To decipher how proteins perform their roles within biology, it is necessary to investigate their dynamical nature. Proteins’ functional movements can be altered in a process called allostery, a phenomenon ubiquitous in nature and leveraged in biotechnology. Thanks to advancements in computing power, computational scientists can now investigate protein allostery at an atomistic level of detail via molecular simulations. In this work, various studies on protein allostery, based on molecular dynamics simulations data, that leverage advanced computational techniques, from machine learning to stochastic modeling and structural analyses, are presented.
The first part of this dissertation focuses on examining allostery in light-oxygen-voltage (LOV) domain proteins, a class of protein sensors used by a variety of organisms to couple and integrate environmental stimuli to cell responses. Specifically, the unique dynamical nature of light-induced allostery activation in the LOV domain of the circadian photoreceptor ZEITLUPE is delineated and an alternative activation mechanism that does not rely on the active Gln is explored.
The second part of this work focuses on studying the allosteric potential of the angiotensin-converting enzyme 2, a regulator of blood pressure and a main target for a variety of coronaviruses. The ability of surface allosteric binders to affect the conformational space explored by the protein is illustrated. The custom deep learning model, named CV-CNN, in conjunction with the REDAN analysis, unraveled the path of allosteric propagation within the protein domain at an atomistic level of detail.
Lastly, the feasibility of a recently developed dimensionality reduction technique called UMAP in studying protein dynamics and allostery is evaluated.
Biochemistry, Biophysics, Chemistry, Molecular Biology
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This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Trozzi, Francesco, "Mechanistic Insights into Protein Allostery in LOV Domains and ACE2 PD via Computational Approaches" (2022). Chemistry Theses and Dissertations. 31.
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