Proteins, the machinery of life, perform a vast array of essential biochemical functions, evolving over time to acquire diverse roles within biological systems. This evolution, primarily driven by mutations within protein sequences, can profoundly impact protein function, potentially leading to various diseases. This thesis aims to dissect the intricate mechanisms through which genetic mutations influence protein functionality, focusing on the dynamic alterations induced by single and combined mutations. Employing a suite of computational tools, including molecular dynamics (MD) simulations and proven analysis metrics like the Dynamic Flexibility Index (DFI) and Dynamic Coupling Index (DCI), I analyze protein dynamics to uncover the common dynamic effects associated with disease causation and compensatory mechanisms. This analysis extends to exploring the concept of epistasis through the lens of protein dynamics, showing how combinations of mutations interact within the protein's 3D structure to either exacerbate or mitigate the functional impacts of individual mutations. The use of EpiScore, a computational tool designed to quantify the epistatic effects of mutations, provides insight on the combined dynamic effects two mutations might have. This is particularly evident in the analysis of rare alleles within human populations, where certain allele combinations, despite their individual rarity, frequently co-occur, suggesting a mechanism of dynamic compensation. This phenomenon is further investigated in the context of the SARS-CoV-2 spike protein, providing insights into viral evolution and the adaptive significance of specific mutations. Additionally, I delve into the role of Intrinsically Disordered Regions (IDRs) in protein function and mutation compensation, highlighting the need for sophisticated dynamics analysis tools to capture the full spectrum of mutation effects. By integrating these analyses, this thesis unveils a complex picture of how proteins' dynamic properties, shaped by mutations, underpin their functional evolution and disease outcomes.

Transportation of material across a cell membrane is a vital process for maintaininghomeostasis. Na+/H+ antiporters, for instance, help maintain cell volume and regulate intracellular sodium and proton concentrations. They are prime drug targets, since dysfunction of these crucial proteins in humans is linked to heart and neurodegenerative diseases. Due to their placement in a cell membrane, their study is particularly difficult compared to globular proteins, which is likely the reason the transport mechanisms for these proteins are not entirely known. This work focuses on the electrogenic bacterial homologs Thermus thermophilus NapA (TtNapA) and Echerichia coli NhaA (EcNhaA), each transporting one sodium from the interior of the cell for two protons on outside of the cell. Even though X-ray crystal structures for both of these systems have been resolved, their study through molecular dynamics (MD) simulations is limited. The dynamic protonation and deprotonation of the binding site residues is a fundamental process in the transport cycle, which currently cannot be explored intuitively with standard MD methodologies. Apart from this limitation, simulation performance is only a fraction of what is needed to understand the full transport process, particularly when it comes to global conformational changes. This work seeks to overcome these limitations through the development and application of a multiscale thermodynamic and kinetic framework for constructing models capable of predicting experimental observables, such as the dependence of transporter turnover on membrane voltage. These models allow interpretation of the effects of individual processes on the function as a whole. This procedure is demonstrated for TtNapA and the connection between structure and function is shown by computing cycle turnover across a range of non-equilibrium conditions.

Traditionally, allostery is perceived as the response of a catalytic pocket to perturbations induced by binding at another distal site through the interaction network in a protein, usually associated with a conformational change responsible for functional regulation. Here, I utilize dynamics-based metrics, Dynamic Flexibility Index and Dynamic Coupling Index to provide insight into how 3D network of interactions wire communications within a protein and give rise to the long-range dynamic coupling, thus regulating key allosteric interactions. Furthermore, I investigate its role in modulating protein function through mutations in evolution. I use Thioredoxin and β-lactamase enzymes as model systems, and show that nature exploits "hinge-shift'' mechanism, where the loss in rigidity of certain residue positions of a protein is compensated by reduced flexibility of other positions, for functional evolution. I also developed a novel approach based on this principle to computationally engineer new mutants of the promiscuous ancestral β-lactamase (i.e., degrading both penicillin and cephatoxime) to exhibit specificity only towards penicillin with a better catalytic efficiency through population shift in its native ensemble.I investigate how allosteric interactions in a protein can regulate protein interactions in a cell, particularly focusing on E. coli ribosome. I describe how mutations in a ribosome can allosterically change its associating with magnesium ions, which was further shown by my collaborators to distally impact the number of biologically active Adenosine Triphosphate molecules in a cell, thereby, impacting cell growth. This allosteric modulation via magnesium ion concentrations is coined, "ionic allostery''. I also describe, the role played by allosteric interactions to regulate information among proteins using a simplistic toy model of an allosteric enzyme. It shows how allostery can provide a mechanism to efficiently transmit information in a signaling pathway in a cell while up/down regulating an enzyme’s activity.
The results discussed here suggest a deeper embedding of the role of allosteric interactions in a protein’s function at cellular level. Therefore, bridging the molecular impact of allosteric regulation with its role in communication in cellular signaling can provide further mechanistic insights of cellular function and disease development, and allow design of novel drugs regulating cellular functions.

Typical eukaryotic organelles use membranes formed by lipid bilayers in order to compartmentalize their functions within the cell. However, cells also contain membraneless organelles formed by intrinsically disordered proteins (IDPs) via liquid-liquid phase separation. The organelles form localized compartments that separate their contents from the environment.1 Here, this mechanism is used to generate artificial membraneless organelles that comprise a chemical reaction. An IDP, DEAD-box helicase (Ddx4), was bioconjugated to an enzyme, horseradish peroxidase (HRP), through the use of a bifunctional chemical linker, succinimidyl 4-(N-maleimidomethyl)cyclohexane-1-carboxylate (SMCC), in order to examine if the enzyme could be incorporated in droplets and whether its activity would be affected. The conjugation of HRP-SMCC (43.4 kDa) to Ddx4 (25.6 kDa) was successful: SDS-PAGE analysis confirmed the presence of a product that was within the range of a full conjugate.

My research centers on the design and fabrication of biomolecule-sensing devices that combine top-down and bottom-up fabrication processes and leverage the unique advantages of each approach. This allows for the scalable creation of devices with critical dimensions and surface properties that are tailored to target molecules at the nanoscale.

My first project focuses on a new strategy for preparing solid-state nanopore sensors for DNA sequencing. Challenges for existing nanopore approaches include specificity of detection, controllability of translocation, and scalability of fabrication. In a new solid-state pore architecture, top-down fabrication of an initial electrode gap embedded in a sealed nanochannel is followed by feedback-controlled electrochemical deposition of metal to shrink the gap and define the nanopore size. The resulting structure allows for the use of an electric field to control the motion of DNA through the pore and the direct detection of a tunnel current through a DNA molecule.

My second project focuses on top-down fabrication strategies for a fixed nanogap device to explore the electronic conductance of proteins. Here, a metal-insulator-metal junction can be fabricated with top-down fabrication techniques, and the subsequent electrode surfaces can be chemically modified with molecules that bind strongly to a target protein. When proteins bind to molecules on either side of the dielectric gap, a molecular junction is formed with observed conductances on the order of nanosiemens. These devices can be used in applications such as DNA sequencing or to gain insight into fundamental questions such as the mechanism of electron transport in proteins.

In a typical living cell, millions to billions of proteins—nanomachines that fluctuate and cycle among many conformational states—convert available free energy into mechanochemical work. A fundamental goal of biophysics is to ascertain how 3D protein structures encode specific functions, such as catalyzing chemical reactions or transporting nutrients into a cell. Protein dynamics span femtosecond timescales (i.e., covalent bond oscillations) to large conformational transition timescales in, and beyond, the millisecond regime (e.g., glucose transport across a phospholipid bilayer). Actual transition events are fast but rare, occurring orders of magnitude faster than typical metastable equilibrium waiting times. Equilibrium molecular dynamics (EqMD) can capture atomistic detail and solute-solvent interactions, but even microseconds of sampling attainable nowadays still falls orders of magnitude short of transition timescales, especially for large systems, rendering observations of such "rare events" difficult or effectively impossible.

Advanced path-sampling methods exploit reduced physical models or biasing to produce plausible transitions while balancing accuracy and efficiency, but quantifying their accuracy relative to other numerical and experimental data has been challenging. Indeed, new horizons in elucidating protein function necessitate that present methodologies be revised to more seamlessly and quantitatively integrate a spectrum of methods, both numerical and experimental. In this dissertation, experimental and computational methods are put into perspective using the enzyme adenylate kinase (AdK) as an illustrative example. We introduce Path Similarity Analysis (PSA)—an integrative computational framework developed to quantify transition path similarity. PSA not only reliably distinguished AdK transitions by the originating method, but also traced pathway differences between two methods back to charge-charge interactions (neglected by the stereochemical model, but not the all-atom force field) in several conserved salt bridges. Cryo-electron microscopy maps of the transporter Bor1p are directly incorporated into EqMD simulations using MD flexible fitting to produce viable structural models and infer a plausible transport mechanism. Conforming to the theme of integration, a short compendium of an exploratory project—developing a hybrid atomistic-continuum method—is presented, including initial results and a novel fluctuating hydrodynamics model and corresponding numerical code.

Fluorescence spectroscopy is a powerful tool for biophysical studies due to its high sensitivity and broad availability. It is possible to detect fluorescence from single molecules allowing researchers to see the behavior of subpopulations whose presence is obscured by “bulk” collection methods. The fluorescent probes used in these experiments are affected by the solution and macromolecular environments they are in. A misunderstanding of a probe’s photophysics can lead researchers to assign observed behavior to biomolecules, when in fact the probe is responsible. On the other hand, a probe’s photophysical behavior is a signature of the environment surrounding it; it can be exploited to learn about the biomolecule(s) under study. A thorough examination of a probe’s photophysics is critical to data interpretation in both cases and is the focus of this work. This dissertation investigates the photophysical behavior of symmetric and asymmetric cyanines in a variety of solution and biomolecular environments. Using fluorescent techniques—such as time-correlated single photon counting (TCSPC) and fluorescence correlation spectroscopy (FCS)—it was found that cyanines are influenced by the local environment. In the first project, the symmetric cyanines are found to be susceptible to paramagnetic species, such as manganese(II), that enhance the intersystem crossing (ISC) rate increasing triplet blinking and accelerating photobleaching. Another project found the increase in fluorescence of Cy3 in the protein induced fluorescence enhancement (PIFE) technique is due to reduced photoisomerization caused by the proximity of protein to Cy3. The third project focused on asymmetric cyanines; their photophysical behavior has not been previously characterized. Dy630 as a free dye behaves like Cy3; it has a short lifetime and can deactivate via photoisomerization. Preliminary experiments on Dy dyes conjugated to DNA show these dyes do not photoisomerize, and do not show PIFE potential. Further research will explore other conjugation strategies, with the goal of optimizing conditions in which Dy630 can be used as the red-absorbing analogue of Cy3 for PIFE applications. In summary, this dissertation focused on photophysical investigations, the understanding of which forms the backbone of rigorous fluorescent studies and is vital to the development of the fluorescence field.

Proteins are essential for most biological processes that constitute life. The function of a protein is encoded within its 3D folded structure, which is determined by its sequence of amino acids. A variation of a single nucleotide in the DNA during transcription (nSNV) can alter the amino acid sequence (i.e., a mutation in the protein sequence), which can adversely impact protein function and sometimes cause disease. These mutations are the most prevalent form of variations in humans, and each individual genome harbors tens of thousands of nSNVs that can be benign (neutral) or lead to disease. The primary way to assess the impact of nSNVs on function is through evolutionary approaches based on positional amino acid conservation. These approaches are largely inadequate in the regime where positions evolve at a fast rate. We developed a method called dynamic flexibility index (DFI) that measures site-specific conformational dynamics of a protein, which is paramount in exploring mechanisms of the impact of nSNVs on function. In this thesis, we demonstrate that DFI can distinguish the disease-associated and neutral nSNVs, particularly for fast evolving positions where evolutionary approaches lack predictive power. We also describe an additional dynamics-based metric, dynamic coupling index (DCI), which measures the dynamic allosteric residue coupling of distal sites on the protein with the functionally critical (i.e., active) sites. Through DCI, we analyzed 200 disease mutations of a specific enzyme called GCase, and a proteome-wide analysis of 75 human enzymes containing 323 neutral and 362 disease mutations. In both cases we observed that sites with high dynamic allosteric residue coupling with the functional sites (i.e., DARC spots) have an increased susceptibility to harboring disease nSNVs. Overall, our comprehensive proteome-wide analysis suggests that incorporating these novel position-specific conformational dynamics based metrics into genomics can complement current approaches to increase the accuracy of diagnosing disease nSNVs. Furthermore, they provide mechanistic insights about disease development. Lastly, we introduce a new, purely sequence-based model that can estimate the dynamics profile of a protein by only utilizing coevolution information, eliminating the requirement of the 3D structure for determining dynamics.

Calcitonin Gene-Related Peptide (CGRP) is an intrinsically disordered protein

that has no regular secondary structure, but plays an important role in vasodilation and pain transmission in migraine. Little is known about the structure and dynamics of the monomeric state of CGRP or how CGRP is able to function in the cell, despite the lack of regular secondary structure. This work focuses characterizing the non-local structural and dynamical properties of the CGRP monomer in solution, and understanding how these are affected by the sequence and the solution environment. The unbound, free state of CGRP is measured using a nanosecond laser-pump spectrophotometer, which allows measuring the end-to-end distance (a non-local structural property) and the rate of end-to-end contact formation (intra-chain diffusional dynamics). The data presented in this work show that electrostatic interactions strongly modulate the structure of CGRP, and that peptide-solvent interactions are sequence and charge dependent and can have a significant effect on the internal dynamics of the peptide. In the last few years migraine research has shifted focus to disrupting the CGRP-receptor pathway through the design of pharmacological drugs that bind to either CGRP or its receptor, inhibiting receptor activation and therefore preventing or reducing the frequency of migraine attacks. Understanding what types of intra- and inter-chain interactions dominate in CGRP can help better design drugs that disrupt the binding of CGRP to its receptor.