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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
Dielectrophoresis (DEP) is a technique that influences the motion of polarizable particles in an electric field gradient. DEP can be combined with other effects that influence the motion of a particle in a microchannel, such as electrophoresis and electroosmosis. Together, these three can be used to probe properties of an analyte, including charge, conductivity, and zeta potential. DEP shows promise as a high-resolution differentiation and separation method, with the ability to distinguish between subtly-different populations. This, combined with the fast (on the order of minutes) analysis times offered by the technique, lend it many of the features necessary to be used in rapid diagnostics and point-of-care devices.
Here, a mathematical model of dielectrophoretic data is presented to connect analyte properties with data features, including the intercept and slope, enabling DEP to be used in applications which require this information. The promise of DEP to distinguish between analytes with small differences is illustrated with antibiotic resistant bacteria. The DEP system is shown to differentiate between methicillin-resistant and susceptible Staphylococcus aureus. This differentiation was achieved both label free and with bacteria that had been fluorescently-labeled. Klebsiella pneumoniae carbapenemase-positive and negative Klebsiella pneumoniae were also distinguished, demonstrating the differentiation for a different mechanism of antibiotic resistance. Differences in dielectrophoretic behavior as displayed by S. aureus and K. pneumoniae were also shown by Staphylococcus epidermidis. These differences were exploited for a separation in space of gentamicin-resistant and -susceptible S. epidermidis. Besides establishing the ability of DEP to distinguish between populations with small biophysical differences, these studies illustrate the possibility for the use of DEP in applications such as rapid diagnostics.
Here, a mathematical model of dielectrophoretic data is presented to connect analyte properties with data features, including the intercept and slope, enabling DEP to be used in applications which require this information. The promise of DEP to distinguish between analytes with small differences is illustrated with antibiotic resistant bacteria. The DEP system is shown to differentiate between methicillin-resistant and susceptible Staphylococcus aureus. This differentiation was achieved both label free and with bacteria that had been fluorescently-labeled. Klebsiella pneumoniae carbapenemase-positive and negative Klebsiella pneumoniae were also distinguished, demonstrating the differentiation for a different mechanism of antibiotic resistance. Differences in dielectrophoretic behavior as displayed by S. aureus and K. pneumoniae were also shown by Staphylococcus epidermidis. These differences were exploited for a separation in space of gentamicin-resistant and -susceptible S. epidermidis. Besides establishing the ability of DEP to distinguish between populations with small biophysical differences, these studies illustrate the possibility for the use of DEP in applications such as rapid diagnostics.
ContributorsHilton, Shannon (Author) / Hayes, Mark A. (Thesis advisor) / Borges, Chad (Committee member) / Herckes, Pierre (Committee member) / Arizona State University (Publisher)
Created2019
Description
DNA, RNA and Protein are three pivotal biomolecules in human and other organisms, playing decisive roles in functionality, appearance, diseases development and other physiological phenomena. Hence, sequencing of these biomolecules acquires the prime interest in the scientific community. Single molecular identification of their building blocks can be done by a technique called Recognition Tunneling (RT) based on Scanning Tunneling Microscope (STM). A single layer of specially designed recognition molecule is attached to the STM electrodes, which trap the targeted molecules (DNA nucleoside monophosphates, RNA nucleoside monophosphates or amino acids) inside the STM nanogap. Depending on their different binding interactions with the recognition molecules, the analyte molecules generate stochastic signal trains accommodating their “electronic fingerprints”. Signal features are used to detect the molecules using a machine learning algorithm and different molecules can be identified with significantly high accuracy. This, in turn, paves the way for rapid, economical nanopore sequencing platform, overcoming the drawbacks of Next Generation Sequencing (NGS) techniques.
To read DNA nucleotides with high accuracy in an STM tunnel junction a series of nitrogen-based heterocycles were designed and examined to check their capabilities to interact with naturally occurring DNA nucleotides by hydrogen bonding in the tunnel junction. These recognition molecules are Benzimidazole, Imidazole, Triazole and Pyrrole. Benzimidazole proved to be best among them showing DNA nucleotide classification accuracy close to 99%. Also, Imidazole reader can read an abasic monophosphate (AP), a product from depurination or depyrimidination that occurs 10,000 times per human cell per day.
In another study, I have investigated a new universal reader, 1-(2-mercaptoethyl)pyrene (Pyrene reader) based on stacking interactions, which should be more specific to the canonical DNA nucleosides. In addition, Pyrene reader showed higher DNA base-calling accuracy compare to Imidazole reader, the workhorse in our previous projects. In my other projects, various amino acids and RNA nucleoside monophosphates were also classified with significantly high accuracy using RT. Twenty naturally occurring amino acids and various RNA nucleosides (four canonical and two modified) were successfully identified. Thus, we envision nanopore sequencing biomolecules using Recognition Tunneling (RT) that should provide comprehensive betterment over current technologies in terms of time, chemical and instrumental cost and capability of de novo sequencing.
To read DNA nucleotides with high accuracy in an STM tunnel junction a series of nitrogen-based heterocycles were designed and examined to check their capabilities to interact with naturally occurring DNA nucleotides by hydrogen bonding in the tunnel junction. These recognition molecules are Benzimidazole, Imidazole, Triazole and Pyrrole. Benzimidazole proved to be best among them showing DNA nucleotide classification accuracy close to 99%. Also, Imidazole reader can read an abasic monophosphate (AP), a product from depurination or depyrimidination that occurs 10,000 times per human cell per day.
In another study, I have investigated a new universal reader, 1-(2-mercaptoethyl)pyrene (Pyrene reader) based on stacking interactions, which should be more specific to the canonical DNA nucleosides. In addition, Pyrene reader showed higher DNA base-calling accuracy compare to Imidazole reader, the workhorse in our previous projects. In my other projects, various amino acids and RNA nucleoside monophosphates were also classified with significantly high accuracy using RT. Twenty naturally occurring amino acids and various RNA nucleosides (four canonical and two modified) were successfully identified. Thus, we envision nanopore sequencing biomolecules using Recognition Tunneling (RT) that should provide comprehensive betterment over current technologies in terms of time, chemical and instrumental cost and capability of de novo sequencing.
ContributorsSen, Suman (Author) / Lindsay, Stuart (Thesis advisor) / Zhang, Peiming (Thesis advisor) / Gould, Ian R. (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2016