Predicting Protein Structure: Comparative Modeling (homology modeling)

Predicting Protein Structure:Predicting Protein Structure:Comparative ModelingComparative Modeling(homology modeling)(homology modeling)

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Predicting Protein Structure:Predicting Protein Structure:Comparative ModelingComparative Modeling

(formerly, homology modeling)(formerly, homology modeling)

Use as template & model

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Similar Sequence

Homologous

• 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.

Structure prediction

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Can we predict protein

structures ?

• ab initio folding simulation: not yet ...• Rosetta approach: neither ...• Fold recognition (threading):

Often works, but ...• ???

obtain sequence (target)

fold assignment

comparativemodeling

ab initiomodeling

build, assess model

Approaches to predicting protein structures

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 have RMSDs less than 2Å more than 70% of the time.

Homology models can be very smart!

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identity

Number of residues aligned

Perc

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(B.Rost, Columbia, NewYork)

Sequence identity implies structural similarity

Don’t know region .....

Sequence similarity implies structural similarity?

Step 1 in Homology Modeling - Fold Identification

Aim: To find a template or templates structures from protein data base

Improved Multiple sequence alignment methods improves sensitivity - remote homologs

PSIBLAST, CLUSTAL

pairwise sequence alignment - finds high homology sequences BLAST

http://www.ncbi.nlm.nih.gov/BLAST/

Comparative ModelingKnown Structures

(Templates)

Target Sequence Template Selection

Alignment Template - Target

Structure modeling

Structure Evaluation &Assessment

HomologyModel(s)

• Protein Data Bank PDB http://www.pdb.org

Database of templates

• Separate into single chains• Remove bad structures

(models)• Create BLAST database

Model Building from template

Multiple templates

Protein Fold

Core conserved regions

Variable Loop regions

Side chains

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/ ]

• averaging core template backbone atoms

(weighted by local sequence similarity with the target sequence)

• Leave non-conserved regions (loops) for later ….

a) Build conserved core framework

II. Template based fragment assembly

Dressing up the Core ModelCore Model-Rigid Body Assembly

Add Side chains

Add loops

End Game in protein folding - Molecular dynamics of all atoms in

explicit solvent

• 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.)

b) Loop modeling

II. Template based fragment assembly

Loop BuildersMini protein folding problem-

3 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

Loops result from

substitutions, insertions and deletions in

the same family

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.

Construction of loops might be done by:

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

• 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

d) Energy minimization

II. Template based fragment assembly

d) Energy minimization

II. Template based fragment assembly

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

Structure selection

Check sequence identity with proteins with known structure

HomologyModeling

> 35%

Fold Recognition

ab initioFolding

< 35%< 35%

Structure refinement

Final Structure

Structure selection

Baker and Sali (2000)

EVA

Evaluation of Automatic protein structure prediction

[ Burkhard Rost, Andrej Sali, http://maple.bioc.columbia.edu/eva/ ]

CASPCommunity Wide Experiment on the Critical Assessment of Techniques for Protein Structure Predictionhttp://PredictionCenter.llnl.gov/casp5/

3D - Crunch

Very Large Scale Protein Modelling Project

http://www.expasy.org/swissmod/SM_LikelyPrecision.html

Model Accuracy Evaluation

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