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This thesis illuminates the critical roles biomolecules, from small molecules to proteins, play in cellular functionality, particularly highlighting their conformational changes in response to environmental cues or binding events-a cornerstone concept in drug design as well as the manifestations of disease. It explores the conformational flexibility of small molecules and proteins, essential for predicting drug interactions and understanding biological processes. Through advanced molecular dynamics simulations and enhanced sampling techniques, this research offers unprecedented insights into the structural dynamics of three distinct biomolecular systems: the capsid assembly modulator AT130, the passenger domain of pertactin, and the SARS-CoV-2 spike protein. Each system represents a unique facet of biological complexity, underscoring the thesis's contribution to our understanding of biomolecular behavior across various scales. Furthermore, the thesis advances the field by updating the Force Field Toolkit for improved simulation accuracy. This work not only showcases the adaptability and importance of simulation techniques in modern biological research but also paves the way for novel therapeutic strategies by deepening our understanding of biomolecular dynamics.
Sommario
Chapter 1: Introduction and Background.- Chapter 2: Parameterization of a drug molecule with a halogen-hole particle using ffTK: Implementation, testing, and comparison.- Chapter 3: Uncovering the folding mechanism of pertactin: A comparative study of isolated and vectorial folding.- Chapter 4: SARS-CoV-2 spike opening dynamics and energetics reveal the individual roles of glycans and their collective impact.- Chapter 5: Conclusions and Future Work.
Info autore
Yui Tik (Andrew) Pang was born in Hong Kong and received the degrees of Bachelor of Science in Physics and Master of Philosophy in Physics from the Chinese University of Hong Kong in 2012 and 2015, respectively. Andrew's primary research interest lies in the field of Biophysics. However, his academic curiosity extends beyond this, as he also possesses a robust interest in Computer Science and Parallel Computing. His diverse interests allow him to approach his research from a unique interdisciplinary perspective, combining the principles of Physics and Computer Science to explore and solve complex biological problems. His latest endeavor involves integrating deep learning techniques with molecular mechanics calculations to predict novel protein conformations.
