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201296 https://hdl.handle.net/2286/R.2.N.201296 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 2026-05-01T17:45:20 132 pages Doctoral Dissertation Academic theses en Neff, Brandon Heyden, Matthias Sulc, Petr Singharoy, Abhishek Arizona State University Partial requirement for: Ph.D., Arizona State University, 2025 Field of study: Chemistry In this thesis, I present three computational projects that explore molecular behavior through molecular dynamics (MD) simulations. Each project explores a distinctsystem and set of questions and uses a diverse set of techniques accordingly. The first project investigates binding free energies between phosphate-functionalized molecular tweezers and lysine and arginine residues. To quantify these interactions, I utilized a variety of techniques, including physical pathway approaches, umbrella sampling, and alchemical methods. A significant challenge was addressing slow and problematic degrees of freedom; by expanding upon existing strategies and developing a novel protocol combining them, I was able to achieve accurate and reproducible binding free energy results sensitive enough to distinguish between different ionic concentrations. This work highlights the importance of well-chosen restraint schemes and adequate sampling, and demonstrates how the ion concentration influences predicted binding affinities and can change underlying free energy profiles. In the second study, I explored energy transfer between proteins and their surrounding solvent using non-equilibrium MD simulations. By establishing a non-equilibrium steady-state system and analyzing the vibrational density of states (VDOS), I discovered that a significant amount of energy dissipation occurs via low-frequency modes associated with collective protein motions. Interestingly, the frequency range exhibiting the most effective energy transfer is slightly higher than the lowest-frequency modes due to a larger number of collective motions contributing, despite individual low-frequency modes being intrinsically more efficient. Since protein–solvent energy transfer is biologically essential but not wholly characterized, these insights into frequency-dependent dissipation pathways significantly advance the understanding of underlying molecular mechanisms. The third project focuses on enhancing protein conformational sampling through frequency-selective anharmonic (FRESEAN) mode analysis combined with well-tempered metadynamics. Using low-frequency modes extracted from short, unbiased MD simulations as collective variables, I significantly improved sampling efficiency, capturing large-scale conformational changes across various proteins without requiring prior knowledge of specific transition pathways. Given the fundamental connection between protein dynamics and biological function, this approach provides a robust and reproducible method for generating extensive datasets suitable for machine learning, potentially enabling breakthroughs analogous to AlphaFold in protein structure prediction. Chemistry Computational Chemistry Physical Chemistry Computational Chemistry Free Energy Perturbation Molecular Dynamics Nonequilibrium Unraveling Molecular Behavior: Insights Through Computational Methods