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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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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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