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.

4

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:

6

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

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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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Structure modeling


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)





Structure modeling


HomologyModel(s)

Template selection:

1. Sequence Similarity / Fold recognition

2. Structure quality (resolution, experimental method)

3. Experimental conditions (ligands and cofactors)


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Structure modeling


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





Structure modeling


HomologyModel(s)

• Errors in template selection or alignment result in bad models

iterative cycles of alignment, modeling and evaluation

Built many models, choose best.


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Structure modeling


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


[ http://www.expasy.org/spdbv/ ]

I. Manual Modeling

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Find structurally conserved core regions


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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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.


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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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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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


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


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a) 11 Cys residues Chi-1 angles

b) smoothed distribution from a)

c) 297 Cys Chi-1 angles as control


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


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


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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EMBnet course: Introduction to Protein Structure ... · Rosetta Stone Approach David Baker group...

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