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Predicting Protein Structure:
Comparative Modeling
(homology modeling)
Predicting Protein Structure:
Comparative Modeling
(formerly, homology modeling)
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KILDIKGIDYWIAHKALCTEKLEQWLCEKE
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Homologous
Share
Similar
Sequence
Use as template
& model
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Structure prediction
• In an ideal world, we would be able to accurately predict protein structure
from the sequence only!
• Because of the myriad possible configurations of a protein chain – This goal
can’t reliably be achieved, yet.
• Knowledge based prediction vs. Simulation based on physical forces.
• Here we will only concern ourselves with knowledge-based methods, although
we might use simulation in order to optimize our models.
Can we predict protein structures ?
MNIFEMLRID
HLLTKSPSLN
DEAEKLFNQD
LDAVRRCALI
LQQKRWDEAA
TTFRTGTWDA
EGLRLKIYKD
AAKSELDKAI
VDAAVRGILR
NMVFQMGETG
VNLAKSRWYN
YKNL
TEGYYTIGIG
GRNCNGVITK
NAKLKPVYDS
VAGFTNSLRM
QTPNRAKRVI
• ab initio folding simulation: not yet ...
• Rosetta approach: neither ...
• Fold recognition (threading):
Often works, but ...
• ???
Approaches to predicting protein structures
obtain sequence (target)
fold assignment
comparative
modeling
ab initio
modeling
build, assess model
Homology Modelling of Proteins
• Definition:
Prediction of three dimensional structure of a target protein from the
amino acid sequence (primary structure) of a homologous (template)
protein for which an X-ray or NMR structure is available.
• Why a Model:
A Model is desirable when either X-ray crystallography or NMR
spectroscopy cannot determine the structure of a protein in time or at
all. The built model provides a wealth of information of how the
protein functions with information at residue property level. This
information can than be used for mutational studies or for drug design.
Homology modeling
= Comparative protein modeling
= Knowledge-based modeling
Idea:
Extrapolation of the structure for a new (target)
sequence from the known 3D-structures of related
family members (templates).
Homology models can be very smart!
Homology models have RMSDs less than 2Å more than 70% of the time.
Sequence similarity implies structural similarity?
100
.
identity
identity/similarity
Percentage sequence
80
Sequence identity implies
structural similarity
60
40
Don’t
know
20
0
region .....
(B.Rost, Columbia, NewYork)
0
50
100
150
200
Number of residues aligned
250
Step 1 in Homology Modeling Fold Identification
Aim: To find a template or templates
structures from protein data base
pairwise sequence alignment - finds high homology
sequences BLAST
http://www.ncbi.nlm.nih.gov/BLAST/
Improved Multiple sequence alignment methods
improves sensitivity - remote homologs
PSIBLAST, CLUSTAL
Comparative Modeling
Known Structures
(Templates)
Target
Sequence
•
Protein Data Bank PDB
http://www.pdb.org

Database of templates
•
•
•
Separate into single chains
Remove bad structures (models)
Create BLAST database
Template Selection
Alignment
Template - Target
Structure modeling
Homology
Model(s)
Structure Evaluation &
Assessment
Model Building from template
Core conserved regions
Protein Fold
Variable Loop regions
Side chains
Multiple templates
Calculate the framework from
average of all template structures
Generate one model for
each template and evaluate
I. Manual Modeling
[ http://www.expasy.org/spdbv/ ]
II. Template based fragment assembly
a) Build conserved core framework
• averaging core template backbone atoms
(weighted by local sequence similarity with the target sequence)
• Leave non-conserved regions (loops) for later ….
Dressing up the Core Model
Core Model-Rigid
Body Assembly
Add loops
Add Side chains
End Game in protein folding Molecular dynamics of all atoms in
explicit solvent
II. Template based fragment assembly
b) Loop modeling
• use the “spare part” algorithm to find
compatible fragments in a Loop-Database
• “ab-initio” rebuilding of loops (Monte Carlo,
molecular dynamics, genetic algorithms, etc.)
Loops result
from
substitutions,
insertions and
deletions in
the same
family
Loop Builders
Mini protein folding problem3 to 10 residues longer in
membrane proteins
Ab Initio methods generates various
random
conformations of
loops and score
Compare the loop
sequence string to
DB and get hits and
evaluate.
Some Homology
modeling methods
have less number of
loops to be added
because of
extensive multiple
sequence alignment
of profiles
Construction of loops might be done by:
Using database of loops which appear in known
structures. The loops could be catagorised by their length
or sequence
Ab initio methods - without any prior knowledge. This is
done by empirical scoring functions that check large number
of conformations and evaluates each of them.
II. Template based fragment assembly
c) Side Chain placement
Find the most probable side chain
conformation, using
• homologues structures
• back-bone dependent rotamer libraries
• energetic and packing criteria
II. Template based fragment assembly
d) Energy minimization
• modeling will produce unfavorable contacts and bonds
 idealization of local bond and angle geometry
• extensive energy minimization will move coordinates away
 keep it to a minimum
• SwissModel is using GROMOS 96 force field for a steepest descent
II. Template based fragment assembly
d) Energy minimization
Homology Modeling Programs
Modeller
(http://guitar.rockefeller.edu/modeller)
Swiss-Model
(http://www.expasy.ch/swissmod)
Whatif
(http://www.cmbi.kun.nl/whatif)
Swiss-Model
• Method:
Knowledge-based approach.
• Requirements:
At least one known 3D-structure of a related protein.
Good quality sequence alignements.
• Procedures:
Superposition of related 3D-structures.
Generation of a multiple a alignement.
Generation of a framework for the new sequence.
Rebuild lacking loops.
Complete and correct backbone.
Correct and rebuild side chains.
Verify model structure quality and check packing.
Refine structure by energy minimisation and molecular dynamics.
Model Confidence Factors
The Model B-factors are determined as follows:
• The number of template structures used for model building.
• The deviation of the model from the template structures.
• The Distance trap value used for framework building.
The Model B-factor is computed as:
85.0 * (1/ # selected template str.) * (Distance trap / 2.5)
and
99.9 for all atoms added during loop and side-chain building
Verifying the Model
• PROCHECK
• WHAT IF
• PROSA II
• VERIFY 3D, Profile3D
Errors in Models !!!
• Incorrect template selection
• Incorrect alignments
• Errors in positioning of sidechains and loops
General Structure Prediction Scheme
Any given protein sequence
Check sequence identity
with proteins with known structure
> 35%
< 35%
Homology
Modeling
Fold
Recognition
< 35%
ab initio
Folding
Structure selection
Structure refinement
Final Structure
Baker and Sali (2000)
Model Accuracy Evaluation
CASP
Community Wide Experiment on the Critical Assessment
of Techniques for Protein Structure Prediction
http://PredictionCenter.llnl.gov/casp5/
EVA
Evaluation of Automatic protein structure prediction
[ Burkhard Rost, Andrej Sali, http://maple.bioc.columbia.edu/eva/ ]
3D - Crunch
Very Large Scale Protein Modelling Project
http://www.expasy.org/swissmod/SM_LikelyPrecision.html
Several web pages for homology modeling
COMPOSER – felix.bioccam.ac.uksoft-base.html
MODELLER – guitar.rockefeller.edu/modeller/modeller.html
WHAT IF – www.sander.embl-heidelberg.de/whatif/
SWISS-MODEL – www.expasy.ch/SWISS-MODEL.html