Development and Application of a Novel Enhanced Sampling Method and Bayesian Analysis for Characterizing Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are a class of proteins with wide-ranging/nsignificance in signaling and disease that do not adopt a dominant folded structure as/nmonomers. Rather, the structures of IDPs in solution are best described as ensembles of/nconformational states that may range from being fully random coil to partially ordered./nThis structural plasticity of IDPs is theorized to facilitate regulation of their interaction/nwith other species, as in signal transduction or aggregation of IDPs into ordered fibrils./nCharacterizing the structural ensembles of IDPs in the free, solvated state is key to/nunderstanding the mechanisms of these interactions, and correspondingly the role an IDP/nspecies plays in signaling or disease./nThe rapid interconversion between conformational states, however, complicates the/nexperimental study of IDPs because most experimental signals report highly averaged/ninformation. Computational modeling with validation through comparison to experiment/nhas therefore been a main approach to characterizing IDP structure and dynamics. The/nfocus of my dissertation is on the development of new methods for computational study of/nIDPs, facilitating better and less expensive de novo generation of IDP structural ensembles/nand improving the metrics used to evaluate the degree of agreement between a simulated/nensemble and a set of experimental data./nDespite vast improvements in computational power and efficiency, molecular/ndynamics (MD) simulations of IDPs for generating conformational ensembles are still/nlimited by the expense of calculations. In Chapter 2 I present the development of a new/nenhanced sampling method – temperature cool walking (TCW) – and comparison of its/nperformance against a standard method – temperature replica exchange (TREx). The TCW/nmethod accelerates the rate of convergence to the equilibrium conformational ensemble/nwith increased sampling acceleration relative to TREx at greatly reduced computational/ncost./nThe second major limitation in MD is the accuracy of the force field. Most classical/nfixed charge force fields were parameterized using data from folded proteins, and have/nbeen thought to be biased to overly collapsed and structured conformations. This has/nmotivated the development of IDP-tailored force fields that sample greater disorder, at the/npotential expense of the ability to model stabilizing interactions between an IDP and its/nbinding partners. In Chapter 3, I assess to what degree the shortcomings assigned to/nstandard force fields may be due to insufficient sampling by characterizing the/nperformance of standard and newly modified force fields on the Alzheimer’s peptide/namyloid-β using both TREx and TCW. We find that with improved sampling, standard and/nmodified force fields produce similar structural ensembles, suggesting that both are/nappropriate for simulation of the disordered state. In Chapter 4 I present preliminary/nresults building off of this work by characterizing the performance of a polarizable force/nfield modeling a synthetic peptide that demonstrates complete loss of helical content with/nincreasing temperature. Inclusion of polarization effects has been thought to be key for/naccurate modeling of such multicomponent systems, especially when there is a shift in the/nelectrostatic environment as is the case for the unfolding peptide. Our early results, while/nlimited by current lack of convergence for tests using the polarizable force field and/nneeding further confirmation, match that expectation by finding early evidence of greater/nresponse to temperature by the polarizable force field than fixed charge comparators./nThe last work presented here is in the development of new methods for calculating/nthe degree of agreement between a simulated IDP ensemble and experimental data. Backcalculation/nof experimental data from structure can be very imprecise, motivating the/ndevelopment in Chapter 5 of scoring formalisms that account for variable uncertainties in/nboth back-calculation and experiment for diverse experimental data types. In summary, the/nmethods described in this dissertation seek to improve computational study of IDPs by/nfacilitating better, less expensive generation of IDP ensembles and producing more/ninformative metrics for evaluating their agreement with experiment.

Advisor: Head-Gordon, Teresa