Developing and Applying Computational Methods on Biomolecules
Abstract
Computational biophysics is an interdisciplinary subject that uses numerical algorithms to study the physical principles underlying biological phenomena and processes. Electrostatic interactions play an important role in computational molecular biophysics and their potential impact on disease mechanisms. At distances larger than several Angstroms, electrostatic interactions dominate all other forces, while the alteration of short-range electrostatic pairwise interactions can also have significant effects. The dual nature of electrostatic interactions, being dominant at long-range and specific at short-range, underscores their profound implications for wild-type structure and function. Any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. During my doctoral research, I utilized a diverse range of multi-scale computational techniques, such as structural modeling, molecular dynamics simulations, and electrostatic analysis, to investigate various biological phenomena. My research involved studying the kinetic cycle of myosin and the activation mechanism of Janus Kinases (JAKs). Additionally, I built upon my prior experience developing the Hybridizing Ions Treatment (HIT) method by implementing machine learning to create an improved and more user-friendly version of HIT, called HIT-2. Sudden cardiac death is responsible for half of all deaths related to cardiovascular diseases. Understanding the mechanism of the kinetic cycle of cardiac myosin is crucial for developing protective measures and drugs for the heart. The change in state from rigor to post-rigor is key to explaining the binding and dissociation of myosin. Using β-cardiac myosin in rigor and post-rigor states, I modeled actomyosin complexes and found that there are fewer interactions and lower electrostatic binding strength in the post-rigor state compared to the rigor state. In the post-rigor state, there were higher levels of free binding energy, fewer salt bridges, and fewer hydrogen bonds, resulting in a lower binding affinity compared to the rigor state. This decrease in binding affinity creates important conditions for the dissociation of myosin from the actin filament. The findings of this study on the mechanism of the myosin kinetic cycle provide a novel direction for future research on genetic diseases. The family of Janus Kinases associated the JAK-STAT signaling pathway plays a vital role in the regulation of various cellular processes. A model of the inhibited full-length JAK1 and the energies of JAK1 with Tyrosine Kinase domain (TK) at different positions were calculated. Also, dynamic programming was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of the different mutations, the reasons why these mutations lead to negative or positive regulation of JAK1 activation are illustrated. Besides, the effects of phosphorylation of tyrosine in TK on activation process and ATP hydrolysis are also investigated and studied. Hybridizing Ions Treatment-2 (HIT-2) is used to model biomolecule-bound ions using the implicit solvation model. By modeling ions, HIT-2 allows the user to calculate important electrostatic features of the biomolecules. HIT-2 applies an efficient algorithm to calculate the position of bound ions from molecular dynamics simulations. Modeling parameters were optimized by machine learning methods from thousands of datasets. The optimized parameters produced results with errors lower than 0.2 Å. The testing results in molecular dynamics simulations also proved that HIT-2 can effectively identify bound ion types, numbers, and positions. HIT-2 can significantly improve electrostatic calculations for many problems in computational biophysics.
Subject Area
Biophysics|Computational chemistry
Recommended Citation
Sun, Shengjie, "Developing and Applying Computational Methods on Biomolecules" (2023). ETD Collection for University of Texas, El Paso. AAI30521708.
https://scholarworks.utep.edu/dissertations/AAI30521708