When I just joined the Lindor-Larsen group as a fresh PhD student, the
Nobel Prize in Chemistry that year was awarded for the development of
multiscale models for complex chemical systems" to prize the pioneering
works of Martin Karplus, Michael Levitt and Arieh Warshel. As a computational
biologist, I was proud and excited for the breaking news as this
prize is not only to them, but also to the whole community of computational
biology. There has been progress in the modeling of protein dynamics in recent
years and it has also started to be clear that computer simulations play
an irreplaceable role rather than supporting role of wet-lab experiments, to
obtain a complete understanding of complex biomolecules. Some of the
progress in the eld has been introduced in the rst Chapter of this thesis.
Despite its enormous success, this eld has not yet been fully developed.
In some respects, for example, accurately quantifying the free energy differences
and transition times of protein conformational exchanges and their
dependence on sequence modications, we are still at the early stages.
In this dissertation, I present a number of new methodological improvements
and applications for protein folding, conformational exchange and
binding with ligands at long time scales. In Chapter 2, we benchmarked
how well the current force elds and molecular dynamics (MD) simulations
could model changes in structure, dynamics, free energy and kinetics for
an extensively studied protein called T4 lysozyme (T4L), whose conformational
dynamics however is still not fully understood. We found modern
simulation methods and force elds are able to capture key aspects of how
this protein changes its shape, paving the way for future studies for systems
that are dicult to study experimentally. In Chapter 3, we revisited
the problem of accurately quantifying the thermodynamics and kinetics, by
following a novel route. In this route both of the forward and backward
rates are calculated directly from MD simulations using a recently developed
enhanced sampling method, called \infrequent metadynamics", and
subsequently used to estimate the free energy dierences based on a twostate
assumption. To show its practical utility, we applied this approach
by taking T4L-benzene system as the model system in which binding free
energies from kinetics, free energy perturbation and experiments are all in
good agreement. Indeed, this route has also been applied to calculate the
kinetics and thermodynamics of the conformational exchange of T4L (as
shown in Chapter 2). In Chapter 4, we designed a novel method, called
\pace-adaptive metadynamics", in which the frequency of bias deposition is
adjusted at the course of simulations. By testing in a simple model system
and applying in a case of T4L binding/unbinding with two dierent ligands,
we showed that the pace adaptive scheme can improve the reliability
and accuracy of kinetics estimation, importantly without the need of extra
computational resources. So this strategy allows us to utilize the limited
computational resources in a more reasonable way. In Chapter 5, we further
illustrated the possibility to combine the free energy
ooding potential obtained from the variational method with infrequent metadynamics to calculate
the long timescale rate. This hybrid method was tested again in the
calculation of the unbinding time of T4L-benzene. The results suggest this
hybrid method can obtain similar results as infrequent metadynamics but
with less computational resources. Thus it is promising to apply this hybrid
method to calculate kinetics of escaping from a deep free energy well, e.g.
the drug residence time. In Chapter 6, we developed an atomistic hybrid
model by integration of physics-based and structure-based potentials in the
context of Monte Carlo software packages. We showed the ability of our
models to distinguish the folding mechanisms of four topologically similar
proteins.