Applying Multi-Scale Computational Approaches to Study Disease Related Biomolecules
Abstract
Computational biophysics plays a significant role in understanding biological processes in various biology systems and provides new sights to investigate disease-related biomolecules. During the doctoral research, I utilized multi-scale computational approaches, including structural modeling, Molecular Dynamics (MD) simulation, electrostatic analysis (DelPhi, DelPhiForce), and machine learning based Hybridizing Ions Treatment-2 (HIT-2) program, to investigate biomolecules. My research includes bound ions effects on kinesin Ncd binding to microtubule, ion concentration effects on kinesin BimC binding affinity, microtubule dynamics, etc.Ions are crucial for biomolecular interactions, especially for highly charged biomolecules. Bound ion effects are difficult to study in implicit solvent models. Based on the machine learning approach, a hybrid solvent method was developed to combine the explicit solvent model with implicit solvent model to study protein-protein interactions. The hybrid approach treats the bound ions explicitly and the free ions implicitly. The work applies the hybrid approach to a kinesintubulin complex, which demonstrates that the bound ions, especially the interfacial bound ions, play significant roles in kinesin-microtubule binding. The hybrid approach is not only capable of handling kinesin-tubulin complexes, but also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.Microtubules are key players in several stages of the cell cycle and are also involved in transportation of cellular organelles. Therefore, understanding the interactions among tubulins is crucial for characterizing microtubule dynamics. Studying microtubule dynamics can help researchers make advances in the treatment of neurodegenerative diseases and cancer. A series of computational approaches were utilized to study the electrostatic interactions at the binding interfaces of tubulin monomers. The calculations explained that due to the electrostatic interactions, the tubulins always preferred to form α/β tubulin dimmers. The interactions between two protofilaments are the weakest, thus the protofilaments are easily separated from each other. The study elucidates some mechanistic details of microtubule dynamics and also identifies important residues at the binding interfaces as potential drug targets for the inhibition of cancer cells.BimC family proteins are bipolar motor proteins belonging to the kinesin superfamily which promote mitosis by crosslinking and sliding apart antiparallel microtubules. Understanding the binding mechanism between BimC and the microtubule is crucial for researchers to make advances in the treatment of cancer and other malignancies. By combining molecular dynamics (MD) simulations with a series of computational approaches, the electrostatic interactions at the binding interfaces of BimC and the microtubule under three different potassium chloride (KCl) concentrations were studied. We found the electrostatic features on the motor domains of BimC provide the strongest attractive interactions to the microtubule at 0 mM KCl compared to the complex at 50 and 150 mM KCl concentrations, which is validated by experimental conclusions. Furthermore, important salt bridges and residues at the binding interfaces of the complexes were identified, which illustrate the details of the BimC/microtubule interactions. The identified important residues involved in salt bridges are potential hot spots of drug targets for designing new drugs to cancer therapy.
Subject Area
Computational physics|Oncology|Microbiology
Recommended Citation
Guo, Wenhan, "Applying Multi-Scale Computational Approaches to Study Disease Related Biomolecules" (2024). ETD Collection for University of Texas, El Paso. AAI31147678.
https://scholarworks.utep.edu/dissertations/AAI31147678