Homology Modeling

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Homology Modeling
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Homology Modeling

• Presentation
• Fold recognition
• Model building
• Loop building
• Sidechain modeling
• Refinement
• Testing methods the CASP experiment

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Homology Modeling

• Presentation
• Fold recognition
• Model building
• Loop building
• Sidechain modeling
• Refinement
• Testing methods the CASP experiment

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Why do we need homology modeling ?
To be compared with
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Structural Genomics project

• Aim to solve the structure of all proteins this
  is too much work experimentally!
• Solve enough structures so that the remaining
  structures can be inferred from those
  experimental structures
• The number of experimental structures needed
  depend on our abilities to generate a model.

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Structural Genomics
Proteins with known structures
Unknown proteins
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Homology Modeling why it works
High sequence identity
High structure similarity
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Homology Modeling How it works

• Find template
• Align target sequence
• with template
• Generate model
• - add loops
• - add sidechains
• Refine model

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Homology Modeling

• Presentation
• Fold recognition
• Model building
• Loop building
• Sidechain modeling
• Refinement
• Testing methods the CASP experiment

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Fold Recognition
Homology modeling refers to the easy case when
the template structure can be identified using
BLAST alone.
What to do when BLAST fails to identify a
template?

• Use more sophisticated sequence methods
• Profile-based BLAST PSIBLAST
• Hidden Markov Models (HMM)
• Use secondary structure prediction to guide the
  selection of a template,
• or to validate a template
• Use threading programs sequence-structure
  alignments
• Use all of these methods! Meta-servers
  http//bioinfo.pl/Meta

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Fold Recognition
Blast for PDB search
Full homology modeling packages
Profile based approach
HMM
Structure-derived profiles
Fold recognition and Secondary structure
prediction
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Homology Modeling

• Presentation
• Fold recognition
• Model building
• Loop building
• Sidechain modeling
• Refinement
• Testing methods the CASP experiment

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Very short loops Analytic Approach
Wedemeyer, Scheraga J. Comput. Chem. 20,
819-844 (1999)
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Medium loops A database approach
Scan database and search protein fragments with
correct number of residues and correct end-to-end
distances
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Medium loops A database approach
cRMS (?)
Method breaks down for loops larger than 9
Loop length
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1) Clustering Protein Fragments to Extract a
Small Set of Representatives (a Library)
Long loops A fragment-based approach
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Generating Loops
Fragment library
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Generating Loops
Fragment library
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Generating Loops
Fragment library
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Generating Loops
Fragment library
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Generating Loops
Fragment library
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Long loops A fragment-based approach
Test cases 20 loops for each loop length
Methods database search, and fragment
building, with fragment libraries of size L
ltcRMS (?)
Loop length
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Loop building Other methods
Heuristic sampling (Monte Carlo, simulated
annealing) Inverse kinematics Relaxation
techniques Systematic sampling
http//www.cs.ucdavis.edu/koehl/BioEbook/loop_bui
lding.html
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Homology Modeling

• Presentation
• Fold recognition
• Model building
• Loop building
• Sidechain modeling
• Refinement
• Testing methods the CASP experiment

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Self-Consistent Mean-Field Sampling
P(J,2)
P(J,1)
P(J,3)
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Self-Consistent Mean-Field Sampling
P(i,2)
P(i,1)P(i,2)P(i,3)1
P(i,1)
P(i,3)
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Self-Consistent Mean-Field Sampling
Multicopy Protein
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Self-Consistent Mean-Field Sampling
Multicopy Protein
Mean-Field Energy
E(i,k) U(i,k) U(i,k,Backbone)
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Self-Consistent Mean-Field Sampling
Multicopy Protein
Mean-Field Energy
E(i,k) U(i,k) U(i,k,Backbone)
Update Cycle
(Koehl and Delarue, J. Mol. Biol., 239249-275
(1994))
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Self-Consistent Mean-Field Sampling
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Dead End Elimination (DEE) Theorem

• There is a global minimum energy conformation
  (GMEC) for which there is a unique
• rotamer for each residue
• The energy of the system must be pairwise.

Each residue i has a set of possible rotamers.
The notation ir means residue i has the
conformation described by rotamer r.
The energy of any conformation C of the protein
is given by
Note that
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Dead End Elimination (DEE) Theorem
Consider two rotamers, ir and it, at residue i
and the set of all other rotamer conformations
S at all residues excluding i. If the pairwise
energy between ir and js is higher than the
pairwise energy between it and js, for all js in
S, then ir cannot exist in the GMEC and is
eliminated. Mathematically
If
then
ir does not belong to the GMEC
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Dead End Elimination (DEE) Theorem
This is impractical as it requires S. It can be
simplified to
If
then
ir does not belong to the GMEC
Iteratively eliminate high energy rotamers
proved to converge to GMEC
Desmet, J, De Maeyer, M, Hazes, B, Lasters, I.
Nature, 356539-542 (1992)
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Other methods for side-chain modeling

• Heuristics (Monte Carlo, Simulated Annealing)
• SCWRL (Dunbrack)
• Pruning techniques
• Mean field methods

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Loop building Sidechain Modeling generalized
SCMF
Template
Add multi-copies of candidate loops
Add multi-copies of candidate side-chains
Final model
Koehl and Delarue. Nature Struct. Bio. 2, 163-170
(1995)
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Homology Modeling

• Presentation
• Fold recognition
• Model building
• Loop building
• Sidechain modeling
• Refinement
• Testing methods the CASP experiment

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Refinement ?
CASP5 assessors, homology modeling
category We are forced to draw the
disappointing conclusion that, similarly to what
observed in previous editions of the experiment,
no model resulted to be closer to the target
structure than the template to any significant
extent.
The consensus is not to refine the model, as
refinement usually pulls the model away from the
native structure!!
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Homology Modeling

• Presentation
• Fold recognition
• Model building
• Loop building
• Sidechain modeling
• Refinement
• Testing methods the CASP experiment

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The CASP experiment

• CASP Critical Assessment of Structure Prediction
• Started in 1994, based on an idea from John Moult
  (Moult, Pederson, Judson, Fidelis, Proteins,
  232-5 (1995))
• First run in 1994 now runs regularly every
  second year (CASP6 was held last december)

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The CASP experiment how it works
1) Sequences of target proteins are made
available to CASP participants in June-July of a
CASP year - the structure of the target protein
is know, but not yet released in the PDB, or
even accessible 2) CASP participants have
between 2 weeks and 2 months over the summer of
a CASP year to generate up to 5 models for each
of the target they are interested in. 3) Model
structures are assessed against experimental
structure 4) CASP participants meet in December
to discuss results
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CASP Statistics
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CASP
Three categories at CASP - Homology (or
comparative) modeling - Fold recognition - Ab
initio prediction
CASP dynamics - Real deadlines pressure
positive, or negative? - Competition? -
Influence on science ?
Venclovas, Zemla, Fidelis, Moult. Assessment of
progress over the CASP experiments. Proteins,
53585-595 (2003)
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CASP quality of alignment
Venclovas, Zemla, Fidelis, Moult. Assessment of
progress over the CASP experiment. Proteins
53585-595 (2003)
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CASP3 Sidechain modeling
SCWRL
Other
Dunbrack, Proteins, S3, 81-87 (1999)
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Homology Modeling Practical guide
Approach 1 Manual - Submit target sequence to
BLAST identify potential templates - For
each template - Generate alignment between
target and template (Smith-Waterman manual
correction) - Build framework - build
loop sidechain - assess model
(stereochemistry, )
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Homology Modeling Practical guide
Approach 2 Submit target sequence to automatic
servers - Fully automatic - 3D-Jigsaw
http//www.bmm.icnet.uk/servers/3djigsaw/ -
EsyPred3D http//www.fundp.ac.be/urbm/bioinfo/esy
pred/ - SwissModel http//swissmodel.expasy.or
g//SWISS-MODEL.html - Fold recognition -
3D-PSSM http//www.sbg.bio.ic.ac.uk/3dpssm/ -
Useful sites - Meta server
http//bioinfo.pl/Meta - PredictProtein
http//cubic.bioc.columbia.edu/predictprotein/