Development and Application of a Novel Enhanced Sampling Method and Bayesian Analysis for Characterizing Intrinsically Disordered Proteins
- Author(s): Lincoff, James A
- Advisor(s): Head-Gordon, Teresa
- et al.
Intrinsically disordered proteins (IDPs) are a class of proteins with wide-ranging
significance in signaling and disease that do not adopt a dominant folded structure as
monomers. Rather, the structures of IDPs in solution are best described as ensembles of
conformational states that may range from being fully random coil to partially ordered.
This structural plasticity of IDPs is theorized to facilitate regulation of their interaction
with other species, as in signal transduction or aggregation of IDPs into ordered fibrils.
Characterizing the structural ensembles of IDPs in the free, solvated state is key to
understanding the mechanisms of these interactions, and correspondingly the role an IDP
species plays in signaling or disease.
The rapid interconversion between conformational states, however, complicates the
experimental study of IDPs because most experimental signals report highly averaged
information. Computational modeling with validation through comparison to experiment
has therefore been a main approach to characterizing IDP structure and dynamics. The
focus of my dissertation is on the development of new methods for computational study of
IDPs, facilitating better and less expensive de novo generation of IDP structural ensembles
and improving the metrics used to evaluate the degree of agreement between a simulated
ensemble and a set of experimental data.
Despite vast improvements in computational power and efficiency, molecular
dynamics (MD) simulations of IDPs for generating conformational ensembles are still
limited by the expense of calculations. In Chapter 2 I present the development of a new
enhanced sampling method – temperature cool walking (TCW) – and comparison of its
performance against a standard method – temperature replica exchange (TREx). The TCW
method accelerates the rate of convergence to the equilibrium conformational ensemble
with increased sampling acceleration relative to TREx at greatly reduced computational
The second major limitation in MD is the accuracy of the force field. Most classical
fixed charge force fields were parameterized using data from folded proteins, and have
been thought to be biased to overly collapsed and structured conformations. This has
motivated the development of IDP-tailored force fields that sample greater disorder, at the
potential expense of the ability to model stabilizing interactions between an IDP and its
binding partners. In Chapter 3, I assess to what degree the shortcomings assigned to
standard force fields may be due to insufficient sampling by characterizing the
performance of standard and newly modified force fields on the Alzheimer’s peptide
amyloid-β using both TREx and TCW. We find that with improved sampling, standard and
modified force fields produce similar structural ensembles, suggesting that both are
appropriate for simulation of the disordered state. In Chapter 4 I present preliminary
results building off of this work by characterizing the performance of a polarizable force
field modeling a synthetic peptide that demonstrates complete loss of helical content with
increasing temperature. Inclusion of polarization effects has been thought to be key for
accurate modeling of such multicomponent systems, especially when there is a shift in the
electrostatic environment as is the case for the unfolding peptide. Our early results, while
limited by current lack of convergence for tests using the polarizable force field and
needing further confirmation, match that expectation by finding early evidence of greater
response to temperature by the polarizable force field than fixed charge comparators.
The last work presented here is in the development of new methods for calculating
the degree of agreement between a simulated IDP ensemble and experimental data. Backcalculation
of experimental data from structure can be very imprecise, motivating the
development in Chapter 5 of scoring formalisms that account for variable uncertainties in
both back-calculation and experiment for diverse experimental data types. In summary, the
methods described in this dissertation seek to improve computational study of IDPs by
facilitating better, less expensive generation of IDP ensembles and producing more
informative metrics for evaluating their agreement with experiment.