Elucidating Dynamic Mechanisms for Extended Spectrum Antibiotic Resistance in Class A Beta-Lactamase through Machine Learning on Molecular Dynamics Simulations

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Avery, C. (2019). Elucidating Dynamic Mechanisms for Extended Spectrum Antibiotic Resistance in Class A Beta-Lactamase through Machine Learning on Molecular Dynamics Simulations. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Beta-lactamase is a protein which is produced in bacteria and is a primary cause of antibiotic resistance to Beta Lactam antibiotics. Beta Lactams are among the most common types of antibiotics (including penicillin) and thus understanding the resistance conferred to bacteria by beta-lactamase enzymes is a critical step in developing effective drugs. There are many mutants of the beta-lactamase enzyme, each with the same function but different substrate affinities to the various types of antibiotics. We are interested in identifying intrinsic dynamics of apo structures, if any, to elucidate differences in substrate specificity, specifically the motions which can explain how Extended Spectrum beta-lactamases (ESBLs) are able to bind to such a wide variety of substrates. To characterize dynamical differences between mutant proteins, we are employing Essential Dynamics, which is the most commonly employed method to analyze the internal dynamics of proteins, as well as several other supervised machine learning methods. We show that Quadratic Discriminant Analysis, when used with a filtering method to circumvent the multicollinearity problem, can be used to classify structures from the wild type (TEM-1) and ESBL (TEM-52) with greater accuracy than obtained from unsupervised learning. Finally, an in-house supervised learning method is used to identify differences in molecular motion to elucidate mechanisms likely responsible for, at least in part, the experimentally determined differences in binding affinity between the ESBL TEM-52 with respect to the wild type TEM-1.

Details
Author

Avery, Christopher

Title

Elucidating Dynamic Mechanisms for Extended Spectrum Antibiotic Resistance in Class A Beta-Lactamase through Machine Learning on Molecular Dynamics Simulations

Physical Description

1 online resource (98 pages) : PDF

Date

2019

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Beta-lactamase is a protein which is produced in bacteria and is a primary cause of antibiotic resistance to Beta Lactam antibiotics. Beta Lactams are among the most common types of antibiotics (including penicillin) and thus understanding the resistance conferred to bacteria by beta-lactamase enzymes is a critical step in developing effective drugs. There are many mutants of the beta-lactamase enzyme, each with the same function but different substrate affinities to the various types of antibiotics. We are interested in identifying intrinsic dynamics of apo structures, if any, to elucidate differences in substrate specificity, specifically the motions which can explain how Extended Spectrum beta-lactamases (ESBLs) are able to bind to such a wide variety of substrates. To characterize dynamical differences between mutant proteins, we are employing Essential Dynamics, which is the most commonly employed method to analyze the internal dynamics of proteins, as well as several other supervised machine learning methods. We show that Quadratic Discriminant Analysis, when used with a filtering method to circumvent the multicollinearity problem, can be used to classify structures from the wild type (TEM-1) and ESBL (TEM-52) with greater accuracy than obtained from unsupervised learning. Finally, an in-house supervised learning method is used to identify differences in molecular motion to elucidate mechanisms likely responsible for, at least in part, the experimentally determined differences in binding affinity between the ESBL TEM-52 with respect to the wild type TEM-1.

Genre

masters theses

Subjects--Topics

Biophysics
Physics

Degree

M.S.

Keywords

Antibiotic Resistance
Beta Lactamase
Discriminant Analysis
Essential Dynamics
Molecular Dynamics Simulation
Principal Component Analysis

Subject Area

Applied Physics

Advisor(s)

Jacobs, Donald

Committee Members

Trammell, Susan
Nesmelov, Yuri

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2019.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Avery_uncc_0694N_12121

Permalink

http://hdl.handle.net/20.500.13093/etd:2461