Bits protein structure

BITS TrainingProtein Structure

Joost Van DurmeVIB Switch Laboratory

Vrije Universiteit Brussel

http://www.bits.vib.be/training

VIB Switch Laboratory

Topics for today

• Exploring the protein structure databank (PDB)

• Viewing and analyzing protein structures with YASARA

• Comparing similar protein structures

• In silico mutagenesis with FoldX

• Homology modeling with FoldX


•PDB contains 65000 structures•EMBL-Bank contains 114,475,051 sequences or 215,540,553,360

nucleotides!

Sequences and structures


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structuressequences

The sequence-structure gap


• X-ray crystallography (crystals)• Nuclear Magnetic Resonance (NMR) (in solution)• Electron microscopy (in native tissue)

Structures can be solved


• Solving structures is lots of work (6 months to years)

• Need lots of material/reagents. Solubility is a problem.

• Some protein structures are really difficult to solve: membrane proteins, extremely large proteins, protein complexes

• The field evolves fast. Techniques improve, more user friendly software, more automatisation (x-ray infrastructure, crystal growth)

• Despite this progress it is not expected that the sequence-structure gap will ever be closed.

But ...


What can we learn from models/structures?

• Active site structure: structure-based drug design

• Protein-protein interactions

• Function• Antigenic behavior / vaccine development

• Stabilising proteins using structural knowledge


Human vs parasite

Parasite

Active site


1918 Influenza Epidemic Influenza Virus


NEURAMIDASE POCKET

SIALIC ACID


RELENZA SIALIC ACID


RELENZA TAMIFLU5.000.000+ doses in NL


Trouw – 3 maart 2009

Trouw – 3 maart 2009


RELENZA TAMIFLU

WT Ki = 1.0H274Y Ki= 1.9

WT Ki = 1.0H274Y Ki

=265

H274YH274Y


PDB structures come from ...

• X-Ray crystallography experiments

• NMR structure determination

The PDB no longer contains:• EM structures (too low resolution)• Models (too unreliable)


Principle of X-Ray crystallography

initial model

electron densities


X-Ray structure


X-Ray models components

• x, y, z coordinates: define the mean atom position

• disorder about this mean: B-factor and occupancy• variations in time and space

• B-factor:• model the ‘smearing out’ of disorder around the mean atom

position (ellipsoids)• higher B-factor means more uncertainty about position

• Occupancy:• consider alternative conformations of the same sidechain• how often do we find this sidechain in one conformation and

how often in the other conformation


Occupancy

ATOM 625 C ILE A 77 -11.322 28.374 -1.179 1.00 28.77 C

ATOM 626 O ILE A 77 -11.946 29.453 -1.112 1.00 28.84 O

ATOM 627 CA AILE A 77 -11.432 27.329 -0.087 0.70 28.15 C

ATOM 628 CB AILE A 77 -12.918 26.874 0.087 0.70 28.64 C

ATOM 629 CG1AILE A 77 -13.042 25.758 1.141 0.70 26.75 C

ATOM 630 CG2AILE A 77 -13.516 26.421 -1.241 0.70 28.13 C

ATOM 631 CD1AILE A 77 -13.378 26.302 2.501 0.70 26.47 C

ATOM 632 CA BILE A 77 -11.423 27.327 -0.082 0.30 28.50 C

ATOM 633 CB BILE A 77 -12.874 26.775 0.117 0.30 28.79 C

ATOM 634 CG1BILE A 77 -13.519 26.423 -1.227 0.30 28.62 C

ATOM 635 CG2BILE A 77 -13.748 27.739 0.916 0.30 28.40 C

ATOM 636 CD1BILE A 77 -14.720 25.518 -1.100 0.30 28.69 C

ATOM 637 N ARG A 78 -10.521 28.048 -2.183 1.00 28.70 N

ATOM 638 CA ARG A 78 -10.258 28.952 -3.268 1.00 28.47 C

ATOM 639 C ARG A 78 -10.857 28.469 -4.584 1.00 28.22 C

2VWC


Atomic B-factors

• Value which determines the precision of an atom’s given position

• Atoms with the largest B-factors will have the largest positional uncertainty

• Indication of mobility of an atom

• 0 < B < 20: Atom is most likely OK • 20 < B < 40: Atom is probably OK, but positional

errors up to 0.5 Ångstrom are normal • 40 < B < 60: Atom is probably reasonably OK, but be

careful, because positional errors up to 1.0 Ångstrom can be observed

• B > 60: Atom is not likely to be within 1.0 Ångstrom from where you see it

• B around 100: Atom is guaranteed not within 1.0 Ångstrom from where you see it


B-factor

www.YASARA.org

Low

High


Resolution (Angstrom)

• Level of detail that can be observed in the electron density map

• The greater the disorder in the crystal, the lower the resolution (proportional to the protein size)

3.0A 2.0A 1.2A


R-factor

• The difference between the observed and computed diffraction pattern

• A measure of how well the refined structure predicts the observed data

• Higher values mean less agreement

• 0.40-0.60: very unreliable• 0.20 seems to be the

standard threshold

electron density map


NMR Structure determination


NMR models components

• In solution• study protein dynamics• solve protein structures that are difficult to crystallize

• Nuclear Overhauser Effect or NOE• intensities of signal peaks correspond to short inter-atomic distances

between spatially close protons (NOE distances)

• NOE constraints are known with low precision. E.g. NOEs are binned 2.5-4.0, 4.0-5.5, and 5.5-7.0 Angstrom

• Multiple models are generated that are consistent with the distance and angle constraints using e.g. molecular dynamics: the NMR ensemble

• Take average or best model from ensemble for PDB deposition, or just deposit a selected ensemble of superposed structures


Structure superposition (root mean

square distance)

RMSD=∑i d i2n

n = number of atomsdi = distance between 2 corresponding atoms i in 2 structures

The more atoms superpose on each other, the lower the RMSD

Unit of RMSD => Ångstroms

identical structures => RMSD = “0”similar structures => RMSD is small (1 – 3 Å)distant structures => RMSD > 3 Å

However, care has to be taken as RMSD is length dependent and dominated by outliers:• comparison of two short peptide structures can result in a small RMSD even if their structure is visibly different.

• very similar structures can have a bad RMSD due to a short part of the structures that is very different (loops)

• Insertions and deletions are not implemented in the RMSD calculation, since we only look at equivalent atoms/residues (see figure)


NMR ensemble RMSD (root mean square

distance)

• Superpose the NMR models

• Calculate RMSD of local regions and also whole models

• Regions with high RMSD are less well defined by the data


Structural data is stored in the Protein Data Bank (PDB)

http://www.pdb.org

Protein Data Bank (PDB)


©CMBI 2009©CMBI 2009

Protein Data Bank (PDB)

•Databank for 3-dimensional structures of biomolecules:

• Protein• DNA• RNA• Ligands

•Obligatory deposit of coordinates in the PDB before publication

•~ 65000 entries (April 2010) ( ~27000 “unique” structures)

• PDB file is a keyword-organised flat-file (80 column)1) human readable2) every line starts with a keyword (3-6 letters)3) platform independent


©CMBI 2009

PDB important records (1)

•PDB nomenclatureFilename= accession number= PDB CodeFilename is 4 positions (often 1 digit & 3 letters, e.g. 1CRN.pdb)

•HEADERdescribes molecule & gives deposition dateHEADER PLANT SEED PROTEIN 30-APR-81 1CRN

•CMPNDname of moleculeCOMPND CRAMBIN

•SOURCEorganismSOURCE ABYSSINIAN CABBAGE (CRAMBE ABYSSINICA) SEED


©CMBI 2009


•SEQRESSequence of protein; be aware: Not always all 3d-coordinates are present for all the amino acids in SEQRES!!SEQRES 1 46 THR THR CYS CYS PRO SER ILE VAL ALA ARG SER ASN PHE 1CRN 51SEQRES 2 46 ASN VAL CYS ARG LEU PRO GLY THR PRO GLU ALA ILE CYS 1CRN 52SEQRES 3 46 ALA THR TYR THR GLY CYS ILE ILE ILE PRO GLY ALA THR 1CRN 53SEQRES 4 46 CYS PRO GLY ASP TYR ALA ASN 1CRN 54

•SSBONDdisulfide bridgesSSBOND 1 CYS 3 CYS 40

SSBOND 2 CYS 4 CYS 32


©CMBI 2009


and at the end of the PDB file the “real” data:

ATOMone line for each atom with its unique name and its x,y,z coordinatesATOM 1 N THR 1 17.047 14.099 3.625 1.00 13.79 1CRN 70ATOM 2 CA THR 1 16.967 12.784 4.338 1.00 10.80 1CRN 71ATOM 3 C THR 1 15.685 12.755 5.133 1.00 9.19 1CRN 72ATOM 4 O THR 1 15.268 13.825 5.594 1.00 9.85 1CRN 73ATOM 5 CB THR 1 18.170 12.703 5.337 1.00 13.02 1CRN 74ATOM 6 OG1 THR 1 19.334 12.829 4.463 1.00 15.06 1CRN 75ATOM 7 CG2 THR 1 18.150 11.546 6.304 1.00 14.23 1CRN 76ATOM 8 N THR 2 15.115 11.555 5.265 1.00 7.81 1CRN 77ATOM 9 CA THR 2 13.856 11.469 6.066 1.00 8.31 1CRN 78ATOM 10 C THR 2 14.164 10.785 7.379 1.00 5.80 1CRN 79ATOM 11 O THR 2 14.993 9.862 7.443 1.00 6.94 1CRN 80


PDB entry


PDB entry


PDB entry


©CMBI 2009

Structure Visualization

Structures from PDB can be visualized with:

1. YASARA (http://www.yasara.org)

2. SwissPDBViewer (http://spdbv.vital-it.ch/)

1. PyMOL (http://www.pymol/org)

1. Chimera (http://www.cgl.ucsf.edu/chimera )


YASARA View nomenclature

Atom Residue = any continuous stretch of atoms sharing the same residue name, residue number and molecule name

Molecule = any continuous stretch of residues sharing the same molecule name (PDB calls this a CHAIN)

Object = a collection of molecules and additional items


Standard atom colors

• C = cyan• O = red• N = blue• H = white• S = green


Atom nomenclature

Cα

Cβ

Cγ

Oγ

N

N

O

Cα

Cβ

C

C

Cγ

Cδ1Cδ2

OT1

OT2

N-term

C-term


FoldX: a molecular design toolkit

• Predict the effect of point mutation on the protein stability

• Predict the 3D structure of a sequence: homology modeling


Predict effect of point mutation

• FoldX is an empirical force field• It is validated with calorimetric experiments• E.g. If such an experiment concludes that breaking a

hydrogen bond costs 1.5 kcal/mol, FoldX uses this knowledge rather than using theoretical physics equations

• FoldX compares WT and mutant for:• Hydrogen bonds, electrostatics, Van der Waals clashes

and contacts, entropy, desolvation, ...

• FoldX energies• Energy of a single molecule is meaningless• The difference in energy of two molecules (such as WT

and a point mutant) approaches realistic values


Predict effect of point mutation

• FoldX calculates the stability of WT and MT and makes the difference (net effect of mutation):• ΔGMT-ΔGWT = ΔΔGmutation

• If ΔΔGmutation

• > 0 : mutation is bad for stability• < 0 : mutation is good for stability

• FoldX error margin is 0.5 kcal/mol, so changes within this margin are meaningless


Introduction to homology modeling

• Goal: predict a structure from its sequence with an accuracy that is comparable to the best results achieved experimentally (X-Ray)

• Protein modeling is the only way to obtain structural information when experimental techniques (x-ray, NMR, EM) fail


Homology Modeling


Principles of Homology Modeling

• Search for a sequence with a known structure that is very similar to the sequence with the unknown structure. Build model using known structure as template

• The structure of a protein is uniquely determined by its amino acid sequence

• Structure is more conserved than sequence• Similar sequences adopt nearly exact same

structure• Distantly related sequences can still fold into

a similar structure


Sequence similarity rule

• Rost (1999) modeled lots of structures and compared them to the real ones in the PDB

• Derived precise limits for homology modeling• This rule tells you whether a model will be reliable or unreliable


FoldX plugin for YASARA


Acknowledgements

• Gert Vriend, Radboud Universiteit Nijmegen, NL (www.cmbi.ru.nl)

• Sander Nabuurs, Lead Pharma, Nijmegen, NL

• Greet De Baets, VIB Switch Laboratory

http://www.cmbi.ru.nl/

Bits protein structure

Technology

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refined structure