Uncovering the Impact of Conformational Dynamics and Allostery on Genetic Diseases, Epistasis, and Evolution

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Description
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…

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.
Date Created
2024
Agent

Analyzing Molecular Interactions of Membrane Proteins by Computational Methods

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Description
Protein interactions with the environment are crucial for proper function, butinteraction mechanisms are not always understood. In G protein-coupled receptors (GPCRs), cholesterol modulates the function in some, but not all, GPCRs. Coarse grained molecular dynamics was used to determine a set of…

Protein interactions with the environment are crucial for proper function, butinteraction mechanisms are not always understood. In G protein-coupled receptors (GPCRs), cholesterol modulates the function in some, but not all, GPCRs. Coarse grained molecular dynamics was used to determine a set of contact events for each residue and fit to a biexponential to determine the time scale of the long contacts observed in simulation. Several residues of interest were indicated in CCK1 R near Y140, which is known to render CCK1 R insensitive to cholesterol when mutated to alanine. A difference in the overall residence time between CCK1 R and its cholesterol insensitive homologue CCK2 R was also observed, indicating the ability to predict relative cholesterol binding for homologous proteins. Occasionally large errors and poor fits to the data were observed, so several improvements were made, including generalizing the model to include K exponential components. The sets of residence times in the improved method were analyzed using Bayesian nonparametrics, which allowed for error estimations and the classification of contact events to the individual components. Ten residues in three GPCRs bound to cholesterol in experimental structures had large tau. Slightly longer overall interaction time for the cholesterol sensitive CB1 R over its insensitive homologue CB2 R was also observed. The interactions between the cystic fibrosis transmembrane conductance regulator (CFTR) and GlyH-101, an open-channel blocker, were analyzed using molecular dynamics. The results showed the bromine in GlyH-101 was in constant contact with F337, which is just inside the extracellular gate. The simulations also showed an insertion of GlyH-101 between TM1 and TM6 deeper than the starting binding pose. Once inserted deeper between TMs 1 and 6, the number of persistent contacts also increased. This proposed binding pose may help in future investigations of CFTR and help determine an open-channel structure for the protein, which in turn may help in the development of treatments for various medical conditions. Overall, the use of molecular dynamics and state of the art analysis tools can be useful in the study of membrane proteins and eventuallyin the development of treatments for ailments stemming from their atypical function.
Date Created
2022
Agent

Deciphering Sequence to Function through Protein Dynamics

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Description
This thesis explores a diverse array of topics related to the role of dynamic allostery in regulating protein functions. Allostery is the phenomenon where a catalytic pocket responds to perturbations caused by binding at another distant site. This response often…

This thesis explores a diverse array of topics related to the role of dynamic allostery in regulating protein functions. Allostery is the phenomenon where a catalytic pocket responds to perturbations caused by binding at another distant site. This response often involves a conformational change resulting in a protein function alteration. However, it is essential to note the existence of dynamic allostery mechanisms that regulate protein function without relying on conformational changes but on dynamic motions. Within this thesis, position-specific equilibrium dynamics-based metrics like Dynamic Flexibility Index and Dynamic Coupling Index are employed to quantify the contributions of specific residues to protein dynamics. I investigated the role of dynamics in protein binding of the WW domain. In particular, I focused on how the mutations of distal positions modulate the binding site dynamics. By employing Dynamic Flexibility Index, I discovered that a residue, 10T, located distally from the binding pocket, plays a significant role in the observed dynamics difference between two variants: N21 (a native folded WW domain not binding Group I peptide) and CC16_N21 (an artificial WW domain binding Group I peptide). The T10H variant, created by exchanging the position 10 residue, enhances flexibility at positions 10 and 16. Consequently, this modification has led to an enhancement in the binding function of N21, enabling it to bind to Group I peptide effectively. Moreover, I investigated the influence of dynamic allostery on protein binding specificity, specifically in the PDZ domain PSD95. To gain insights into the binding process and accurately measure binding affinity, I employed two parallel computational approaches: Adaptive BP-docking and Steered Molecular Dynamics. These methods allowed me to model the binding interactions and quantify the binding strength robustly and comprehensively. The significance of allostery can serve as foundational knowledge in Deep Learning models, enabling the efficient mapping of protein sequences to their corresponding functionalities. One particular metric, Dynamic Coupling Index asymmetry, offers valuable insights into how the three-dimensional network of interactions facilitates communication within a protein structure. Leveraging these interactions, I developed a deep neural network architecture demonstrating enhanced capability in capturing epistatic interactions within Beta-lactamase and protein G function.
Date Created
2023
Agent

Investigation of Synthesis of Beta-Cyclodextrin into an Anti-Viral Platform

Description
Cyclodextrins are known for their pharmaceutical applications in a range of pathologies. Beta(ꞵ)-cyclodextrins have been suggested to be effective scaffolds that can ligate to peptides when chemically modified, which has the potential to be cost-effective in comparison to other…

Cyclodextrins are known for their pharmaceutical applications in a range of pathologies. Beta(ꞵ)-cyclodextrins have been suggested to be effective scaffolds that can ligate to peptides when chemically modified, which has the potential to be cost-effective in comparison to other available treatments for antiviral therapeutics. It is hypothesized that a ꞵ-cyclodextrin platform can be modified through a few-step reaction process to develop a ꞵ-cyclodextrin-DBCO-GFP nanobody. The findings of this few-step reaction support the general approach of conjugating the ꞵ-cyclodextrin derivative to GPF nanobody for developing a cyclodextrin antiviral scaffold.
Date Created
2023-05
Agent

Optimizing the stability of a DNA origami snub cube inhibitor

Description

With climate change threatening to increase the frequency of global pandemics, the need for quick and adaptable responses to novel viruses will become paramount. DNA nanotechnology offers a highly customizable, biocompatible approach to combating novel outbreaks. For any DNA nanotechnology-based…

With climate change threatening to increase the frequency of global pandemics, the need for quick and adaptable responses to novel viruses will become paramount. DNA nanotechnology offers a highly customizable, biocompatible approach to combating novel outbreaks. For any DNA nanotechnology-based therapeutic to have future success in vivo, the structure must be able to withstand serological conditions for an extended time period. In this study, the stability of a wireframe DNA snub cube with attached nbGFP used to bind a nonessential viral epitope on Pseudorabies virus is evaluated in vitro both with and without one of two modifications designed to enhance stability: 1) the use of trivalent spermidine cations during thermal annealing of the nanostructure, and 2) the introduction of a polylysine-polyethylene glycol coating to the conjugated nanostructure. The design, synthesis, and purification of the multivalent inhibitor were also evaluated and optimized. Without modification, the snub cube nanostructure was stable for up to 8 hours in culture media supplemented with 10% FBS. The spermidine-annealed nanostructures demonstrated lesser degrees of stability and greater degradation than the unmodified structures, whereas the polylysine-coated structures demonstrated equivalent stability at lower valencies and enhanced stability at the highest valency of the snub cube inhibitor. These results support the potential for the polylysine-polyethylene glycol coating as a potential method for enhancing the stability of the snub cube for future in vivo applications.

Date Created
2023-05
Agent

2D and 3D DNA Origami Encryption Optimization Utilizing DNA-PAINT

Description

A form of nanoscale steganography exists described as DNA origami cryptography which is a technique of secure information encryption through scaffold, staple, and varying docking strand self- assembling mixtures. The all-DNA steganography based origami was imaged through high-speed DNA-PAINT super-resolution…

A form of nanoscale steganography exists described as DNA origami cryptography which is a technique of secure information encryption through scaffold, staple, and varying docking strand self- assembling mixtures. The all-DNA steganography based origami was imaged through high-speed DNA-PAINT super-resolution imaging which uses periodic docking sequences to eliminate the need for protein binding. The purpose of this research was to improve upon the DNA origami cryptography protocol by encrypting information in 2D Rothemund Rectangular DNA Origami (RRO) and 3D cuboctahedron DNA origami as a platform of self-assembling DNA nanostructures to increase the routing possibilities of the scaffold. The initial focus of the work was increasing the incorporation efficiency of all individual docking spots for full 20nm grid RRO pattern readout. Due to this procedural optimization was pursued by altering annealing cycle length, centrifugal spin rates for purification, and lengthening docking strands vs. imager poly T linkers. A 14nm grid was explored as an intermediate prior to the 10nm grid for comparison of optimized experimental procedure for a higher density encryption pattern option. Imager concentration was discovered to be a vital determining factor in effectively resolving the 10nm grids due to high concentrations of imager strands inducing simultaneous blinking of adjacent docking strands to be more likely causing the 10nm grids to not be resolved. A 2 redundancy and 3 redundancy encryption scheme was developed for the 10nm grid RRO to be encrypted with. Further experimentation was completed to resolve full 10nm DNA-origami grids and encrypt with the message ”ASU”. The message was successfully encrypted and resolved through the high density 10nm grid with 2 and 3 redundancy patterns. A cuboctahedron 3D origami was explored with DNA-PAINT techniques as well resulting in successful resolution of the z-axis through variation of biotin linker length and calibration file. Positive results for short message ”0407” encryption of the cuboctahedron were achieved. Data encryption in DNA origami is further being explored and could be an optimal solution for higher density data storage with greater longevity of media.

Date Created
2023-05
Agent

Phage-Enabled Nanotechnology and Novel Sensing Architectures in Ribodevices

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Description
Two distinct aspects of synthetic biology were investigated: the development of viral structures for new methods of studying self-assembly and nanomanufacturing, and the designs of genetic controls systems based on controlling the secondary structure of nucleic acids. Viral structures have…

Two distinct aspects of synthetic biology were investigated: the development of viral structures for new methods of studying self-assembly and nanomanufacturing, and the designs of genetic controls systems based on controlling the secondary structure of nucleic acids. Viral structures have been demonstrated as building blocks for molecular self-assembly of diverse structures, but the ease with which viral genomes can be modified to create specific structures depends on the mechanisms by which the viral coat proteins self-assemble. The experiments conducted demonstrate how the mechanisms that guide bacteriophage lambda’s self-assembly make it a useful and flexible platform for further research into biologically enabled self-assembly. While the viral platform investigations focus on the creation of new structures, the genetic control systems research focuses on new methods for signal interpretation in biological systems. Regulators of genetic activity that operate based on the secondary structure formation of ribonucleic acid (RNA), also known as riboswitches, are genetically compact devices for controlling protein translation. The toehold switch ribodevice can be modified to enable multiplexed logical operations with RNA inputs, requiring no additional protein transcription factors to regulate activity, but they cannot receive chemical inputs. RNA sequences generated to bind to specific chemicals, known as aptamers, can be used in riboswitches to confer genetic activity upon binding their target chemical. But attempts to use aptamers for logical operations and genetic circuits are difficult to generalize due to differences in sequence and binding strength. The experiments conducted demonstrate a ribodevice structure in which aptamers can be used semi-interchangeably to translate chemical inputs into the toehold switch paradigm, marrying the programmability and orthogonality of toehold switches with the broad sensing potential of aptamer-based ribodevices.
Date Created
2022
Agent

Determination of Propulsion Matrix from Microscale Brownian Motion

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Description
The propulsion matrix provides a compact description of the locomotion of a single flagella molecular motor in a low Reynolds number environment. The locomotion properties of individual flagellar motors are central to bacterial behavior, including chemotaxis, pathogenesis, and biofilm formation.…

The propulsion matrix provides a compact description of the locomotion of a single flagella molecular motor in a low Reynolds number environment. The locomotion properties of individual flagellar motors are central to bacterial behavior, including chemotaxis, pathogenesis, and biofilm formation. However, because conventional hydrodynamic measurement approaches require applied forces, torques, or fluid flows, it is not possible to directly measure the propulsion matrix for an individual microscale helical filament. Here, the limitations inherent to conventional measurement approaches are overcome using a combination of theoretical, experimental, and computational advancements. First, the relationship between the elements of the propulsion matrix with translational and rotational Brownian motion is derived using the fluctuation-dissipation theorem. Next, a volumetric fluorescent imaging using high resolution oblique plane microscopy with sufficient spatio-temporal resolution is conducted to resolve both translation and rotation of individual helical filaments isolated from E.coli's flagellar motor. Finally, a computational framework is developed to track individual helical filaments across six degrees of freedom, extract diffusion coefficients, and quantify the temporal correlation between translation and rotation. This study computed the maximum propulsion efficiency to be around 1.7%. Direct measurement of propulsion efficiency generally agrees with the ensemble and large-scale measurements previously performed using conventional hydrodynamic measurements. The findings suggest that the approach described here can be extended to more complex in-vitro experiments that evaluate microscale molecular motors. For example, evaluating sperm motility without inducing chemotaxis or utilizing a microfluidic setup.
Date Created
2022
Agent

Computational Analysis & Design of Biopolymers

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Description
Biopolymers perform the majority of essential functions necessary for life. From a small amount of components emerges considerable complexity in both structure and function. The separated timescales of dynamic processes and intricate intra- and inter-molecular interactions of these molecules necessitate…

Biopolymers perform the majority of essential functions necessary for life. From a small amount of components emerges considerable complexity in both structure and function. The separated timescales of dynamic processes and intricate intra- and inter-molecular interactions of these molecules necessitate the development and utilization of computational approaches for biopolymer study and nanotechnology applications. Biopolymer nanotechnology exploits the natural chemistry of biopolymers to perform novel functions at the nanoscale. Molecular dynamics is the numerical simulation of chemical entities according to the physical laws of motion and statistical mechanics. The number of atoms in biopolymers require coarse-grained methods to fully sample the dynamics of the system with reasonable resources. Accordingly, a coarse-grained molecular dynamics model for the characterization of hybrid nucleic acid-protein nanotechnology was developed. Proteins are represented as an anisotropic network model (ANM) which show good agreement with experimentally derived protein dynamics for a small computational cost. The model was subsequently applied to hybrid DNA-protein cages systems and exhibited excellent agreement with experimental results. Ongoing development efforts look to apply network models to oxDNA origami to create multiscale models for DNA origami. The network approximation will allow for detailed simulation of DNA origami association, of concern to DNA crystal and lattice formation. Identification and design of target-specific binders (aptamers) has received considerable attention on account of their diagnostic and therapeutic potential. Generated in selection cycles from extensive random libraries, biopolymer aptamers are of particular interest due to their potential non-immunogenic properties. Machine learning leverages the use of powerful statistical principles to train a model to transform an input into a desired output. Parameters of the model are iteratively adjusted according to the gradient of the cost function. An unsupervised and generative machine learning model was applied to Thrombin aptamer sequence data. From the model, sequence characteristics necessary for binding were identified and new aptamers capable of binding Thrombin were sampled and verified experimentally. Future work on the development and utilization of an unsupervised and interpretable machine learning model for unaligned sequence data is also discussed.
Date Created
2022
Agent

Exploration of Aggregation and Multivalency as Viral Inhibition Strategies

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Description
Scientists are entrusted with developing novel molecular strategies for effective prophylactic and therapeutic interventions. Antivirals are indispensable tools that can be targeted at viral domains directly or at cellular domains indirectly to obstruct viral infections and reduce pathogenicity. Despite their…

Scientists are entrusted with developing novel molecular strategies for effective prophylactic and therapeutic interventions. Antivirals are indispensable tools that can be targeted at viral domains directly or at cellular domains indirectly to obstruct viral infections and reduce pathogenicity. Despite their transformative potential in healthcare, to date, antivirals have been clinically approved to treat only 10 out of the greater than 200 known pathogenic human viruses. Additionally, as obligate intracellular parasites, many virus functions are intimately coupled with host cellular processes. As such, the development of a clinically relevant antiviral is challenged by the limited number of clear targets per virus and necessitates an extensive insight into these molecular processes. Compounding this challenge, many viral pathogens have evolved to evade effective antivirals. Therefore, a means to develop virus- or strain-specific antivirals without detailed insight into each idiosyncratic biochemical mechanism may aid in the development of antivirals against a larger swath of pathogens. Such an approach will tremendously benefit from having the specific molecular recognition of viral species as the lowest barrier. Here, I modify a nanobody (anti-green fluorescent protein) that specifically recognizes non-essential epitopes (glycoprotein M-pHluorin chimera) presented on the extra virion surface of a virus (Pseudorabies virus strain 486). The nanobody switches from having no inhibitory properties (tested up to 50 μM) to ∼3 nM IC50 in in vitro infectivity assays using porcine kidney (PK15) cells. The nanobody modifications use highly reliable bioconjugation to a three-dimensional wireframe deoxyribonucleic acid (DNA) origami scaffold. Mechanistic studies suggest that inhibition is mediated by the DNA origami scaffold bound to the virus particle, which obstructs the internalization of the viruses into cells, and that inhibition is enhanced by avidity resulting from multivalent virus and scaffold interactions. The assembled nanostructures demonstrate negligible cytotoxicity (<10 nM) and sufficient stability, further supporting their therapeutic potential. If translatable to other viral species and epitopes, this approach may open a new strategy that leverages existing infrastructures – monoclonal antibody development, phage display, and in vitro evolution - for rapidly developing novel antivirals in vivo.
Date Created
2022
Agent
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