Thesis Summary
Robustness of the elastic network model against chemical or physical fitting ofparameters( パラメタの化学的・物理的最適化に対する弾性ネットワークモデルの頑健性 )Name: AMYOT Romain

Elastic network model (ENM) is a simple representation of proteins where aminoacids are considered as point-like particles and interactions as harmonic ones.
Pairs of amino acids for which the distance is under a certain cut-off distance,
are connected by linear springs with the same stiffness for all interactions.
Within the assumption of small fluctuations, it has successfully been coupledwith the normal mode analysis. Normal mode analysis (NMA) expresses thedynamicsofthemodelthroughmodes

anddecomposes

theprotein

conformational motions into the fundamental motions vibrating along each mode.
Although the ENM has been able to reproduce the motion of some proteins (e.g.
HCV Helicase), it does not contain any chemical information about proteins. The
springs are set in an empirical manner disregarding the chemical details of theprotein. Many attempts have been made to include such properties in themodelling and especially, a recent study has proposed amino-acid dependent
spring constants determined statistically using experimental data. For the lattermodels, improvements have been shown by measuring performance at theprotein level. Here, we decided to investigate in depth these models by looking
at the residue level in order to explain the origin of and how valuable is theimprovement. For that, we have first looked, using the NMA, at the prediction offluctuations of individual residues which reveal a poor sensitivity to the additionof the sequence specificity. The differences with classical models reside onunstructured parts, poorly connected, which bias the global improvement seen atthe protein level. The study on collective behaviours (transitions, inter-residuesfluctuations) has given the same conclusions. Without the assumption of small
fluctuations (and hence of linearity), we have run molecular dynamics
simulations using the ENM for relaxation trajectories. Again, no sensible
improvement has been observed when considering the sequence of residues.
Such conclusions have led us to consider the robustness of the elastic networkmodel against the fitting of spring constants. Using the, same measuringmethods,we have studied some well-known heterogeneous ENM for which thespring constants are distance dependent. It appears that each of them gives
similar results to a classical model with a certain cut-off distance. Finally, wehave used an extreme method by totally randomising the spring constants in an
incoherent way and look at the spring constant values of the most efficient
random networks. The common point over several protein examples is thestiffening of the flexible regions. The rest of the structure has typically noincidence on the improvement at the protein level. In other words, the best
random networks do not show any coherent heterogeneity in the spring constantvalues which could be related to the biological or chemical properties of theprotein. Our conclusion is that the performance of the elastic network model ismainly determined by the structure itself and that the fitting of spring constants is
not a valuable way of bringing chemical or physical details in the modelling.