Matching Items (40)
Description
Peptides offer great promise as targeted affinity ligands, but the space of possible peptide sequences is vast, making experimental identification of lead candidates expensive, difficult, and uncertain. Computational modeling can narrow the search by estimating the affinity and specificity of a given peptide in relation to a predetermined protein target. The predictive performance of computational models of interactions of intermediate-length peptides with proteins can be improved by taking into account the stochastic nature of the encounter and binding dynamics. A theoretical case is made for the hypothesis that, because of the flexibility of the peptide and the structural complexity of the target protein, interactions are best characterized by an ensemble of possible bound configurations rather than a single “lock and key” fit. A model incorporating these factors is proposed and evaluated. A comprehensive dataset of 3,924 peptide-protein interface structures was extracted from the Protein Data Bank (PDB) and descriptors were computed characterizing the geometry and energetics of each interface. The characteristics of these interfaces are shown to be generally consistent with the proposed model, and heuristics for design and selection of peptide ligands are derived. The curated and energy-minimized interface structure dataset and a relational database containing the detailed results of analysis and energy modeling are made publicly available via a web repository. A novel analytical technique based on the proposed theoretical model, Virtual Scanning Probe Mapping (VSPM), is implemented in software to analyze the interaction between a target protein of known structure and a peptide of specified sequence, producing a spatial map indicating the most likely peptide binding regions on the protein target. The resulting predictions are shown to be superior to those of two other published methods, and support the validity of the stochastic binding model.
ContributorsEmery, Jack Scott (Author) / Pizziconi, Vincent B (Thesis advisor) / Woodbury, Neal W (Thesis advisor) / Guilbeau, Eric J (Committee member) / Stafford, Phillip (Committee member) / Taylor, Thomas (Committee member) / Towe, Bruce C (Committee member) / Arizona State University (Publisher)
Created2010
Description
The relation between water and protein physics is a topic of much interest. Molecular dynamics (MD) simulations of biomolecules are a common computational technique to obtain atomistic insight into the physical behavior of biomolecules, including the nature of the interaction between water and the protein. In order to model biomolecules at the highest level of accuracy, an explicit, atomistic representation of the water is typically necessary. The number of water molecules that need to be simulated is normally on the order of thousands. The high dimensional MD dataset is then expanded with considerably more dimensions. We describe here a set of tools which can be used to extract general features of the water behavior, which can then be utilized to build simplified models of the water kinetics which make quantitative predictions, such as the flux rate through a pore.
ContributorsWelland, Ian (Author) / Beckstein, Oliver (Committee member) / Matyushov, Dmitry (Committee member) / Barrett, The Honors College (Contributor)
Created2015-12
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 crystal jellyfish, Aequorea victoria, produces and emits light, called bioluminescence. Its DNA codes for sequence of 238 amino acids that forms a protein called Green Fluorescent Protein (GFP). FP is folded so that a part of the protein, called the chromophore, is located in the center of the protein. The chemical structure of the chromophore emits a green fluorescence when exposed to light in the range of blue to ultraviolet.
ContributorsGuerrero, Anna (Creator) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2017-02-06
Description
In 2011, Sonja Vernes and Simon Fisher performed a series of experiments to determine which developmental processes are controlled by the mouse protein Foxp2. Previous research showed that altering the Foxp2 protein changed how neurons grew, so Vernes and Fisher hypothesized that Foxp2 would affect gene networks that involved in the development of neurons, or nerve cells. Their results confirmed that Foxp2 affected the development of gene networks involved in the growth of neurons, as well as networks that are involved in cell specialization and cell communication. The researchers determined that Foxp2 is important for a variety of developmental processes such as motor control, language acquisition, and cognition.
ContributorsFowler, Kat (Author) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2017-05-30
Description
The hedgehog signaling pathway is a mechanism that regulates cell growth and differentiation during embryonic development, called embryogenesis, in animals. The hedgehog signaling pathway works both between cells and within individual cells.
ContributorsHaskett, Dorothy Regan (Author) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2016-06-27
Description
Barnase-Barstar is a protein complex that has a strong association constant. The purpose of this research is to investigate the effects of conformational fluctuations on protein-water interactions, resulting water-mediated interactions, and the binding free energy of the protein complex. Using all-atom molecular dynamics simulations, the sets of simulations for flexible and rigid proteins to identify the effects on water-mediated interactions were prepared for analysis. To analyze the properties and interactions that result in the strong association of the Barnase-Barstar protein complex, the molecular dynamics simulations were prepared. A thorough review of the GROMACS manual and completion of the GROMACS Lysozyme in Water tutorial was completed to understand the steps and commands to write and run molecular dynamics simulations. The preliminary data investigated the impact of water-mediated interactions on the solvation free energy in the Barnase-Barstar protein complex where the proteins are kept rigid. This was achieved by observing the change in solvation free energy with respect to separation distance. From the data obtained, it is concluded that solvent-mediated interactions do not contribute to the negative binding free energy. With increasing separation distance, the change in solvation free energy decreased. Therefore, thermodynamically, water-mediated interactions destabilize the protein complex, while the binding free energy is dominated by direct protein-protein interactions. The follow-up simulations of flexible proteins with controlled protein-protein separation distances, for which a fully automated simulation and analysis protocol has been prepared in this project, will allow us to quantify the impact of conformational fluctuations on water-mediated interactions and the binding free energy of the protein complex by comparison to the simulations of rigid proteins.
ContributorsJoshi, Mansi (Author) / Heyden, Matthias (Thesis director) / Sulc, Petr (Committee member) / Singharoy, Abhishek (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor)
Created2022-05
Description
Non-canonical amino acids (NCAAs) can be used in protein chemistry to determine their structures. A common method for imaging proteins is cryo-electron microscopy (cryo-EM) which is ideal for imaging proteins that cannot be obtained in large quantities. Proteins with indistinguishable features are difficult to image using this method due to the large size requirements, therefore antibodies designed specifically for binding these proteins have been utilized to better identify the proteins. By using an existing antibody that binds to stilbene, NCAAs containing this molecule can be used as a linker between proteins and an antibody. Stilbene containing amino acids can be integrated into proteins to make this process more access able. In this paper, synthesis methods for various NCAAs containing stilbene were proposed. The resulting successfully synthesized NCAAs were E)-N6-(5-oxo-5-((4-styrylphenyl) amino) pentanoyl) lysine, (R,E)-2-amino-3-(5-oxo-5-((4-styrylphenyl)amino)pentanamido)propanoic acid, (E)-2-amino-5-(5-oxo-5-((4-styrylphenyl) amino) pentanamido) pentanoic acid. A synthesis for three more shorter amino acids, (R,E)-2-amino-3-(3-oxo-3-((4-styrylphenyl) amino) propanamido) propanoic acid, (E)-2-amino-5-(3-oxo-3-((4-styrylphenyl) amino) propanamido) pentanoic acid, and (E)-N6-(3-oxo-3-((4-styrylphenyl) amino) propanoyl) lysine, is also proposed.
ContributorsJenkins, Bryll (Author) / Mills, Jeremy (Thesis director) / Ghirlanda, Giovanna (Committee member) / Nannenga, Brent (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor)
Created2022-05
Description
One of the greatest problems facing society today is the development of a
sustainable, carbon neutral energy source to curb the reliance on fossil fuel combustion as the primary source of energy. To overcome this challenge, research efforts have turned to biology for inspiration, as nature is adept at inter-converting low molecular weight precursors into complex molecules. A number of inorganic catalysts have been reported that mimic the active sites of energy-relevant enzymes such as hydrogenases and carbon monoxide dehydrogenase. However, these inorganic models fail to achieve the high activity of the enzymes, which function in aqueous systems, as they lack the critical secondary-shell interactions that enable the active site of enzymes to outperform their organometallic counterparts.
To address these challenges, my work utilizes bio-hybrid systems in which artificial proteins are used to modulate the properties of organometallic catalysts. This approach couples the diversity of organometallic function with the robust nature of protein biochemistry, aiming to utilize the protein scaffold to not only enhance rates of reaction, but also to control catalytic cycles and reaction outcomes. To this end, I have used chemical biology techniques to modify natural protein structures and augment the H2 producing ability of a cobalt-catalyst by a factor of five through simple mutagenesis. Concurrently I have designed and characterized a de novo peptide that incorporates various iron sulfur clusters at discrete distances from one another, facilitating electron transfer between the two. Finally, using computational methodologies I have engineered proteins to alter the specificity of a CO2 reduction reaction. The proteins systems developed herein allow for study of protein secondary-shell interactions during catalysis, and enable structure-function relationships to be built. The complete system will be interfaced with a solar fuel cell, accepting electrons from a photosensitized dye and storing energy in chemical bonds, such as H2 or methanol.
sustainable, carbon neutral energy source to curb the reliance on fossil fuel combustion as the primary source of energy. To overcome this challenge, research efforts have turned to biology for inspiration, as nature is adept at inter-converting low molecular weight precursors into complex molecules. A number of inorganic catalysts have been reported that mimic the active sites of energy-relevant enzymes such as hydrogenases and carbon monoxide dehydrogenase. However, these inorganic models fail to achieve the high activity of the enzymes, which function in aqueous systems, as they lack the critical secondary-shell interactions that enable the active site of enzymes to outperform their organometallic counterparts.
To address these challenges, my work utilizes bio-hybrid systems in which artificial proteins are used to modulate the properties of organometallic catalysts. This approach couples the diversity of organometallic function with the robust nature of protein biochemistry, aiming to utilize the protein scaffold to not only enhance rates of reaction, but also to control catalytic cycles and reaction outcomes. To this end, I have used chemical biology techniques to modify natural protein structures and augment the H2 producing ability of a cobalt-catalyst by a factor of five through simple mutagenesis. Concurrently I have designed and characterized a de novo peptide that incorporates various iron sulfur clusters at discrete distances from one another, facilitating electron transfer between the two. Finally, using computational methodologies I have engineered proteins to alter the specificity of a CO2 reduction reaction. The proteins systems developed herein allow for study of protein secondary-shell interactions during catalysis, and enable structure-function relationships to be built. The complete system will be interfaced with a solar fuel cell, accepting electrons from a photosensitized dye and storing energy in chemical bonds, such as H2 or methanol.
ContributorsSommer, Dayn (Author) / Ghirlanda, Giovanna (Thesis advisor) / Redding, Kevin (Committee member) / Moore, Gary (Committee member) / Arizona State University (Publisher)
Created2016
Description
Borrelia burgdorferi (Bb), the causative agent of Lyme disease, is a unique pathogen, with a complex genome and unique immune evasion tactics. It lacks genes encoding proteins involved in nutrient synthesis and typical metabolic pathways, and therefore relies on the host for nutrients. The Bb genome encodes both an unusually high number of predicted outer surface lipoproteins of unknown function but with multiple complex roles in pathogenesis, and an unusually low number of predicted outer membrane proteins, given the necessity of bringing in the required nutrients for pathogen survival. Cellular processing of bacterial membrane proteins is complex, and structures of proteins from Bb have all been solved without the N-terminal signal sequence that directs the protein to proper folding and placement in the membrane. This dissertation presents the first membrane-directed expression in E. coli of several Bb proteins involved in the pathogenesis of Lyme disease. For the first time, I present evidence that the predicted lipoprotein, BBA57, forms a large alpha-helical homo-multimeric complex in the OM, is soluble in several detergents, and purifiable. The purified BBA57 complex forms homogeneous, 10 nm-diameter particles, visible by negative stain electron microscopy. Two-dimensional class averages from negative stain images reveal the first low-resolution particle views, comprised of a ring of subunits with a plug on top, possibly forming a porin or channel. These results provide the first evidence to support our theories that some of the predicted lipoproteins in Bb form integral-complexes in the outer membrane, and require proper membrane integration to form functional proteins.
ContributorsRobertson, Karie (Author) / Hansen, Debra T. (Thesis advisor) / Fromme, Petra (Thesis advisor) / Van Horn, Wade (Committee member) / Chiu, Po-Lin (Committee member) / Arizona State University (Publisher)
Created2020