Transcript CS689-homology-modeling - TAMU Computer Science Faculty

Homology Modeling
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comparative modeling vs. ab initio folding
alignment (check gaps)
threading
loop building
re-packing side-chains in core, DEE, SCWRL
fold evaluation/scoring
statistical potentials (pot. of mean force) - DFIRE
minimization
servers: Swiss-model
• ab initio folding
– Rosetta (Baker)
– MONSSTER (Skolnick)
– I-TASSER (Zhang)
Sequence Alignment
• critical step
• gaps should be in loops (check in final model)
• dynamic programming (Smith-Waterman)
– LALIGN:
http://www.ch.embnet.org/software/LALIGN_form.html
– adjust gap parameters: gap-open penalty>x?
gap-extension penalty<x? x=average match score
– could also adjust substitution matrix (PAM250,
BLOSUM62)
• use PSI-Blast to include info from homologs
– iterative: retrieves homologs, refines search...
• use HMM to align to family
Threading
• use info about 3D structure to
improve alignment
• local secondary structure,
solvent-accessibility
• 3D profiles (Eisenberg)
• 3D-PSSM/Phyre (Sternberg,
Lawrence Kelley)
• THREADER
• RAPTOR
MODELLER (Sali)
• references
– A. Šali and T. L. Blundell. Comparative
protein modelling by satisfaction of spatial
restraints. J. Mol. Biol. 234, 779-815, 1993.
– A. Fiser, R. K. G. Do and A. Š ali. Modeling
of loops in protein structures. Protein
Science 9, 1753-1773, 2000.
– Fiser A, Sali A. (2003). Modeller: generation
and refinement of homology-based protein
structure models. Methods Enz. 374:461-91.
• loop-modeling via dynamics
• evaluation:
– >30% identity?
– stereochemistry: Procheck
– contacts/exposure: ProSA (Sippl,
1993) – distance-based pair
potentials
Side-chain re-packing
• mutations cause steric conflicts (and voids)
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changing rotamers can relieve conflicts
adjacent side-chains are coupled
multiple changes might be required
combinatorial search: exhaustive versus
Monte Carlo (Holm & Sander, 1992)
• DEE (Dead-End Elimination)
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pruning method
pre-processing, singles, pairs
Desmet, Mayo
reduction in branching factor?
• rigid backbone assumption
– how important is backbone flexibility?
• interesting application: use DEE to
– also sample alternative backbone
determine rotamer populations for
conformations at each site
tryptophans; use to predict fluorescence
– (Georgiev and Donald, 2007)
quenching times (Hellings 2003, BiophysJ)
SCWRL 3.0
• Canutescu et al. (2003)
– Dunbrack BBdep rotamer library
– de-couple interaction graph into
bi-connected components
representing local dependencies
side-chain interactions:
energy of configuration:
• TreePack (Xu and Berger, JACM 2006)
– geometric neighborhood graph decomposition; up to 90x faster
Loop Modeling
• two approaches:
1. MD/conformational sampling
2. templates from loop library
• accuracy depends on length:
2-4 (turns), 4-8, >8 (ab initio)
• importance in immunoglobulins
(hyper-variable loops in
antigen-binding region)
• modeling loops via molecular dynamics
– Monte Carlo conformational search using a FF/energy function,
high temp MD: 800K (Bruccoleri & Karplus, 1990)
– “Does Conformational Free Energy Distinguish Loop
Conformations in Proteins?”
• templates from loop library (examples from existing
structures)
– amino acid similarities
– fit to stems:
• Ca distance, vectors (i-1:i,j,j+1), carbonyls?, f/y angles
Scoring
• statistical potentials
– knowledge-based poten (Sippl, 1990)
– potential of mean force
– residue-based potentials (e.g. Cb-Cb
contact distance, or centers-of-mass)
• atomic pairwise potentials
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– capture side-chain interactions better
– discriminate correct folds better
– z-score of true fold vs. decoys
(gapless threading)
DFIRE (Yaoqi Zhou)
• Distance-scaled Finite Ideal-gas REference state
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Zhou and Zhou (Prot. Sci, 2002)
all-atom potential
Nexp(i,j,r) will not increase in r2 as in an infinite system
a=1.57 gives best correlation with density in radial shells
improves ability to recognize correct fold versus decoys
instead, assume:
fair???
– see also:
• RAPDF (Samudrala and Moult, 1998)
• DOPE (Shen and Sali, 2006)
Minimization
• a logical step, however...
• one of the conclusions from CASP4 (Baker):
– minimization generally made models worse (took
predicted structures farther from native)
– threshold:
• minimization works if rmsd<2Å,
• but ab initio models are often 4-6Å rmsd
– backbone adjustments required?