embnet course: introduction to protein structure ... · rosetta stone approach david baker group...
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Swiss Institute of Bioinformatics
Torsten SchwedeBiozentrum - Universität Basel Swiss Institute of BioinformaticsKlingelbergstr 50-70 CH - 4056 Basel, Switzerland Tel: +41-61 267 15 81
EMBnet course: Introduction to Protein Structure Bioinformatics
Homology Modeling IBasel, September 30, 2004
[ PDB: http://www.pdb.org ]
Growth of the Protein Data Bank PDB
2
100
1'000
10'000
100'000
1'000'000
1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
TrEMBL
SwissProt
PDB
No experimentalstructure for most
sequences
Public Database Holdings
The protein sequence contains all information needed to create a correctly folded protein.
Can we predict protein structures from protein sequences alone (ab initio) ?
Many proteins fold spontaneously to their native structureProtein folding is relatively fast (nsec – sec)Chaperones speed up folding, but do not alter the structure
MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITKDEAEKLFNQD VDAAVRGILR NAKLKPVYDS LDAVRRCALI NMVFQMGETG VAGFTNSLRMLQQKRWDEAA VNLAKSRWYN QTPNRAKRVI TTFRTGTWDA YKNL
3
( )
( )
( )( )
∑ ∑
∑
∑
∑
= +=
+
−
+
−++
−+
−=
N
i
N
ij ij
ji
ij
ij
ij
ijij
torsions
N
anglesii
i
bondsii
i
rqq
rr
nV
k
llk
1 1 0
612
2
0,
2
0,
44
cos12
2
2
πεσσ
πε
γω
θθ
ν
Molecular Dynamics
Ab initio protein folding simulation
[ http://www.research.ibm.com/bluegene/ ]
Physical time for simulation 10–4 seconds Typical time-step size 10–15 seconds Number of MD time steps 1011
Atoms in a typical protein and water simulation 32’000 Approximate number of interactions in force calculation 109
Machine instructions per force calculation 1000 Total number of machine instructions 1023
BlueGene capacity (floating point operations per second) 1 petaflop (1015)
Blue Gene will need 1-3 years to simulate 100 µsec.
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Helix position
Am
ino
acid
sta
tist
ics
Rosetta Stone Approach
David Baker group
Find sequence patterns that strongly correlate with protein structure at the local level to create a library of fragments (I-sites).
E.g. „amphipathic helix“:
Rosetta Stone Approach
To build a model building for a new sequence:
Search for compatible fragments (reduced alphabet)
Use Monte Carlo simulated annealing to assemble overlapping fragments
Scoring functions are used to select best models (~1000)
http://isites.bio.rpi.edu
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Generates thousands of models
Best Models in CASP4: ~ 5 – 10 Å rmsd Ca
Difficult to distinguish good and bad models
http://isites.bio.rpi.edu
Rosetta Stone ApproachP
DB
sub
mis
sion
s pe
r yea
r
Year
Already known folds
New folds
The number of different protein folds is limited:
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Evolution of the globin family:
0.0
2.5
0.5
1.5
2.0
1.0
100 050
Percent identical residues in core
Rm
sdof
bac
kbone
atom
s in
core
[ Chothia & Lesk (1986) ]
Evolution of protein structure families
Common core = all residues that can be superposed in 3D
For proteins > 60% identical residues, the core contains >
90 % of all residues deviating less than 1.0 Å.
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.
0
20
40
60
80
100
0 50 100 150 200 250
identity
Number of residues aligned
Perc
enta
ge
sequen
ce
iden
tity
/sim
ilari
ty
(B.Rost, Columbia, NewYork)
Sequence identity implies structural similarity
Don’t know
region .....
Sequence similarity implies structural similarity?
.
0
20
40
60
80
100
0 50 100 150 200 250
identitysimilarity
Number of residues aligned
Perc
enta
ge
sequen
ce
iden
tity
/sim
ilari
ty
(B.Rost, Columbia, NewYork)
Sequence similarity implies structural similarity?
Don’t
know region .....
Sequence identity implies structural similarity
8
Homology modeling= Comparative protein modeling = Knowledge-based modeling
Idea: Using experimental 3D-structures of related family members (templates) to calculate a model for a new sequence (target).
Similar Sequence Similar Structure
Known Structures(Templates)
Target Sequence Template Selection
Alignment Template - Target
Structure modeling
Structure Evaluation &Assessment
HomologyModel(s)
Comparative Modeling
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Known 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 BLASTable database
or fold library (profiles, HMMs)
Comparative Modeling
Known Structures(Templates)
Target Sequence Template Selection
Alignment Template - Target
Structure modeling
Structure Evaluation &Assessment
HomologyModel(s)
Template selection:
1. Sequence Similarity / Fold recognition
2. Structure quality (resolution, experimental method)
3. Experimental conditions (ligands and cofactors)
Comparative Modeling
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Known Structures(Templates)
Target Sequence Template Selection
Alignment Template - Target
Structure modeling
Structure Evaluation &Assessment
HomologyModel(s)
• Multiple sequence alignment for pairs > 40% identity
or• Use structural alignment of
templates to guide sequence alignment of target
or• Use separate profiles for
template and targets
Comparative Modeling
Known Structures(Templates)
Target Sequence Template Selection
Alignment Template - Target
Structure modeling
Structure Evaluation &Assessment
HomologyModel(s)
• Errors in template selection or alignment result in bad models
iterative cycles of alignment, modeling and evaluation
Built many models, choose best.
Comparative Modeling
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Known Structures(Templates)
Target Sequence Template Selection
Alignment Template - Target
Structure modeling
Structure Evaluation &Assessment
HomologyModel(s)
I. Manual Model building
II. Template based fragment assembly
– Composer (Sybyl, Tripos)– SWISS-MODEL
III. Satisfaction of spatial restraints– Modeller (Insight II, MSI)– CPH-Models
Comparative Modeling
[ http://www.expasy.org/spdbv/ ]
I. Manual Modeling
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II. Template based fragment assembly
Find structurally conserved core regions
II. Template based fragment assembly
Build model core… by averaging core template backbone atoms (weighted by local sequence similarity with the target sequence). Leave non-conserved regions (loops) for later ….
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II. Template based fragment assembly
Loop (insertion) modelingUse the “spare part” algorithm to find compatible fragments in a Loop-Database, or “ab-initio” rebuilding (e.g. Monte Carlo, MD, GA, etc.) to build missing loops.
II. Template based fragment assembly
Side Chain placementFind the most probable side chain conformation, using
• homologues structure information• back-bone dependent rotamer libraries• energetic and packing criteria
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II. Template based fragment assembly
Rotamer Libraries
Only a small fraction of all possible side chain conformations is observed in experimental structures
Rotamer libraries provide an ensemble of likely conformations
The propensity of rotamers depends on the backbone geometry:
g+
trans
g-
p(g+ | phi)
p(t | phi)
p(g- | phi)
p(g+ | psi)
p(t | psi)
p(g- | psi)
Phe,Tyr, His
Backbone-dependent rotamer libraries
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II. Template based fragment assembly
Energy minimization
modeling method will produce unfavorable contacts and bonds
Energy minimization is used to
• regularize local bond and angle geometry
• Relax close contacts and geometric strain
extensive energy minimization will move coordinates away from real structure ⇒ keep it to a minimum
SWISS-MODEL is using GROMOS 96 force field for a steepest descent
III. Satisfaction of Spatial restraints
Alignment of target sequence with templates
Extraction of spatial restraints from templates
Modeling by satisfaction of spatial restraints
M
A
T
EA
F
TS
G
Q
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Some features of a protein structure:
R resolution of X-ray experimentr amino acid residue typeΦ, Ψ main chain anglest secondary structure classM main chain conformation classΧ i,, ci side chain dihedral angle classa residue solvent accessibilitys residue neighborhood differenced Ca - Ca distance∆d difference between two Ca - Ca distances
III. Satisfaction of Spatial restraints
Feature properties can be associated with
a protein (e.g. X-ray resolution)
residues (e.g. solvent accessibility)
pairs of residues (e.g. Ca - Ca distance)
other features (e.g. main chain classes)
How can we derive modeling restraints from this data?A restraint is defined as probability density function (pdf) p(x):
∫=<≤1
2
)()21(x
x
dxxpxxxp1)( =∫ dxxp
with
0)( >xp
III. Satisfaction of Spatial restraints
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a) 11 Cys residues Chi-1 angles
b) smoothed distribution from a)
c) 297 Cys Chi-1 angles as control
III. Satisfaction of Spatial restraints
Derive pdfs from frequency tables by smoothing:
4.0'2.0 << s4.0''2.0 << s
4.0'2.0 << s 6.0''4.0 << s 4.0''2.0 << s6.0'4.0 << s
III. Satisfaction of Spatial restraints
Combine basis pdfs to molecular probability density functions
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Satisfaction of spatial restraints
Find the protein model with the highest probability
Variable target function:
Start with a linear conformation model or a model close to
the template conformation
At first, use only local restraints
minimize some steps using a conjugate gradient optimization
repeat with introducing more and more long range restraints
until all restraints are used
III. Satisfaction of Spatial restraints
EVA
Evaluation of Automatic protein structure prediction [ Burkhard Rost, Andrej Sali, http://cubic.bioc.columbia.edu/eva/ ]
CASPCommunity Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction http://PredictionCenter.llnl.gov
Model Accuracy Evaluation
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Evaluation of Automatic protein structure prediction
[ Burkhard Rost, Andrej Sali, http://cubic.bioc.columbia.edu/eva/ ]
Target SequenceMNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK
New PDB ReleasePrediction Servers
e.g.
Evaluation of prediction accuracy
1
2
3
Typical types of errors
Sequence alignment errors.
Loops which cannot be rebuilt.
Inappropriate template selection.
Structural rearrangements.
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e.g. GROMOS, CHARMM, AMBER, ...
Which type of errors in a protein structure can you identify by an empirical force filed?
Which type of errors are not recognized?
Empirical Force Fields
Useful to identify regions with errors in backbone geometry
Statistical Methods
Ramachandran Plot of backbone angles (ϕ,ψ)favored regionsgenerously allowed regions disallowed regions
Amino acids with special properties:• PRO: ϕ = 60º• GLY (�)
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Probability for a feature to occur in a given
environment, e.g.
Solvent exposed / buried
Hydrophobic / polar environment
Electrostatic interactions
Secondary structure
etc.
1D - 3D Checks
+, Ile86
III, Ala182
II, Phe134
I, Val13
*, Met80
I II III*
Val13 Met80 Phe134 Ala182
A
B
+
Statistical Mean Force Potentials
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Atom Type Definitions
Distance Å
MFPkcal/mol
Methyl-Methyl pairsCysteine S-S-pairs
Distance Å
Statistical Mean Force Potentials
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ANOLEA : (Atomic Non-Local Environment Assessment)
http://protein.bio.puc.cl/cardex/servers/anolea/
http://swissmodel.expasy.org/anolea/
Correct Structure:PDB: 1GES
Model with wrongalignment:
Detects local packing errors
Errors in alignments
ANOLEA
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Checks the stereo-chemical quality of a protein structure, producing a
number of plots analyzing its overall and residue-by-residue geometry.
• Covalent geometry• Planarity• Dihedral angles• Chirality• Non-bonded interactions• Main-chain hydrogen bonds• Disulphide bonds• Stereochemical parameters• Residue-by-residue analysis
Laskowski R A, MacArthur M W, Moss D S & Thornton J M (1993). PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Cryst., 26, 283-291. Morris A L, MacArthur M W, Hutchinson E G & Thornton J M (1992). Stereochemical quality of protein structure coordinates. Proteins, 12, 345-364.
PROCHECK
WHAT IF I check my structure?
Imagine ...• An everyday situation in a biocomputing lab: "Should they use the structure?" • An everyday situation in a crystallography lab: "Should they deposit the structure already?" In a WHAT_CHECK report, each reported fact has an assigned severity:
error:severe errors encountered during the analyses. Items marked as errors are considered severe problems requiring immediate attention.
warning:Either less severe problems or uncommon structural features. These still need special attention.
note:Statistical values, plots, or other verbose results of tests and analyses that have been performed.
WHAT IF: A molecular modeling and drug design program. G.Vriend, J. Mol. Graph. (1990) 8, 52-56. Errors in protein structures. R.W.W. Hooft, G. Vriend, C. Sander, E.E. Abola, Nature (1996) 381, 272-272.
WhatCheck / WhatIf
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# 49 # Note: Summary report for users of a structureThis is an overall summary of the quality of the structure ascompared with current reliable structures. This summary is mostuseful for biologists seeking a good structure to use for modellingcalculations.
The second part of the table mostly gives an impression of how wellthe model conforms to common refinement constraint values. Thefirst part of the table shows a number of constraint-independentquality indicators.
Structure Z-scores, positive is better than average:1st generation packing quality : -2.5502nd generation packing quality : -5.472 (bad)Ramachandran plot appearance : -1.898chi-1/chi-2 rotamer normality : -1.433Backbone conformation : -2.173
RMS Z-scores, should be close to 1.0:Bond lengths : 0.905Bond angles : 1.476Omega angle restraints : 0.921Side chain planarity : 2.681 (loose)Improper dihedral distribution : 1.771 (loose)Inside/Outside distribution : 1.333 (unusual)
WhatCheck / WhatIf report for a bad model ...
All checking tools are happy, so can I believe it now?
Models are not experimental facts !
Models can be partially inaccurate or sometimes completely wrong !
A model is a tool that helps to interpret biochemical data.
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ANOLEA : (Atomic Non-Local Environment Assessment)
• http://protein.bio.puc.cl/cardex/servers/anolea/• http://swissmodel.expasy.org/anolea/
ProCheck
• http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html
WhatCheck
• http://www.cmbi.kun.nl/gv/whatcheck/
Verify3D
• http://www.doe-mbi.ucla.edu/Services/Verify_3D/
Biotech Validation Suite for Protein Structures
• http://biotech.ebi.ac.uk:8400/
Some useful Evaluation Tools
Save Zone
TwilightZone
MidnightZone
Model quality vs. sequence identity
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What can models be used for ?
Reference:
Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking.
Vangrevelinghe E, Zimmermann K, Schoepfer J, Portmann R, Fabbro D, Furet P.Oncology Research, Novartis Pharma, Basle, J Med Chem. 2003 Jun 19;46(13):2656-62.
Discovery of CK2a Inhibitors by in silico docking
Homology model of
the target molecule:
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In silico docking of a virtual library of 400‘000 compounds
Distributed Computing on PC Grid
Discovery of CK2a Inhibitors by in silico docking
• large scale experimental structure solution projects
Goal: Most of the sequences in a genome database should match
at least one structure with a sufficient sequence identity
allowing for reliable modeling.
Range of sequence space that can be modeled with acceptable accuracy.
The modeling error determines selection of targets for structural genomics.
Structural Genomics
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Structural Genomics – Target Selection
Protein Modeling Resources
SWISS-MODEL http://swissmodel.expasy.org
Modeller http://www.salilab.org
WhatIf http://www.cmbi.kun.nl/whatif/
3D-JIGSAW http://www.bmm.icnet.uk/people/paulb/3dj/form.html
CPHmodels http://www.cbs.dtu.dk/services/CPHmodels/
SDSC1 http://cl.sdsc.edu/hm.html
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