Protein aggregation involves self-assembly of normally soluble proteins or peptides into supramolecular structures. This process is particularly important due to its involvement in several amyloid diseases, such as Parkinson's, Alzheimer's, and Type II diabetes. Several fibrillization mechanisms have been proposed, including a condensation-ordering mechanism where ordered fibril structures emerge from disordered oligomers and a dock-lock mechanism where a growing fibril induces attached polypeptides to organize individually into fibril-compatible conformations.

We present a series of computational studies using a coarse-grained peptide aggregate model that exhibits a rich diversity of structures: amorphous/disordered aggregates, beta-barrels, multi-layered fibrils, and aggregates of mixed type. Our model has a tunable backbone stiffness that governs the propensity to form fibrils in bulk solution. In this work, we investigate how this beta-sheet propensity couples with the properties of a surface template to influence the mechanism of aggregation. Here, we focus on peptide aggregation in the presence of three templates: a solid surface, the surface of a pre-existing aggregate seed, and a lipid bilayer.

Aggregation on solid hydrophilic or hydrophobic surfaces frequently occurs in many experimental setups. We find that the solid surface strongly biases toward the formation of fibrillar aggregates. Peptide-peptide interactions and surface attraction couple cooperatively on a solid surface to influence the binding/aggregation transition. Aggregation and binding occur almost simultaneously since the surface's crystal symmetry enforces a preferred direction of bound fibril growth, thus accelerating the process.

Seeding peptides with compatible aggregates removes the nucleation barrier for aggregation. We find that the aggregation mechanism is strongly dependent on the beta-sheet propensity of both the seed and bulk peptides. Additionally, bulk peptides that exhibit polymorphism can have multiple pathways to aggregation depending on which class of aggregate they initially form. We find that a fibrillar seed can induce amorphous-prone peptides into fibrillar structures via a condensation-ordering mechanism, thus sequestering potentially cytotoxic oligomers into a more inert form.

We simulate aggregation on lipid bilayers in an effort to approximate the complexity of the cellular milieu. While aggregation in vivo would occur in the presence of membrane surfaces, few simulation studies have been conducted on this combined system due to its computational complexity.

We have determined that a membrane surface, like a crystal surface, biases toward fibrillar aggregates. However, membrane undulations disturb multi-layered fibrils into non-planar beta-sheet structures, such as beta-barrels. The presence of fibrils on the membrane also affects its fluid properties, creating a hexagonally packed lipid ordering underneath the fibrils, locally increasing its bending modulus and aligning lipid tilt to the orientation of the peptides. Thus peptide aggregation and membrane fluidity affect each other's structure and dynamics.

The key general features of a surface that control its modulation of peptide aggregation are its structural order and fluidity. An ordered, rigid template biases more strongly toward fibrillar structures and restricts the set of aggregation pathways and morphologies. The dynamic nature of a fluid surface biases less toward fibrils and enhances the range of aggregation dynamics.

Modeling of biochemical polymers using classical simulation and the analysis of large datasets has become important in an age when high-throughput experimentation and advanced computational resources have enabled the collection of massive bodies of information regarding these systems. Models help us understand and extract insight from experimental data, allowing us to develop simulations and predictions around their behavior that can help elucidate macroscopic behaviors of interest. The body of work applies techniques in molecular dynamics, machine intelligence and information theory to problems within the realm of biological heteropolymers with the intent of developing understanding regarding modern methods of data generation and analysis. The differing paradigms of first-principles models versus data first models are considered and examined, and work is done to show areas where the methods are complementary or applicable to helping understand experimental data sets.

Chemical reactions, mass transport in solids, protein folding, and nucleation in first-order phase transitions are examples of processes characterized by multiple, long-lived states. Transitions from one (meta)stable state to another are rare and brief, making them difficult to resolve experimentally. And yet the short transition paths contain valuable structural and dynamic information that governs the lifetimes of the stable states. Transition state theory provides a valuable framework for analyzing rare events, provided that an exact dividing surface with no-recrossing can be found. Direct simulation of rare events processes are complicated by the long waiting times for a transition to spontaneously occur. Simulation methods that introduce a bias decrease the waiting time but also risk altering the mechanism. The reactive flux correlation function provides a two-step recipe for computing rates from simulation using an approximate dividing surface, but may miss important details about the physical reaction mechanism. Transition path sampling (TPS) was developed specifically to sample unbiased dynamical reactive trajectories and in combination with likelihood maximization provides an optimized reaction coordinate model.

We present new, simple TPS methods that reduce the computational expense of simulating rare events over existing methods. We apply these methods to study rare events in condensed phases and analyze the resulting data with likelihood maximization. The mechanism for vacancy migration in a single domain crystal by activated hops is examined. We find that accurately locating the donor and acceptor sites has a dramatic effect on identifying the mechanism. We next investigate the role of water in ion-pair dissociation, uncovering two solvent mechanisms that influence ion-pair transition states. The resulting dividing surface does not eliminate recrossing, so we present a test for the existence of a no-recrossing surface. It is revealed that an exact dividing surface does not exist for ion-pair dissociation. We discuss the ramifications for transition state theory, Grote-Hynes theory and the relationship between them.

There is currently a need for improved adhesives for medical and marine applications, primarily because the presence of water severely undermines adhesion. Due to their robust adhesion under water, marine mussels have been widely studied as inspirations for the design of wet adhesives. However, development of mussel-inspired materials has historically overemphasized the importance of the catechol functionality in mussel adhesion. This thesis demonstrates that the mechanical properties of mussel-inspired materials, including adhesion, cohesion, and stiffness, result from a range of factors beyond the presence of catechols. By investigating model systems spanning multiple length scales, this work reveals the importance of failure mode, binding group density, and electrostatic interactions on interfacial adhesion of mussel-inspired surface primers and peptides at the atomic scale. Interactions between thin films of pressure sensitive adhesives and polymers are shown to depend on film composition which is influenced by solvent-induced structural rearrangement and contact-induced damage. The mechanical properties of micro-scale hydrogel films are shown to be dictated by fluid flow through the polymer network. By providing a better understanding of mussel adhesion, this work seeks to guide the design of new mussel-inspired materials for diverse applications.