Matching Items (5)
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- All Subjects: Antibodies
- All Subjects: MATLAB
- Creators: Diehnelt, Chris
Description
Methicillin-Resistant Staphylococcus aureus (MRSA) infections are a major challenge to healthcare professionals. Treatment of MRSA is expensive, and otherwise avoidable deaths occur every year in the United States due to MRSA infections. Additionally, such infections lengthen patients’ stays in hospitals, keeping them out of work and adversely affecting the economy. Beta lactam antibiotics used to be highly effective against S. aureus infections, but resistance mechanisms have rendered methicillin, oxacillin, and other beta lactam antibiotics ineffective against these infections. A promising avenue for MRSA treatment lies in the use of synthetic antibodies—molecules that bind with specificity to a given compound. Synbody 14 is an example of such a synbody, and has been designed with MRSA treatment in mind. Mouse model studies have even associated Syn14 treatment with reduced weight loss and morbidity in MRSA-infected mice. In this experiment, in vitro activity of Syn 14 and oxacillin was assessed. Early experiments measured Syn 14 and oxacillin’s effectiveness in inhibiting colony growth in growth media, mouse serum, and mouse blood. Syn14 and oxacillin had limited efficacy against USA300 strain MRSA, though interestingly it was noted that Syn14 outperformed oxacillin in mouse serum and whole mouse blood, indicating the benefits of its binding properties. A second experiment measured the impact that a mix of oxacillin and Syn 14 had on colony growth, as well as the effect of adding them simultaneously or one after the other. While use of either bactericidal alone did not show a major inhibitory effect on USA300 MRSA colony growth, their use in combination showed major decreases in colony growth. Moreover, it was found that unlike other combination therapies, Syn14 and oxacillin did not require simultaneous addition to MRSA cells to achieve inhibition of cell growth. They merely required that Syn14 be added first. This result suggests Syn14’s possible utility in therapeutic settings, as the time insensitivity of synergy removes a major hurdle to clinical use—the difficulty in ensuring that two drugs reach an affected area at the same time. Syn14 remains a promising antimicrobial agent, and further study should focus on its precise mechanism of action and suitability in clinical treatment of MRSA infections.
ContributorsMichael, Alexander (Author) / Diehnelt, Chris (Thesis director) / Stafford, Phillip (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2015-05
Description
This project aims to address the current protocol regarding the diagnosis and treatment of traumatic brain injury (TBI) in medical industries around the world. Although there are various methods used to qualitatively determine if TBI has occurred to a patient, this study attempts to aid in the creation of a system for quantitative measurement of TBI and its relative magnitude. Through a method of artificial evolution/selection called phage display, an antibody that binds highly specifically to a post-TBI upregulated brain chondroitin sulfate proteoglycan called neurocan has been identified. As TG1 Escheria Coli bacteria were infected with KM13 helper phage and M13 filamentous phage in conjunction, monovalent display of antibody fragments (ScFv) was performed. The ScFv bind directly to the neurocan and from screening, phage that produced ScFv's with higher affinity and specificity to neurocan were separated and purified. Future research aims to improve the ScFv characteristics through increased screening toward neurocan. The identification of a highly specific antibody could lead to improved targeting of neurocan post-TBI in-vivo, aiding researchers in quantitatively defining TBI by visualizing its magnitude.
ContributorsSeelig, Timothy Scott (Author) / Stabenfeldt, Sarah (Thesis director) / Ankeny, Casey (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
Description
Predicting the binding sites of proteins has historically relied on the determination of protein structural data. However, the ability to utilize binding data obtained from a simple assay and computationally make the same predictions using only sequence information would be more efficient, both in time and resources. The purpose of this study was to evaluate the effectiveness of an algorithm developed to predict regions of high-binding on proteins as it applies to determining the regions of interaction between binding partners. This approach was applied to tumor necrosis factor alpha (TNFα), its receptor TNFR2, programmed cell death protein-1 (PD-1), and one of its ligand PD-L1. The algorithms applied accurately predicted the binding region between TNFα and TNFR2 in which the interacting residues are sequential on TNFα, however failed to predict discontinuous regions of binding as accurately. The interface of PD-1 and PD-L1 contained continuous residues interacting with each other, however this region was predicted to bind weaker than the regions on the external portions of the molecules. Limitations of this approach include use of a linear search window (resulting in inability to predict discontinuous binding residues), and the use of proteins with unnaturally exposed regions, in the case of PD-1 and PD-L1 (resulting in observed interactions which would not occur normally). However, this method was overall very effective in utilizing the available information to make accurate predictions. The use of the microarray to obtain binding information and a computer algorithm to analyze is a versatile tool capable of being adapted to refine accuracy.
ContributorsBrooks, Meilia Catherine (Author) / Woodbury, Neal (Thesis director) / Diehnelt, Chris (Committee member) / Ghirlanda, Giovanna (Committee member) / Department of Psychology (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Monoclonal antibody therapy focuses on engineering immune cells to target specific peptide sequences indicative of disease. An impediment in the continued advancement of this market is the lack of an efficient, inexpensive means of characterization that can be broadly applied to any antibody while still providing high-density data. Many characterization methods address an antibody's affinity for its cognate sequence but overlook other important aspects of binding behavior such as off-target binding interactions. The purpose of this study is to demonstrate how the binding intensity between an antibody and a library of random-sequence peptides, otherwise known as an immunosignature, can be evaluated to determine antibody specificity and polyreactivity. A total of 24 commercially available monoclonal antibodies were assayed on 125K and 330K peptide microarrays and analyzed using a motif clustering program to predict candidate epitopes within each antigen sequence. The results support the further development of immunosignaturing as an antibody characterization tool that is relevant to both therapeutic and non-therapeutic antibodies.
ContributorsDai, Jennifer T. (Author) / Stafford, Phillip (Thesis director) / Diehnelt, Chris (Committee member) / School of Life Sciences (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.
ContributorsPoweleit, Andrew Michael (Author) / Woodbury, Neal (Thesis director) / Diehnelt, Chris (Committee member) / Chiu, Po-Lin (Committee member) / School of Molecular Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05