1. Proteins are made up of amino acids and take on specific three-dimensional structures that dictate their function. Determining a protein's structure is important for understanding its role in biological processes. 2. There are several methods for determining and predicting protein structure, including X-ray crystallography, NMR, and computational methods like homology modeling or ab initio structure prediction. 3. Protein structure is hierarchical, ranging from secondary structure like alpha helices and beta sheets to the overall fold classified in databases like SCOP and CATH. Predicting secondary structure is easier than predicting a protein's full three-dimensional structure.

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Protein Structure, Structure
Classification and Prediction
Bioinformatics X3
January 2005
P. Johansson, D. Madsen
Dept.of Cell & Molecular Biology,
Uppsala University

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Overview
• Introduction to proteins, structure & classification
• Protein Folding
• Experimental techniques for structure determination
• Structure prediction

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4. 4
Proteins
• Proteins play a crucial role in virtually all biological processes
with a broad range of functions.
• The activity of an enzyme or the function of a protein is
governed by the three-dimensional structure

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20 amino acids - the building blocks

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The Amino Acids

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Hydrophilic or hydrophobic..?
• Virtually all soluble proteins feature a hydrophobic core
surrounded by a hydrophilic surface
• But, peptide backbone is inherently polar ?
• Solution ; neutralize potential H-donors & acceptors using
ordered secondary structure

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Secondary Structure: a-helix

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• 3.6 residues / turn
• Axial dipole moment
• Not Proline & Glycine
• Protein surfaces
Secondary Structure: a-helix

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Secondary Structure: b-sheets

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Secondary Structure: b-sheets
• Parallel or antiparallel
• Alternating side-chains
• No mixing
• Loops often have polar amino acids

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Structural classification
• Databases
– SCOP, ’Structural Classification of Proteins’,
manual classification
– CATH, ’Class Architecture Topology Homology’, based
on the SSAP algorithm
– FSSP, ’Family of Structurally Similar Proteins’, based
on the DALI algorithm
– PClass, ’Protein Classification’
based on the LOCK and 3Dsearch algorithms

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Structural classification, CATH
• Class, four types :
– Mainly a
– a / b structures
– Mainly b
– No secondary structure
• Arhitecture (fold)
• Topology (superfamily)
• Homology (family)

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Structural classification..

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Structural classification..
• Two types of algorithms
– Inter-Molecular, 3D, Rigid Body ; structural alignment in a
common coordinate system (hard) e.g. VAST, LOCK.. alg.
– Intra-Molecular, 2D, Internal Geometry ; structural
alignment using internal distances and angles e.g. DALI,
STRUCTURAL, SSAP.. alg.

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Structural classification, SSAP
• SSAP, ‘Sequential Structure Alignment Program’
Basic idea ; The similarity between residue i in molecule A
and residue k in molecule B is characterised in terms of their
structural surroundings
This similarity can be quantified into a score, Sik
Based on this similarity score and some specified gap penalty,
dynamic programming is used to find the optimal structural
alignment

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Structural classification, SSAP
The structural neighborhood of residue i in A compared
to residue k in B
i k

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Structural classification, SSAP..
Distance between residue i & j in molecule A ; dA
i,j
Similarity for two pairs of residues, i j in A & k l in B ;
,
,
b
d
d
a
s B
kl
A
ij
kl
ij


 a,b constants
Similarity between residue i in A and residue k in B ;


 
 


n
n
m
B
m
k
k
A
m
i
i
k
i
b
d
d
a
S
,
,
,
Idea ; Si,k is big if the distances from residue i in A to the 2n
nearest neighbours are similar to the corresponding distances
around k in B

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Structural classification, SSAP..
This works well for small structures and local structural
alignments - however, insertions and deletions cause problems
 unrelated distances
HSERAHVFIM..
GQ-VMAC-NW..
i=5
k=4
A :
B :
- The real algorithm uses Dynamic programming on two levels,
first to find which distances to compare  Sik, then to align the
structures using these scores

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Experimental techniques for structure
determination
• X-ray Crystallography
• Nuclear Magnetic Resonance
spectroscopy (NMR)
• Electron Microscopy/Diffraction
• Free electron lasers ?

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X-ray Crystallography

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X-ray Crystallography..
• From small molecules to viruses
• Information about the positions of
individual atoms
• Limited information about
dynamics
• Requires crystals

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NMR
• Limited to molecules up to ~50kDa
(good quality up to 30 kDa)
• Distances between pairs of
hydrogen atoms
• Lots of information about dynamics
• Requires soluble, non-aggregating
material
• Assignment problem

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Electron Microscopy/ Diffraction
• Low to medium resolution
• Limited information about
dynamics
• Can use very small crystals
(nm range)
• Can be used for very large
molecules and complexes

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Structure Prediction
GPSRYIV…
?

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Protein Folding
• Different sequence  Different
structure
• Free energy difference small due
to large entropy decrease,
DG = DH - TDS

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Structure Prediction
Why is structure prediction and especially ab
initio calculations hard..?
• Many degrees of freedom / residue
• Remote noncovalent interactions
• Nature does not go through all conformations
• Folding assisted by enzymes & chaperones

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Ab initio calculations used
for smaller problems ;
• Calculation of affinity
• Enzymatic pathways
Molecular dynamics

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Sequence Classification rev.
• Class : Secondary structure content
• Fold : Major structural similarity.
• Superfamily : Probable common
evolutionary origin.
• Family : Clear evolutionary relationship.

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• Search sequence data banks for homologs
• Search methods e.g. BLAST, PSIBLAST,
FASTA…
• Homologue in PDB..?
Structure Prediction
IVTY…PGGG HYW…QHG

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Multiple sequence / structure alignment
• Contains more information than a single sequence
for applications like homology modeling and
secondary structure prediction
• Gives location of conserved parts
and residues likely to be buried in
the protein core or exposed to solvent
Structure Prediction

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HFD fingerprint
Multiple alignment example

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• Statistical Analysis (old fashioned):
– For each amino acid type assign it’s ‘propensity’
to be in a helix, sheet, or coil.
• Limited accuracy ~55-60%.
• Random prediction ~38%.
MTLLALGINHKTAP...
CCEEEEEECCCCCC...
Secondary Structure Prediction

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• Each residue is classified as:
– Ha/Hb, strong helix / strand former.
– ha/hb, weak helix / strand former.
– I, indifferent.
– ba/bb, weak helix/strand breaker.
– Ba/Bb, strong helix / strand breaker.
The Chou & Fasman Method

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The Chou & Fasman Method..
• Score each residue:
– Ha/ha=1, Ia=0 or ½, Ba/ba=-1.
– Hb/hb=1, Ib=0 or ½, Bb/bb=-1.
• Helix nucleation:
– Score > 4 in a “window” of 6 residues.
• Strand nucleation:
– Score > 3 in a “window” of 5 residues.
• Propagate until score < 1 in a 4 residue “window”.

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GPSRYIVTLANGK
Helix:
Strand
-1 -1 0 0 -1 1 1 0 1 1 -1 -1 1
-1 -1 -1 .5 1 1 1 1 1 0 0 -1 -1
-2 0 1 2 3 3 1
No nucl.
-1.5 .5 2.5 4.5 5 4 3 1 -1
-2.5 -.5 1.5 … 3 1 -1
Nucleation
Propagate
GPSRYIVTLANGK
Result
The Chou & Fasman Method..

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• Neural networks (e.g. the PHD server):
– Input: a number of protein sequences +
secondary structure.
– Output: a trained network that predicts
secondary structure elements with ~70%
accuracy.
• Use many different methods and compare
(e.g. the JPred server)!
Modern methods

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Summary
• The function of a protein is governed by its structure
• Different sequence  Different structure
• PDB, protein data bank
• Secondary structure prediction is hard, tertiary
structure prediction is even harder
• Use homologs whenever possible or different methods
to assess quality

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