Download Modelling protein Modelling protein--surface interactions: a surface interactions: a challenge for computations

Modelling protein
protein--surface interactions: a
challenge for computations
A. Calzolari, R. Di Felice, F. Iori, S.Corni
INFM-CNR S3 National Research Center on nanoStructures and bioSystems at
Surfaces, Modena, Italy
G. Cicero - Politecnico di Torino, Italy
A. Catellani – CNR-IMEM, Parma, Italy
C. Cavazzoni – CINECA, Bologna, Italy
Proteins
• Long linear chains of a-amino acids
• 20 natural a-amino acids
• Have complex 3D structures
• Perform different functions, e.g.:
– Catalyze chemical reactions (enzymes)
– Bind external molecules (immunoproteins)
– Transport electrons between redox partners
Can specifically recognize other molecules/proteins
Exploiting the intrinsic capabilities of proteins
• Proteins can recognize other proteins/molecules
Exploiting the intrinsic capabilities of proteins
• Proteins can recognize other proteins/molecules
Can we design a protein specific for a given surface?
Can proteins recognize
surfaces?
or
a surface that maximally/minimally binds a given protein?
Proteins specific for a surface
Examples of possible applications
•Use proteins to guide the
self-assembly of inorganic
nanodevices (proteins as a
smart glue)
•Use protein to control the growth of inorganic materials, e.g., to obtain nanocrystals
with new shapes
Examples of possible applications
protein specific for a surface
large gold sphere
•Use proteins to guide the
self-assembly of inorganic
nanodevices (proteins as a
smart glue)
small polystirene sphere
•Use protein to control the growth of inorganic materials, e.g., to obtain nanocrystals
with new shapes
Surface
Surface--recognizing peptides via
combinatorial biotechnologies
• In recent years, combinatorial biochemical methods have been
used to select the best binder to given surfaces, or to find
selective binders
iterate
recover
the binders
amplify
S. Brown, PNAS 89, 8651 (1992); M. Sarikaya et al. Nature Mat. 3, 577 (2003)
Proteins can recognize surfaces
• Some specific proteins indeed found in this way, e.g.:
GaP
C. Tamerler et al., Small 2006
InP
Ge
K.Goede et al., NanoLett 2004
A.Artzy-Schnirman
al., NanoLett 2006
•However:
– Still no understanding of the basic physical mechanisms that
govern the specific interaction
No concepts to enable rational
design
–Combinatorial selects proteins for a given surface, not viceversa
et
Our (long
(long--term) goal
Understanding the basic physical
principles that govern specific proteinsurface interactions by computational
methods
Outline
• Motivations
• General computational strategies
– Multiscale modelling
– Ab initio on case studies
• Ab initio molecular dynamics for poly-serine on Au(111)
– Method
– Simulated system
– Results
• Summary
Complexity of the systems
Computational challenge:
• Very large systems(103-104 atoms)
• Protein + Inorganic Surface + Solvent
• Multiple length scales
• Require statistical sampling and/or
simulation of time evolution
• Multiple time scales
• Involve interactions of different origins
• Chemical bonds, Coulomb, dispersion...
• Different methods most suitable for
different portions
Choice of prototypical systems
• The uncertainties on real surfaces (e.g., presence of defects,
steps, amorphous oxide layers) and on proteins/peptides (e.g.,
unknown structure, too flexible peptides) introduce further
complexity in modelling.
• To minimize uncontrolled assumptions, initially focus on welldefined and well-characterized surfaces and polypeptides.
• The knowledge generated from these studies on a range of
protoypical systems fosters understanding of more complex
examples.
Computational strategies
Multiscale modelling
relatively general but long term
(needs to be developed)
Using the most accurate,
already available methods
feasible only for case studies
(need enormous computer power)
Computational strategies
Multiscale modelling
relatively general but long term
(needs to be developed)
Using the most accurate,
already available methods
feasible only for case studies
(need enormous computer power)
Setting
Setting--up the multiscale scheme
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Parametrization of AA fragmentsurface interaction energy: QM
validation
From energy of fragments to freeenergy of AAs in water Classical MD
validation
From free-energy of AAs to
free energy of proteins BD
validation
Computational multiscale toolbox
The Prosurf project
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Parametrization of AA fragmentsurface interaction energy: QM
validation
From energy of fragments to freeenergy of AAs in water Classical MD
validation
From free-energy of AAs to
free energy of proteins BD
validation
Computational multiscale toolbox
“Computational
toolbox for proteinsurface docking”
The Prosurf project
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Parametrization of AA fragmentsurface interaction energy: QM
validation
From energy of fragments to freeenergy of AAs in water Classical MD
validation
From free-energy of AAs to
free energy of proteins BD
validation
Computational multiscale toolbox
“Computational
toolbox for proteinsurface docking”
Target surfaces
• Au(111)
–
–
–
–
stable in air and water
important for nanobioelectronics (contacts)
used in optical detection systems for protein materials
target surface for well-characterized gold binding peptides
• Fully hydroxilated a-Al2O3(0001)
– ceramic surfaces used in today's biomaterials
– transparent and thus used in optics application
– well-characterized surface, even when hydrated
Target surfaces
• Au(111)
–
–
–
–
stable in air and water
important for nanobioelectronics (contacts)
used in optical detection systems for protein materials
target surface for well-characterized gold binding peptides
• Fully hydroxilated a-Al2O3(0001)
– ceramic surfaces used in today's biomaterials
– transparent and thus used in optics application
– well-characterized surface, even when hydrated
GolP: a force field for proteinprotein-surface
interaction in water
• Other FFs for gold exist (e.g., Zerbetto et al.), not tailored for
proteins in water
• Derived from DFT calculations + experimental data + MP2
calculations
acetone
trans 2 butene
diethylsulfide
m
1-nonene
F. Iori et al. J. Comp. Chem. 30, 1465 (2009); J. Comp. Chem. 29, 1656 (2008)
GolP force field in action
• Studying liquid water on Au(111)
• Simulating b-sheets proteins/peptides adhesion on Au(111) (in
collaboration with S.Monti, M. Hoefling, K.Gottschalk)
• Interpreting electron transfer measurements for cytochrome C
mutants on gold (see poster by M. Siwko)
• Studying potential of mean force for amino acids adsorption on
gold (by M.Hoefling and K. Gottschalk)
• Developing free-energy models for Brownian dynamics (by
D.Kokh, B. Huang, R. Wade)
Computational strategies
Multiscale modelling
relatively general but long term
(needs to be developed)
Using the most accurate,
already available methods
feasible only for case studies
(need enormous computer power)
Outline
• Motivations
• General computational strategies
– Multiscale modelling
– Ab initio on case studies
• Ab initio molecular dynamics for poly-serine on Au(111)
– Method
– Simulated system
– Results
• Summary
Ab Initio Molecular Dynamics (AIMD)
• Nuclei: classical particles moving on a energy
surface determined by electron density
• Electrons: quantum particles that follow the slower
nuclear motion
Car-Parrinello AIMD: a single equation of motion with fictitious electronic
degrees of freedom, to keep electrons in the ground state during the
nuclear dynamics
Ab Initio Molecular Dynamics (AIMD)
No empirical parameters
• Nuclei: classical particles moving on a energy
surface determined by electron density
• Electrons: quantum particles that follow the slower
nuclear motion
Car-Parrinello AIMD: a single equation of motion with fictitious electronic
degrees of freedom, to keep electrons in the ground state during the
nuclear dynamics
Simulated system
Water layer
Protein: poly-Serine b-sheet
Interstitial water layer
Surface: Au(111)
4 layers; 23x7 supercell; unreconstructed
3D periodic boundary conditions
In total: # atoms = 587; # electrons = 2552
Simulated system
Why polySerine?
• Serine (and other hydroxilated AA) appears
in experimental gold binding peptides (GBP)
• Redundancy improves averaging
Why b-sheet?
• Maximize contact with the surface
• Allow easy implementation of periodic
boundary condition
Details of the calculations
 ab initio Car-Parrinello molecular dynamics
 xc: PBE, PW basis set (Ecut=25 Ry ), ultrasoft pseudopotential
 CODE = cp.x (quantum espresso package)
 Running machine = MareNostrum (Barcelona, Spain) ): DEISA project
 Total CPU time = 350 000 CPU hours.
 Bloechl-Parrinello thermostat for electrons
 20 ps of simulated evolution at T= 400K; time step dt=0.17 fs
Outline
• Motivations
• General computational strategies
– Multiscale modelling
– Ab initio on case studies
• Ab initio molecular dynamics for poly-serine on Au(111)
– Method
– Simulated system
– Results
• Summary
Main results
• Polyser and water has a weak but not
negligible interaction with on Au(111).
• Protein and water recognize the atomic
corrugation of the gold surface.
• Protein+hydration layer creates an object with
well-defined geometric properties
Main results
• Polyser and water has a weak but not
negligible interaction with on Au(111).
• Protein and water recognize the atomic
corrugation of the gold surface.
• Protein+hydration layer creates an object with
well-defined geometric properties
Small charge transfer to Au
e- popul. per Ser side chain O
(superpos. of all O at all snapshots)
e- population per Au atom
External Au layers have
excess negative charge
Löwdin
öwdin pop.
Löwdin
öwdin pop.
(superpos. of all Au at all snapshots)
Small charge transfer to Au
e- popul. per water O atom
(superpos. of all O at all snapshots)
e- population per Au atom
External Au layers have
excess negative charge
Löwdin
öwdin pop.
Löwdin
öwdin pop.
(superpos. of all Au at all snapshots)
Orbital mixing?
Projected density of states for serine
PDOS (a.u.)
Non-interacting
Interacting
red O
black tot
energy (eV)
occupied
virtual
Simplifying the system: a single
methanol molecule on Au(111)
side chain model
occupied
virtual
Eint= -12 kJ/mol
Simplifying the system: a single
methanol molecule on Au(111)
side chain model
occupied
virtual
Eint= -12 kJ/mol
Simplifying the system: a single
methanol molecule on Au(111)
side chain model
occupied
virtual
Eint= -12 kJ/mol
• Small but detectable O-to-Au charge transfer
• Small but detectable perturbation of PDOS.
• However, interaction is weak, as in experiments*
(small interaction energy; protein and water slide on
the surface)
*water: Kay et al., JCP 1989
serine: Peelle et al. Langmuir 2005
Main results
• Polyser and water has a weak but not
negligible interaction with on Au(111).
• Protein and water recognize the atomic
corrugation of the gold surface.
• Protein+hydration layer creates an object with
well-defined geometric properties
How to describe relative Au
Au--O
positions?
In-plane pair distribution function:

g AuO (  ) 
1
N Au N O

Au



   O   Au   


O
Meaning: where atoms O sit w.r.t. an atom Au, averaged on
all the Au atom and projected on the surface plane.
gAuO averages over the exposed Au and O atoms -> takes
advantage of the redundancy in our system
The protein recognizes the atomic
structure of the gold surface
max
color scale: gAuOser(x,y):
where Oser sits w.r.t. Au atom,
averaged on Au atoms and
projected on the surface
0
Oser prefers one adsorption site over the others
The same for water...
max
0
Owat prefer one adsorption site over the others
• Polyser and water “are aware” of the atomic
structure of the surface.
• Oser localize in bridge sites; Owat localize on
top sites
O wat
O ser
Effects of missing long
long--range
dispersion?
• GGA xc functionals do not correctly describe longrange dispersion
• No direct estimate of effects of long-range dispersion
in our system
• However: for related systems (rare gases on metals):
– on-top is the preferred adsorption site
– for tested cases, adsorption site preferences are not
modified by adding dispersion
•Calc
Effects of dispersion on adsorption
geometries of rare gases on metals
•Exp
Bruch et al.
Rev. Mod. Phys. 2007
top and bridge
Main results
• Polyser and water has a weak but not
negligible interaction with on Au(111).
• Protein and water recognize the atomic
corrugation of the gold surface.
• Protein+hydration layer creates an object with
well-defined geometric properties
Profiles of atomic densities
Profiles of atomic densities
The solvent and
gold exposed
protein surfaces
include a solvent
layer
Hydration layers
•Hydration layer exchanges with the solution
•Hydration water is structured
The surfaces of the hydrated protein
max
0
density map of WAT Oxygen in the hydration layers
The surfaces of the hydrated protein
max
0
SER Oxygens not distinguishable by Water Oxygens
• Protein and water recognize the surface
• Hydration water is (dynamically) bound to the
protein in well-defined positions (similar to
antifreeze proteins Nuts et al. JACS 2008)
• In spatial matching between the protein and
the surface, hydration water may play a role
Summary
•
Specific protein-inorganic surface interactions are
promising for several nanobiotechnology applications
•
Basic principles regulating such interactions are not
understood; High level calculations can shed light on
them
•
Main results of ab initio simulation:
1. Polyser and water do not chemisorb on Au(111). However, a
weak interaction is indeed present.
2. Protein and water recognize the atomic corrugation of the gold
surface.
3. Protein+hydration layer creates an object with well-defined
geometric properties
Acknowledgments
E
x
p
E. Molinari, F. Iori, M. Siwko, F. De
Rienzo, A. Calzolari, A. Catellani, R. Di
Felice@MODENA: QM
G. Schreiber
O. Cohavi
D. Reichman
A. Vaskevitch
I. Rubinstein
A. B. Tesler
O. Kedem
T. Karakouz
@WEIZMANN
validation
K. Gottschalk, M. Hoefling @LMU:
Classical MD
validation
R. Wade, B.Huang, D.Kokh,
P. Winn@EML: BD
validation
Computational multiscale toolbox
SIXTH FRAMEWORK
PROGRAMME
Acknowledgments
• Computational time and assistance with calculations
• Funding:
SIXTH FRAMEWORK
PROGRAMME
Acknowledgments
• Computational time and assistance with calculations
• Funding:
SIXTH FRAMEWORK
PROGRAMME
Thank you for your attention!
The same for water...
max
hollow site
0
on-top site
bridge site
Owat prefer one adsorption site over the others
Au
Au--O distribution functions:
Classical system set
set--up vs ab initio run
During
system
set up
SER Oxygen
Ab initio
run
WAT Oxygen
•Calc
Effects of missing van der Waals on
adsorption geometry
•Exp
Bruch et al.
Rev. Mod. Phys. 2007
top and bridge
The trajectory
Only water molecules between the protein and the surface are shown
A single water molecule on Au(111)
occupied
virtual
HOMO at G
Dr