Data-driven peptide drug discovery and design

Mi, Xuenan

Permalink

https://hdl.handle.net/2142/129159 Copy

Description

Title

Data-driven peptide drug discovery and design

Author(s)

Mi, Xuenan

Issue Date

2025-01-13

Director of Research (if dissertation) or Advisor (if thesis)

Shukla, Diwakar

Doctoral Committee Chair(s)

Shukla, Diwakar

Committee Member(s)

• Mitchell, Douglas Alan
• Pogorelov, Taras
• Jackson, Nicholas

Department of Study

School of Molecular & Cell Bio

Discipline

Biophysics & Quant Biology

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Ribosomally synthesized and post-translationally modified peptides
• Deep Learning
• Molecular Dynamics

Abstract

Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a diverse class of natural products that have obtained significant attention in recent years due to their broad spectrum of biological activities. These peptides undergo extensive post-translational modifications, which impart unique structures not achievable through natural ribosomal peptides. Such modifications often restrict conformational flexibility, enhancing target recognition, metabolic and chemical stability, and functional diversity. RiPPs are particularly attractive for engineering applications, as their precursor peptides are genetically encoded, and many biosynthetic enzymes exhibit substrate tolerance. Despite their potential, the discovery and design of novel RiPPs for therapeutic applications remain challenging due to a limited understanding of the substrate tolerance mechanisms in RiPP synthetases and the folding processes involved in their biosynthesis. In this dissertation, I have employed computational approaches, including deep learning and molecular dynamics (MD) simulations, to advance peptide drug discovery and design. My research primarily focuses on two natural peptide families: lasso peptides and lanthipeptides. For lasso peptides, I developed a specialized language model, LassoESM, leveraging advancements in pre-trained protein language models to enhance lasso peptide-related properties prediction. Furthermore, I conducted extensive atomic-level MD simulations to investigate the universal folding mechanisms of 20 structurally characterized lasso peptides. Using a deep learning architecture based on Variational Autoencoder, I successfully identified distinct folding pathways for these peptides. For lanthipeptides, I utilized MD simulations to explore the site-selectivity of lanthipeptide cyclization, uncovering critical insights into the molecular mechanisms underlying their biosynthesis. Additionally, I addressed the challenges posed by limited data in peptide discovery and protein-peptide interaction prediction by implementing a deep learning framework based on one-shot learning architecture. Overall, I anticipate that LassoESM and its future iterations will serve as valuable tools for the rational design of lasso peptides with tailored properties. Insights into the folding mechanisms of lasso peptides and the site-selectivity of lanthipeptide cyclization will deepen our understanding of their biosynthetic processes. Moreover, one-shot learning provides a promising framework for accurately predicting peptide-protein interactions, particularly in low-data scenarios.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129159

Copyright and License Information

Copyright 2025 Xuenan Mi

Owning Collections