Protein and Water Modeling in Computer-Aided Drug Discovery
The field of Computer-Aided Drug Design (CADD) is continuously evolving to improve protein modeling, a crucial step in the drug discovery process. However, limitations exist in how CADD accounts for the various configurations a protein can adopt due to different rotamer and protonation states of its residues. This thesis explores advancements in CADD to address this challenge, focusing on protein modeling and water interactions. In Chapter 1, I introduce the drug discovery process with a brief overview of its history, the purpose of FDA clinical trials, and the cost and time duration for bringing a drug to the market. I then introduce the workflow of Computer-Aided Drug Design and how it’s incorporated in drug projects in a pharmaceutical setting. I conclude the chapter by highlighting water modeling in biological systems as well as the importance of rotamer and protonation state assignment in protein modeling. In Chapter 2, I introduce our novel Rotamer and Protonation state Assignment (RAPA) tool. Unlike existing methods, RAPA analyzes local hydrogen bonding environments to identify a broader range of energetically favorable configurations, each with a unique protonation and rotamer assignment for every residue. This approach significantly improves the accuracy of protein modeling for CADD applications, potentially identifying a greater number of viable candidate drug molecules. The chapter further discusses the validation of RAPA's findings through simulations and emphasizes that each configuration remains energetically consistent with the experimental structure. In Chapter 3, I give an overview of water modeling and structural and thermodynamic mapping by the SSTMap tool. I then explain the HSA program in SSTMap, and I introduce the water orientational code which I have written to analyze the most probable water orientations in high density water clusters. In Chapter 4, I discuss the contributions we made towards making publicly available solvation thermodynamic and structural maps of SARS-CoV-2 targets. This work was intended to aid as a resource to the academic and industrial drug design community in their pursuit of identifying small molecule treatments for COVID-19.
Computational Chemistry, Medicinal-Pharmaceutical Chemistry
City University of New York - Graduate Center
Networked Digital Library of Theses and Dissertations
The Graduate School and University Center of The City University of New York