Protein mechanical responses play a critical role in a wide variety of biological phenomena, impacting events as diverse as muscle contraction and stem cell differentiation. Recent advances in both experimental and computational techniques have provided the opportunity to explore protein constitutive properties at the molecular level. However, despite these advances many questions remain about how proteins respond to applied mechanical forces, particularly as a function of load magnitude. In order to address these questions, relatively simple helical structures were computationally tested to determine the mechanisms and kinetics of unfolding at a range of physiologically relevant load magnitudes. Atomically detailed constant force molecular dynamics simulations combined with the Milestoning kinetic analysis framework revealed that the mean first passage time (MFPT) of the initiation of unfolding of long (~16nm) isolated helical domains was a non-monotonic function of the magnitude of applied tensile load. The unfolding kinetics followed a profile ranging from 2.5ns (0pN) to a peak of 3.75ns (20pN) with a decreasing MFPT beyond 40pN reflected by an MFPT of 1ns for 100pN. The application of the Milestoning framework with a coarse-grained network analysis approach revealed that intermediate loads (15pN-25pN) retarded unfolding by opening additional, slower unfolding pathways through non-native [pi]-helical conformations. Analysis of coiled-coil helical pairs revealed that the presence of the second neighboring helix delayed unfolding initiation by a factor of 20, with calculated MFPTs ranging from 55ns (0pN) to 85ns (25pN per helix) to 20ns (100pN per helix). The stability of the coiled-coil domains relative to the isolated helix was shown to reflect a decreased propensity to break flexibility restraining intra-helix hydrogen bonds, thereby delaying [psi] backbone dihedral angle rotation and unfolding. These results show for the first time a statistically determined profile of unfolding kinetics for an atomically detailed protein that is non-monotonic with respect to load caused by a change in the unfolding mechanism with load. Together, the methods introduced for analyzing the mechanical response of proteins as well as the timescales determined for the initiation of unfolding provide a framework for the determination of the constitutive properties of proteins and non-biological polymers with more complicated geometries. / text

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It is well known that folded proteins in water are destabilized by the addition of urea. When a protein loses its ability to perform its biological activity due to a change in its structure, it is said to denaturate. The mechanism by which urea denatures proteins has been thoroughly studied in the past but no proposed mechanism has yet been widely accepted. The topic of this thesis is the study of the mechanism of urea-induced protein denaturation, by means of Molecular Dynamics (MD) computer simulations and Nuclear Magnetic Resonance (NMR) spectroscopy. Paper I takes a thermodynamic approach to the analysis of protein – urea solution MD simulations. It is shown that the protein – solvent interaction energies decrease significantly upon the addition of urea. This is the result of a decrease in the Lennard-Jones energies, which is the MD simulation equivalent to van der Waals interactions. This effect will favor the unfolded protein state due to its higher number of protein - solvent contacts. In Paper II, we show that a combination of NMR spin relaxation experiments and MD simulations can successfully be used to study urea in the protein solvation shell. The urea molecule was found to be dynamic, which indicates that no specific binding sites exist. In contrast to the thermodynamic approach in Paper I, in Paper III we utilize MD simulations to analyze the affect of urea on the kinetics of local processes in proteins. Urea is found to passively unfold proteins by decreasing the refolding rate of local parts of the protein that have unfolded by thermal fluctuations. Based upon the results of Paper I – III and previous studies in the field, I propose a mechanism in which urea denatures proteins mainly by an enthalpic driving force due to attractive van der Waals interactions. Urea interacts favorably with all the different parts of the protein. The greater solvent accessibility of the unfolded protein is ultimately the factor that causes unfolded protein structures to be favored in concentrated urea solutions.

abstract: The properties of materials depend heavily on the spatial distribution and connectivity of their constituent parts. This applies equally to materials such as diamond and glasses as it does to biomolecules that are the product of billions of years of evolution. In science, insight is often gained through simple models with characteristics that are the result of the few features that have purposely been retained. Common to all research within in this thesis is the use of network-based models to describe the properties of materials. This work begins with the description of a technique for decoupling boundary effects from intrinsic properties of nanomaterials that maps the atomic distribution of nanomaterials of diverse shape and size but common atomic geometry onto a universal curve. This is followed by an investigation of correlated density fluctuations in the large length scale limit in amorphous materials through the analysis of large continuous random network models. The difficulty of estimating this limit from finite models is overcome by the development of a technique that uses the variance in the number of atoms in finite subregions to perform the extrapolation to large length scales. The technique is applied to models of amorphous silicon and vitreous silica and compared with results from recent experiments. The latter part this work applies network-based models to biological systems. The first application models force-induced protein unfolding as crack propagation on a constraint network consisting of interactions such as hydrogen bonds that cross-link and stabilize a folded polypeptide chain. Unfolding pathways generated by the model are compared with molecular dynamics simulation and experiment for a diverse set of proteins, demonstrating that the model is able to capture not only native state behavior but also partially unfolded intermediates far from the native state. This study concludes with the extension of the latter model in the development of an efficient algorithm for predicting protein structure through the flexible fitting of atomic models to low-resolution cryo-electron microscopy data. By optimizing the fit to synthetic data through directed sampling and context-dependent constraint removal, predictions are made with accuracies within the expected variability of the native state. / Dissertation/Thesis / Ph.D. Physics 2011

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