury decision of several
  different instantiations of the first two levels.

http//cubic.bioc.columbia.edu/predictprotein/subm
it_def.htmltop
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Lysozyme 5lyz PsiPred Structure Prediction
http//bioinf.cs.ucl.ac.uk/psipred/psiform.html
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PsiPred
PSIPRED is a  simple and reliable secondary
structure prediction method, incorporating two
feed-forward neural networks which perform an
analysis on output obtained from PSI-BLAST
(Position Specific Iterated - BLAST). Version
2.0 of PSIPRED includes a new algorithm which
averages the output from up to 4 separate neural
networks in the prediction process to further
increase prediction accuracy. Using a very
stringent cross validation method to evaluate the
method's performance, PSIPRED 2.0 is capable of
achieving an average Q3 score of nearly 78.
Predictions produced by PSIPRED were also
submitted to the CASP4 server and assessed
during the CASP4 meeting, which took place in
December 2000 at Asilomar. PSIPRED 2.0 achieved
an average Q3 score of 80.6 across all 40
submitted target domains with no obvious
sequence similarity to structures present in PDB,
which placed PSIPRED in first place out of 20
evaluated methods (an earlier version of PSIPRED
was also ranked first in CASP3 held in 1998).
http//bioinf.cs.ucl.ac.uk/psipred/psiform.html
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PSI-BLAST
Position specific iterative BLAST (PSI-BLAST)
refers to a feature of BLAST 2.0 in which a
profile (or position specific scoring matrix,
PSSM) is constructed (automatically) from a
multiple alignment of the highest scoring hits in
an initial BLAST search. The PSSM is generated
by calculating position-specific scores for each
position in the alignment. Highly conserved
positions receive high scores and weakly
conserved positions receive scores near zero.
The profile is used to perform a second (etc.)
BLAST search and the results of each "iteration"
are used to refine the profile. This iterative
searching strategy results in increased
sensitivity.
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Comparing Secondary Structure Prediction Results
PsiPred
Chou-Fasman
Phd/PROF
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Comparing Secondary Structure Prediction Results
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Protein Secondary Structure Prediction - Summary

• 1st Generation - 1970s
• Chou Fasman, Q3 50-55
• 2nd Generation -1980s
• Qian Sejnowski, Q3 60-65
• 3rd Generation - 1990s
• PHD, PSI-PRED, Q3 70-80
• Features of the new methods
• Taking into account evolutionary information
• Neural networks
• Failures
• Nonlocal sequence interactions
• Wrong prediction at the ends of H/E

Q3 Percentage of correctly assigned amino acids
in a test set
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Protein Structure Prediction
http//speedy.embl-heidelberg.de/gtsp/flowchart2.h
tml
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Modeling by Homology (Comparative Modeling)
http//salilab.org/modeller/
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Modeling by Homology (Comparative Modeling)
http//swissmodel.expasy.org/
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Modeling by Homology (Comparative Modeling)

• Comparative modeling predicts the
  three-dimensional structure of a given
• protein sequence (target) based primarily on its
  alignment to one or more proteins
• of known structure (templates).
• The prediction process consists of
• fold assignment,
• target  template alignment,
• model building, and
• model evaluation and refinement.
• The number of protein sequences that can be
  modeled and the accuracy of
• the predictions are increasing steadily because
  of the growth in the number of
• known protein structures and because of the
  improvements in the modeling
• software.
• Further advances are necessary in recognizing
  weak sequence  structure
• similarities, aligning sequences with structures,
  modeling of rigid body shifts,
• distortions, loops and side chains, as well as
  detecting errors in a model.

http//salilab.org/modeller/
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Fold Recognition (Threading)
Methods of protein fold recognition attempt to
detect similarities between protein 3D structure
that are not accompanied by any significant
sequence similarity. The unifying theme of
these appraoches is to try and find folds that
are compatible with a particular sequence.
Unlike sequence-only comparison, these methods
take advantage of the extra information made
available by 3D structure information. Rather
than predicting how a sequence will fold, they
predict how well a fold will fit a sequence.
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Fold Recognition (Threading) Why ?

• Secondary structure is more conserved than
  primary structure
• Tertiary structure is more conserved than
  secondary structure
• Therefore very remote relationships can be better
  detected through 2o or 3o structural homology
  instead of sequence homology

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Fold Recognition (Threading)
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Fold Recognition (Threading) 2 Kinds

• 2D Threading or Prediction Based Methods (PBM)
• Predict secondary structure (SS) or ASA of query
• Evaluate on basis of SS and/or ASA matches
• 3D Threading or Distance Based Methods (DBM)
• Create a 3D model of the structure
• Evaluate using a distance-based hydrophobicity
  or pseudo-thermodynamic potential

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Fold Recognition

• Database of 3D structures and sequences
• Protein Data Bank (or non-redundant subset)
• Query sequence
• Sequence lt 25 identity to known structures
• Alignment protocol
• Dynamic programming
• Evaluation protocol
• Distance-based potential or secondary structure
• Ranking protocol

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Fold Recognition
http//www.sbg.bio.ic.ac.uk/3dpssm/index2.html
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Ab Initio Prediction

• Predicting the 3D structure without any prior
  knowledge
• Used when homology modelling or threading have
  failed (no homologues are evident)
• Equivalent to solving the Protein Folding
  Problem
• Still a research problem

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Ab Initio Prediction
http//rosettadesign.med.unc.edu/
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Ab Initio Prediction
http//rosettadesign.med.unc.edu/
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Ab Initio Prediction Lysozyme (5lyz)
http//rosettadesign.med.unc.edu/
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Combining Prediction Procedures
http//robetta.bakerlab.org/