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- All Subjects: Biochemistry
- Genre: Doctoral Dissertation
- Creators: Stephanopoulos, Nicholas
Description
Structural-based drug discovery is becoming the essential tool for drug development withlower cost and higher efficiency compared to the conventional method. Knowledge of the
three-dimensional structure of protein targets has the potential to accelerate the process
for screening drug candidates. X-ray crystallography has proven to be the most used and
indispensable technology in structural-based drug discovery. The provided
comprehensive structural information about the interaction between the disease-related
protein target and ligand can guide the chemical modification on the ligand to improve
potency and selectivity. X-ray crystallography has been upgraded from traditional
synchrotron to the third generation, which enabled the surge of the structural
determination of macromolecular. The introduction of X-ray free electron laser further
alleviated the uncertain and time-consuming crystal size optimization process and
extenuated the radiation damage by “diffraction before destruction”. EV-D68 2A
protease was proved to be an important pharmaceutical target for acute flaccid myelitis.
This thesis reports the first atomic structure of the EV-D68 2A protease and the structuresof its two mutants, revealing it adopting N-terminal four-stranded sheets and C-terminal
six-stranded ß-barrels structure, with a tightly bound zinc atom. These structures will
guide the chemical modification on its inhibitor, Telaprevir. Integrin ⍺Mβ2 is an integrin
with the α I-domain, related to many immunological functions including cell
extravasation, phagocytosis, and immune synapse formation, so studying the molecular
ligand-binding mechanism and activation mechanism of ⍺Mβ2 is of importance. This
thesis uncovers the preliminary crystallization condition of ⍺Mβ2-I domain in complex
with its ligand Pleiotrophin and the initial structural model. The structural model shows consistency with the previous hypothesis that the primary binding sites are metal iondependent
adhesion sites on ⍺Mβ2-I domain and the thrombospondin type-1 repeat (TSR)
domains of Pleiotrophin. Drug molecules with high potency and selectivity can be
designed based on the reported structures of the EV-D68 2A protease and ⍺Mβ2-I domain
in the future.
ContributorsLiu, Chang (Author) / Liu, Wei (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Chiu, Po-Lin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Transient protein-protein and protein-molecule interactions fluctuate between associated and dissociated states. They are widespread in nature and mediate most biological processes. These interactions are complex and are strongly influenced by factors such as concentration, structure, and environment. Understanding and utilizing these types of interactions is useful from both a fundamental and design perspective. In this dissertation, transient protein interactions are used as the sensing element of a biosensor for small molecule detection. This is done by using a transcription factor-small molecule pair that mediates the activation of a CRISPR/Cas12a complex. Activation of the Cas12a enzyme results in an amplified readout mechanism that is either fluorescence or paper based. This biosensor can successfully detect 9 different small molecules including antibiotics with a tuneable detection limit ranging from low µM to low nM. By combining protein and nucleic acid-based systems, this biosensor has the potential to report on almost any protein-molecule interaction, linking this to the intrinsic amplification that is possible when working with nucleic acid-based technologies. The second part of this dissertation focuses on understanding protein-molecule interactions at a more fundamental level, and, in so doing, exploring design rules required to generalize sensors like the ones described above. This is done by training a neural network algorithm with binding data from high density peptide micro arrays incubated with specific protein targets. Because the peptide sequences were chosen simply to evenly, though sparsely, represent all sequence space, the resulting network provides a comprehensive sequence/binding relationship for a given target protein. While past work had shown that this works well on the arrays, here I have explored how well the neural networks thus trained, predict sequence-dependent binding in the context of protein-protein and peptide-protein interactions. Amino acid sequences, either free in solution or embedded in protein structure, will display somewhat different binding properties than sequences affixed to the surface of a high-density array. However, the neural network trained on array sequences was able to both identify binding regions in between proteins and predict surface plasmon resonance-based binding propensities for peptides with statistically significant levels of accuracy.
ContributorsSwingle, Kirstie Lynn (Author) / Woodbury, Neal W (Thesis advisor) / Green, Alexander A (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2022
Description
The biological lipid bilayer on cells or the cell membrane is a surface teeming with activity. Several membrane proteins decorate the lipid bilayer to carry out various functionalities that help a cell interact with the environment, gather resources and communicate with other cells. This provides a repertoire of biological structures and processes that can be mimicked and manipulated. Since its inception in the late 20th century deoxyribonucleic acid (DNA) nanotechnology has been used to create nanoscale objects that can be used for such purposes. Using DNA as the building material provides the user with a programmable and functionalizable tool box to design and demonstrate these ideas. In this dissertation, I describe various DNA nanostructures that can insert or interact with lipid bilayers for cargo transport, diagnostics and therapeutics. First, I describe a reversibly gated DNA nanopore of 20.4nm x 20.4nm cross sectional width. Controlled transport of cargoes of various sizes across a lipid bilayer through a channel formed by the DNA nanopore was demonstrated. This demonstration paves the way for a class of nanopores that can be activated by different stimuli. The membrane insertion capability of the DNA nanopore is further utilized to design a nanopore sensor that can detect oligonucleotides of a specific s equence inside a lipid vesicle. The ease with which the sensor can be modified to i dentify different diagnostic markers for disease detection was shown by designing a sensor that can identify the non small cell lung cancer marker micro ribonucleic acid -21 (miRNA21). Finally, I demonstrate the therapeutic capabilities of DNA devices with a DNA tetrabody that can recruit natural killer cells (NK cells) to target cancer cells. The DNA tetrabody functionalized with cholesterol molecules and Her2 affibody inserts into NK cell membrane leading it to Her2 positive cancer cells. This shows that inthe presence of DNA tetrabody, the NK cell activation gets accelerated.
ContributorsAbraham, Leeza (Author) / Yan, Hao (Thesis advisor) / Liu, Uan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Exoelectrogenic organisms transfer electrons from their quinone pool to extracellular acceptors over m-scale distances through appendages known as “biological nanowires”. These structures have been described as cytochrome-rich membrane extensions or pili. However, the components and mechanisms of this long-range electron transfer remain largely unknown. This dissertation describes supramolecular assembly of a tetraheme cytochrome into well-defined models of microbial nanowires and uses those structures to explore the mechanisms of ultra-long-range electron transfer. Chiral-induced-spin-selectivity through the cytochrome is also demonstrated. Nanowire extensions in Shewanella oneidensis have been hypothesized to transfer electrons via electron tunneling through proteinaceous structures that reinforce π-π stacking or through electron hopping via redox cofactors found along their lengths. To provide a model to evaluate the possibility of electron hopping along micron-scale distances, the first part of this dissertation describes the construction of a two-component, supramolecular nanostructure comprised of a small tetraheme cytochrome (STC) from Shewanella oneidensis fused to a peptide domain that self-assembles with a β-fibrillizing peptide. Structural and electrical characterization shows that the self-assembled protein fibers have dimensions relevant to understanding ultralong-range electron transfer and conduct electrons along their length via a cytochrome-mediated mechanism of electron transfer. The second part of this dissertations shows that a model three-component fiber construct based on charge complementary peptides and the redox protein can also be assembled. Structural and electrical characterization of the three-component structure also demonstrates desirable dimensions and electron conductivity along the length via a cytochrome-mediated mechanism.
In vivo, it has been hypothesized that cytochromes in the outer surface conduit are spin-selective. However, cytochromes in the periplasm of Shewanella oneidensis have not been shown to be spin selective, and the physiological impact of the chiral-induced-spin-selectivity (CISS) effect on microbial electron transport remains unclear. In the third part of this dissertation, investigations via spin polarization and a spin-dependent conduction study show that STC is spin selective, suggesting that spin selectivity may be an important factor in the electron transport efficiency of exoelectrogens.
In conclusion, this dissertation enables a better understanding of long-range electron transfer in bacterial nanowires and bioelectronic circuitry and offers suggestions for how to construct enhanced biosensors.
ContributorsNWACHUKWU, JUSTUS NMADUKA (Author) / Jones, Anne K. (Thesis advisor) / Mills, Jeremy (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Molecular recognition forms the basis of all protein interactions, and therefore is crucial for maintaining biological functions and pathways. It can be governed by many factors, but in case of proteins and peptides, the amino acids sequences of the interacting entities play a huge role. It is molecular recognition that helps a protein identify the correct sequences residues necessary for an interaction, among the vast number of possibilities from the combinatorial sequence space. Therefore, it is fundamental to study how the interacting amino acid sequences define the molecular interactions of proteins. In this work, sparsely sampled peptide sequences from the combinatorial sequence space were used to study the molecular recognition observed in proteins, especially monoclonal antibodies. A machine learning based approach was used to study the molecular recognition characteristics of 11 monoclonal antibodies, where a neural network (NN) was trained on data from protein binding experiments performed on high-throughput random-sequence peptide microarrays. The use of random-sequence microarrays allowed for the peptides to be sparsely sampled from sequence space. Post-training, a sequence vs. binding relationship was deduced by the NN, for each antibody. This in silico relationship was then extended to larger libraries of random peptides, as well as to the biologically relevant sequences (target antigens, and proteomes). The NN models performed well in predicting the pertinent interactions for 6 out of the 11 monoclonal antibodies, in all aspects. The interactions of the other five monoclonal antibodies could not be predicted well by the models, due to their poor recognition of the residues that were omitted from the array. Furthermore, NN predicted sequence vs. binding relationships for 3 other proteins were experimentally probed using surface plasmon resonance (SPR). This was done to explore the relationship between the observed and predicted binding to the arrays and the observed binding on different assay platforms. It was noted that there was a general motif dependent correlation between predicted and SPR-measured binding. This study also indicated that a combined reiterative approach using in silico and in vitro techniques is a powerful tool for optimizing the selectivity of the protein-binding peptides.
ContributorsBisarad, Pritha (Author) / Woodbury, Neal W (Thesis advisor) / Green, Alexander A (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2022
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 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.
ContributorsProcyk, Jonah (Author) / Sulc, Petr (Thesis advisor) / Stephanopoulos, Nicholas (Thesis advisor) / Hariadi, Rizal (Committee member) / Heyden, Matthias (Committee member) / Arizona State University (Publisher)
Created2022
Description
In the past decade, technological breakthroughs have facilitated structure determination of so many difficult-to-study membrane protein targets. In this thesis research, three techniques were investigated to enable the structural determination of such challenging targets, polychromatic pink-beam serial crystallography with high-viscous sample, lipidic cubic phase (LCP)-based microcrystal electron diffraction (MicroED), and single-particle cryogenic electron microscopy targeting (cryoEM).
Inspired by the successful serial crystallography (SX) experiment at a synchrotron radiation source, it is first-time equipping the high-viscosity injector to X-ray fluxes increased at 100 times by a moderate increased in bandwidth to perform the pink beam SX experiments. The structure of proteinase K (PK) was determined to 1.8 Å resolution with 4 consecutive 100 ps X-ray pink beam pulse exposures. The structure of human A2A adenosine receptor (A2AAR) reached to a 4.2 Å resolution using 24 consecutive X-ray pink beam pulse exposures. It has proven the feasibility to utilize such storage-ring synchrotron sources complemented to serial femtosecond crystallography, presenting new opportunities for microcrystallography and the time-resolved experiments.
As an alternative approach to serial femtosecond crystallography, a novel protocol was developed to combine the lipidic cubic phase crystallization approach and microED strategy and solved the structure from LCP-embedded proteinase K microcrystals with the comparable high resolution to conventional crystallographic method.
It cannot be neglected that only very few portions of membrane proteins were able to be successfully crystallized for structure determination. Single particle cryoEM method allows the structural studies from protein molecules detour away from crystallization. An atomic resolution structure of the β1-AR bound with agonist in complex with Gs protein, with particle size of less than 200 kDa, was determined by cryoEM, reaching to an atomic resolution of 3.8 Å. The complex structure captured a fully active conformation and revealed the important mechanisms of how the agonist bound receptor activated Gs protein.
These technological developments provide more opportunities to the structural biology community to discover mechanisms underlying such complicated machinery network, which would eventually benefit the structure-based drug discovery.
Inspired by the successful serial crystallography (SX) experiment at a synchrotron radiation source, it is first-time equipping the high-viscosity injector to X-ray fluxes increased at 100 times by a moderate increased in bandwidth to perform the pink beam SX experiments. The structure of proteinase K (PK) was determined to 1.8 Å resolution with 4 consecutive 100 ps X-ray pink beam pulse exposures. The structure of human A2A adenosine receptor (A2AAR) reached to a 4.2 Å resolution using 24 consecutive X-ray pink beam pulse exposures. It has proven the feasibility to utilize such storage-ring synchrotron sources complemented to serial femtosecond crystallography, presenting new opportunities for microcrystallography and the time-resolved experiments.
As an alternative approach to serial femtosecond crystallography, a novel protocol was developed to combine the lipidic cubic phase crystallization approach and microED strategy and solved the structure from LCP-embedded proteinase K microcrystals with the comparable high resolution to conventional crystallographic method.
It cannot be neglected that only very few portions of membrane proteins were able to be successfully crystallized for structure determination. Single particle cryoEM method allows the structural studies from protein molecules detour away from crystallization. An atomic resolution structure of the β1-AR bound with agonist in complex with Gs protein, with particle size of less than 200 kDa, was determined by cryoEM, reaching to an atomic resolution of 3.8 Å. The complex structure captured a fully active conformation and revealed the important mechanisms of how the agonist bound receptor activated Gs protein.
These technological developments provide more opportunities to the structural biology community to discover mechanisms underlying such complicated machinery network, which would eventually benefit the structure-based drug discovery.
ContributorsZhu, Lan, Ph.D (Author) / Liu, Wei (Thesis advisor) / Mills, Jeremy (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2019
Description
Proteins are, arguably, the most complicated molecular machines found in nature. From the receptor proteins that decorate the exterior of cell membranes to enzymes that catalyze the slowest of chemical reactions, proteins perform a wide variety of essential biological functions. A reductionist view of proteins as a macromolecular group, however, may hold that they simply interact with other chemical species. Notably, proteins interact with other proteins, other biological macromolecules, small molecules, and ions. This in turn makes proteins uniquely qualified for use technological use as sensors of said chemical species (biosensors). Several methods have been developed to convert proteins into biosensors. Many of these techniques take advantage of fluorescence spectroscopy because it is a fast, non-invasive, non-destructive and highly sensitive method that also allows for spatiotemporal control. This, however, requires that first a fluorophore be added to a target protein. Several methods for achieving this have been developed from large, genetically encoded autofluorescent protein tags, to labeling with small molecule fluorophores using bioorthogonal chemical handles, to genetically encoded fluorescent non-canonical amino acids (fNCAA). In recent years, the fNCAA, L-(7-hydroxycoumarin-4yl)ethylglycine (7-HCAA) has been used in to develop several types of biosensors.
The dissertation I present here specifically addresses the use of the fNCAA L-(7-hydroxycoumarin-4-yl)ethylglycine (7-HCAA) in protein-based biosensors. I demonstrate 7-HCAA’s ability to act as a Förster resonance energy transfer (FRET) acceptor with tryptophan as the FRET donor in a single protein containing multiple tryptophans. I the describe efforts to elucidate—through both spectroscopic and structural characterization—interactions within a 7-HCAA containing protein that governs 7-HCAA fluorescence. Finally, I present a top-down computational design strategy for incorporating 7-HCAA into proteins that takes advantage of previously described interactions. These reports show the applicability of 7-HCAA and the wider class of fNCAAs as a whole for their use of rationally designed biosensors.
The dissertation I present here specifically addresses the use of the fNCAA L-(7-hydroxycoumarin-4-yl)ethylglycine (7-HCAA) in protein-based biosensors. I demonstrate 7-HCAA’s ability to act as a Förster resonance energy transfer (FRET) acceptor with tryptophan as the FRET donor in a single protein containing multiple tryptophans. I the describe efforts to elucidate—through both spectroscopic and structural characterization—interactions within a 7-HCAA containing protein that governs 7-HCAA fluorescence. Finally, I present a top-down computational design strategy for incorporating 7-HCAA into proteins that takes advantage of previously described interactions. These reports show the applicability of 7-HCAA and the wider class of fNCAAs as a whole for their use of rationally designed biosensors.
ContributorsGleason, Patrick Ray (Author) / Mills, Jeremy H (Thesis advisor) / Hecht, Sidney M. (Committee member) / Fromme, Petra (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2020
Description
Since the conception of DNA nanotechnology, the field has evolved towards the development of complex, dynamic 3D structures. The predictability of Watson-Crick base pairing makes DNA an unparalleled building block, and enables exceptional programmability in nanostructure shape and size. The work presented in this dissertation focuses on expanding two facets of the field: (1) introducing functionality through the incorporation of peptides to create DNA-peptide hybrid materials, and (2) the development of self-assembling DNA crystal lattices for scaffolding biomolecules. DNA nanostructures have long been proposed as drug delivery vehicles; however, they are not biocompatible because of their low stability in low salt environments and entrapment within the endosome. To address these issues, a functionalized peptide coating was designed to act as a counterion to a six-helix bundle, while simultaneously displaying numerous copies of an endosomal escape peptide to enable cytosolic delivery. This functionalized peptide coating creates a DNA-peptide hybrid material, but does not allow specific positioning or orientation of the peptides. The ability to control those aspects required the synthesis of DNA-peptide or DNA-peptide-DNA conjugates that can be incorporated into the nanostructure. The approach was utilized to produce a synbody where three peptides that bind transferrin with micromolar affinity, which were presented for multivalent binding to optimize affinity. Additionally, two DNA handle was attached to an enzymatically cleavable peptide to link two unique nanostructures. The second DNA handle was also used to constrain the peptide in a cyclic fashion to mimic the cell-adhesive conformations of RGD and PHSRN in fibronectin.
The original goal of DNA nanotechnology was to use a crystalline lattice made of DNA to host proteins for their structural determination using X-ray crystallography. The work presented here takes significant steps towards achieving this goal, including elucidating design rules to control cavity size within the scaffold for accommodating guest molecules of unique sizes, approaches to improve the atomic detail of the scaffold, and strategies to modulate the symmetry of each unique lattice. Finally, this work surveys methodologies towards the incorporation of several guest molecules, with promising preliminary results that constitute a significant advancement towards the ultimate goal of the field.
ContributorsMacCulloch, Tara Lynn (Author) / Stephanopoulos, Nicholas (Thesis advisor) / Borges, Chad (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2021
Description
G protein-coupled receptors (GPCRs) are a large family of proteins involved in the cell signaling and regulation of many biological and pathological processes in the human body. To fully understand their functions, various approaches are needed. This work combines several techniques to advance the study of GPCRs with the overarching goal of pursuing X-ray crystallization using lipidic cubic phase (LCP). In meso, or LCP crystallization method involves imbedding the GPCR into a lipid membrane-mimetic material which spontaneously forms when monoacylglycerols (MAGs) are mixed at the correct hydration level and temperature. It provides a stable environment for GPCRs and has been established as the most common method to resolve structural details of GPCRs (Chapter 2). Yet, before crystallization, GPCRs need to be put through several rounds of optimization of the construct design, including truncation of N- and C- termini, fusing different soluble proteins, and mutating the receptor (Chapter 3). Other methods were also used to gain structural insights into GPCR interactions, such as coarse-grained molecular dynamic simulations, which showed the specific regions of interactions with cholesterol molecules imbedded in the membranes (Chapter 4). This study demonstrated β2-adrenergic receptor (β2AR), a GPCR, as a model of a cholesterol-sensitive receptor. Mutations were made to test the effect of removing specific residues of interest on cholesterol stabilization through the LCP-Tm assay, producing results that align with the simulation data. Finally, the goal of the last study is to provide a guide to identify which host lipids form stable LCP phases for different applications (Chapter 5). Small angle X-ray scattering is used to identify phases in hundreds of different precipitant conditions in the search of suitable host lipid for LCP studies. The results present a systematic overview of the compatibility of common MAGs by screening them against different precipitant solutions including varying salts and polyethylene glycol (PEG) concentrations, different PEG sizes, the presence of detergent or protein in the sample, and the addition of cholesterol. Together, these studies present a variety of methods to advance the structural studies of GPCRs using LCP
ContributorsAL-SAHOURI, ZINA (Author) / Liu, Wei (Thesis advisor) / Stephanopoulos, Nicholas (Committee member) / Chiu, Po-Lin (Committee member) / Arizona State University (Publisher)
Created2021