Abstract
Antibiotics resistance posed a serious threat to the public health and caused huge economic cost. β-Lactamases, which are enzymes produced by bacteria to hydrolyze β-lactam based antibiotics, are one of the driving forces behind antibiotic resistance. To explore the antibiotic resistance effect, understanding the mechanistic and dynamical features of β-lactamases through their interactions with antibiotics is critical. In my doctoral research, I applied both molecular dynamic (MD) simulations and machine learning approaches to explore these crucial interactions. Vancomycin is a typical glycopeptide antibiotic, which inhibits the bacterial cell wall through binding with peptidoglycan (PG). The key interactions of vancomycin and cell wall structure are identified by the conformational distributions of vancomycin and its three derivatives with PG complexes. TEM-1 is a serine-based β-lactamase and can hydrolyze the benzyl penicillin antibiotic. The key residues on TEM-1 are identified by random forest classification models. Moreover, the dynamical motions of four antibiotic resistance related proteins TEM-1, TOHO-1, PBP-A and DD-transpeptidase with a benzyl penicillin are analyzed and compared to explore their evolutionary correlation. I also investigated the petroleum thermal cracking mechanism through quantum chemistry calculations, and provided a quantitative and insightful understanding of thermal cracking processes.
Degree Date
Fall 12-2019
Document Type
Dissertation
Degree Name
Ph.D.
Department
Chemistry
Advisor
Peng Tao
Subject Area
Chemistry
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Wang, Feng, "Bridging Between Protein Dynamics and Evolution Through Simulations and Machine Learning Approaches" (2019). Chemistry Theses and Dissertations. 12.
https://scholar.smu.edu/hum_sci_chemistry_etds/12
