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Thesis - Evan Schroeder (Revisions v2)7.67 MB
Embargoed Access, Embargo ends: 01/31/2026
Dissertation
Artificial neural network classification of two-dimensional infrared spectroscopy to facilitate high-throughput screening of allosteric effectors
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Autumn 2024
DOI: 10.25820/etd.007575
Abstract
Allosteric effectors of enzymes offer many advantages over their orthosteric counterparts, such as increased specificity and selectivity. However, orthosteric pharmaceuticals significantly outnumber allosteric pharmaceuticals when considering FDA approved drugs. One reason for this is the relative ease of screening for potential orthosteric effectors, where all that is required is a binding study. In contrast, potential allosteric effectors must be subject to not only a binding study, but also an assay for altered activity. This significantly increases the time, effort, and resources required to identify potential allosteric effectors.
This thesis investigates the use of two-dimensional infrared spectroscopy (2D IR) as a high-throughput screening method for allosteric effectors. Traditionally, 2D IR would be too slow to be considered high-throughput, and so this work attempts to ascertain the minimal amount of data necessary to successfully classify a 2D IR spectrum of a potential effector as either a hit or a miss.
To facilitate this classification, I present the use of artificial neural networks. I first demonstrate that neural networks can successfully categorize samples based on their 2D IR spectra, even on reduced datasets. I then show that classification is still possible even when the spectral differences between samples are small and only a single 2D IR datapoint is used. Additionally, I identify trends in terms of which datapoints result in increased classification ability. I finally end with a direct experimental analogue to high-throughput screening of allosteric effectors and show that high accuracy classification is possible on a similar timescale to the state of the art for high-throughput screening of orthosteric effectors.
Details
- Title: Subtitle
- Artificial neural network classification of two-dimensional infrared spectroscopy to facilitate high-throughput screening of allosteric effectors
- Creators
- Evan Bradley Schroeder
- Contributors
- Christopher Cheatum (Advisor)Aditi Bhattacherjee (Committee Member)Johna Leddy (Committee Member)Claudio Margulis (Committee Member)Alexei Tivanski (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Chemistry
- Date degree season
- Autumn 2024
- DOI
- 10.25820/etd.007575
- Publisher
- University of Iowa
- Number of pages
- xv, 114 pages
- Copyright
- Copyright 2024 Evan Bradley Schroeder
- Language
- English
- Date submitted
- 11/30/2024
- Description illustrations
- illustrations, graphs, tables
- Description bibliographic
- Includes bibliographical references (pages 110-114).
- Public Abstract (ETD)
- Enzymes are essential to life, as they speed up chemical reactions in organisms that would otherwise run too slowly. However, sometimes it is necessary to alter how an enzyme functions. For example, a pharmaceutical company may want to develop a drug that can “turn off” the enzymes that provide cancer cells with energy. Almost all drugs on the market that effect enzymes function by binding to the enzyme at the location where the actual chemical reactions take place. However, there is another type of drug that binds elsewhere on the enzyme but can still alter how the enzyme functions. This is called an “allosteric” binder, and they offer some distinct advantages compared to their counterparts. Unfortunately, there are almost no allosteric drugs on the market. This is because it takes much longer to screen for allosteric drugs compared to their cousins, which makes them undesirable to pursue for pharmaceutical companies. In this work, I present a method that has the potential to screen for allosteric drugs at a rate just as fast as other drugs. Two-dimensional infrared spectroscopy (2D IR) is an incredibly useful technique as it can report how a drug influences an enzyme, allosteric or otherwise. This research uses 2D IR to capture information about the drug – enzyme interactions, and then uses artificial neural networks to determine if the drug is a candidate for further study. The results of these studies provide an avenue for screening a category of pharmaceutical that is virtually unrepresented in the current drug market.
- Academic Unit
- Chemistry
- Record Identifier
- 9984774548502771
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