UC Berkeley

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

cost.

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.