Computational Discovery of Inhibitors for the Oncogenic Protein MKK3 Restricted; Files Only

Abstract

The number of new cancer cases in the US are expected to rise by 31% in 2030. While cancer treatments have greatly improved, there are still many oncogenic pathways that have yet to be discovered. Genomic and artificial intelligence advances have revealed new protein-protein interaction pathways, and mitogen activated protein kinase kinase 3 (MKK3) has been identified as a promising target for treatments. MKK3 is most known for phosphorylating and activating p38, a protein associated with cancer development. However, it has been recently discovered that MKK3 interacts with and activates the major oncogene MYC and causes racial disparity in triple negative breast cancer cases. We are using computational and experimental methods to find inhibitors for both pathways. First, we evaluated AlphaFold and Homology modelling to determine the best predictive protein structure software. It was found that the AlphaFold model was more accurate at predicting a correct structure and the AlphaFold model of MKK3 was therefore used in all computational tests. Type I kinase inhibitors were computationally investigated to inhibit the MKK3/p38 pathway. A pharmacophore model of ATP allowed us to screen large libraries of molecules that revealed many promising compounds that were selective to MKK3. These compounds were then experimentally tested with GST-pulldowns and were shown to inhibit the phosphorylation of p38. In addition, MKK3-MYC inhibitors were investigated through computational methods using SGI-1027, a known inhibitor of MKK3-MYC, as a model. A computational model was created of the MKK3-MYC complex that supported previous experimental data, and this was used to identify promising compounds. Docking and simulation revealed many promising compounds that docked well to MKK3 and were shown to be selective. These compounds were then tested with TR-FRET assays and were shown to inhibit the MKK3-MYC interaction. Overall, we were able to utilize computational methods as an effective system for drug discovery.

Table of Contents

Chapter 1: Introduction: MKK3 as a Target for Cancer Treatments

1.1 Computational Chemistry Methods for Cancer Drug Discovery………………………..……1

1.2 Oncogenic Role of MKK3…………………………………………………………………….4

1.3 MKK3-MYC Protein-Protein Interaction in Aggressive Triple Negative Breast Cancer…….6

1.4 Scope & Aims………………………………………………………………………………..10

Chapter 2: Designing MKK3 Type I Inhibitors

2.1 Introduction…………………………………………………………………………………..11

2.2 Methodology…………………………………………………………………………………11

2.3 Results & Discussion………………………………………………………………………...16

Chapter 3: MKK3-MYC Inhibitor Design and Analysis

3.1 Introduction…………………………………………………………………………………..24

3.2 Methodology…………………………………………………………………………………24

3.3 Results & Discussion………………………………………………………………………...29

Chapter 4: Conclusions………………………………………………………………………...45

References……………………………………………………………………………………….47

About this Honors Thesis

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School	Emory College
Department	Chemistry
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Chemistry, Biochemistry
Parola chiave	Computational Chemistry Cancer Drug discovery
Committee Chair / Thesis Advisor	James Kindt, Emory University Matthew Weinschenk, Emory University Andrey Ivanov, Emory University

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