1. Freezing Immunoglobulins to see them move
Biology after the genome: a physical view
BIFI-2004, Zaragoza
Francesco Piazza
EPFL, Switzerland

2. The ideas and the message
Dynamics is a central issue to understand protein functions
A quantitative description of protein dynamics would represent a crucial leap forward
Dynamical features of proteins relevant to their functions can be modeled on a coarse-grained scale (collective motions)
Freeze individuals and you will see them move

3. Overview of the talk
Cryo-Electron Tomography and COMET: high-resolution images of individual proteins in solution
The IgG molecule
A coarse-grained physical model of the IgG dynamics: from a gallery of snapshots to the potential energy
Studying the binding to an antigen

4. Cryo-Electron Tomography
Part 1

5. Electron Tomography process
Preparation
Microscopy
Reconstruction
Visualization

6. The temperature quenching
Monoclonal IgG2a
In a Buffer Solution
10nm Colloidal Gold
Ethane
Plunge freezing gives hydrated
amorphous vitrified sample. Quenching rate oC/sec
Nitrogen

7. The measurements

8. Tomography: look to an object from different directionsq
The number of sides you have to look at increases with the size of the sample and the accuracy you wish to achieve in 3D.
Tiltseries

9. A real reconstruction 1 projection at 0 degree 3 projections
from –30 to +30 degrees
7 projections
from –60 to +60 degrees
13 projections
from –60 to +60 degrees
121 projections
from –60 to +60 degrees
180 projections
from –90 to +89 degrees
© Sergej Masich

10. Cryo-Electron Tomography and COMET

11. The immunoglobulin IgG
Part 2

12. The general structure of Immunoglobulins
Antigen-Binding Fragments
Hinge region: flexible
peptide chains mutually
crossing. Hinged onto
di-sulfide bonds

13. Post refinement with COMET
Low-dose
tilt series
Filtered
back
projection
COMET-
refined

15. Docking of crystallographic structures into the tomograms
Multi-resolution docking (SITUS) of the IgG2a atomic X-ray structure into the reconstructed structure (wireframe).

16. Crystallographic structure is not the only arrangement
The tomograms reveal a great variability of the Fab-Fab angle
Crystallographic structures can be docked into the tomograms provided the Fab-Fab angles are modified

17. Gallery of 3D refined reconstructions
# 1
# 2
# 3
Putting together different snapshots of a moving object amounts to sampling its dynamics

18. Physics meets Biology
Part 3

19. The general method Gallery of 3D snapshots Equilibrium distribution
of configurations
The full movie
Potential Energy
Dynamics: study of biological
functions through simulations

20. Equilibrium at temperature T, the system occupies
“points”
in the phase space, with density
The probability density in the phase
space depends on the kinetic and
potential energies. In principle, one
may invert such dependency to determine the potential if the density is known
We have access to the
configurations only. Hence,
We must integrate out the
velocities

21. A coarse-grained model of the IgG
We describe the system in terms
of the angular coordinates

22. Factorization The statistics is poor
Identification of front and back sides of the molecule falls outside the experimental resolution

23. The experimental distributions: the angles f

24. The experimental distributions: the angles q

25. The potentials: V1(f)

26. The potentials: V2(q) p

27. Langevin dynamics dissipation fluctuation Input from the experiments
Fluctuation-dissipation
theorem

29. The encounter rate (ns) RANTIGEN (Å)
Langevin dynamics of the diffusion-controlled
antigen-antibody encounter
(ns)
Dynamics favours
the encounters
RANTIGEN (Å)

30. Conclusions
Potential energy of large-scale displacements in macromolecules from Cryo-tomograms of individual objects
The Immunoglobulin IgG
Understanding the relation between dynamics and functions opens new perspectives to molecular engineers

31. Perspectives
Formation of the encounter complex: cooperativity vs anticooperativity in the overall rate
Binding of large antigens: cooperativity of the two Fab arms
Coupling of Fab oscillations with instabilities in the Fc terminal region

32. Project coworkers Duccio Fanelli, KI, Sara Sandin, KI,
Lorenzo Bongini, Univ. of Florence,
Duccio Fanelli, KI,
Sara Sandin, KI,
Ulf Skoglund, KI
Lars-Göran Öfverstedt, KI.
Francesco Piazza, EPFL,
Paolo De Los Rios, EPFL.