Diverse fiber-forming proteins are found in nature that accomplish a wide range of functions including signaling, cell adhesion, and mechanical support. Unique sequence characteristics of these proteins often lead to their specialized roles. However, these proteins also share a common organizational hierarchy in primary and secondary structures that strongly influence both their intramolecular folding and intermolecular interactions. Based on what is known regarding protein fiber assembly of silk peptides, shear-induced elongation of the molecular strands drives interchain secondary structure crystallization via anisotropic alignment, which creates a molecular superstructure that forms the basis a fiber network. In this work, the hypothesis is this type of protein fiber assembly is not unique to silk sequences and that other proteins can be spun into fibers in similar fashion while maintaining unique functionality given by their specialized amino acid sequences such as RGD, GX1X2, and so forth. This was investigated by modeling the manner in which hydrophobic and hydrophilic blocks of amino acids create interacting secondary structures at the chain level when exposed to shear. It was determined computationally and then verified experimentally that fiber spinning success is most likely to occur after shear processing if the protein sequence exhibits a balance of hydrophobic and hydrophilic content and has sufficient length. Applied to the biological scale, both pure and mixed solutions of proteins such as fibronectin, laminin, and silk fibroin were spun into fibers. In particular, alloy protein fibers of silk fibroin mixed with fibronectin exhibited the characteristic mechanical integrity of silk and the bioactivity of fibronectin. This simple method of creating protein fibers with hybrid characteristics is significantly faster, less expensive, and less technically intensive than chimeric protein production, which purports to do the same. This finding also provides insight into a fundamental means by which protein fibers may be assembled in vivo by taking advantage of the thermodynamically favorable assembly of peptide sequences at the chain level under proper molecular orientation. Taken together, a high throughput means of producing a wide-range of pure and hybrid protein fibers has been developed for various biological applications and research investigations into the fibrous elements of biology.

The present work addresses issues related to the derivation of reduced models of atomistic systems, their statistical calibration, and their relation to atomistic models of materials. The reduced model, known in the chemical physics community as a coarse-grained model, is calibrated within a Bayesian framework. Particular attention is given to developing likelihood functions, assigning priors on coarse-grained model parameters, and using data from molecular dynamics representations of atomistic systems to calibrate coarse-grained models such that certain physically relevant atomistic observables are accurately reproduced. The developed Bayesian framework is then applied in three case studies of increasing complexity and practical application. A freely jointed chain model is considered first for illustrative purposes. The next example entails the construction of a coarse-grained model for a liquid heptane system, with the explicit design goal of accurately predicting a vapor-liquid transfer free energy. Finally, a coarse-grained model is developed for an alkylthiophene polymer that has been shown to have practical use in certain types of photovoltaic cells. The development therein employs Bayesian decision theory to select an optimal CG potential energy function. Subsequently, this model is subjected to validation tests in a prediction scenario that is relevant to the performance of a polyalkylthiophene-based solar cell. / text

No description available.