Tag: Marina Guenza

Coursed-Grained Approach for the Protein Dynamics of the SARS-CoV-2 Spike Protein Variants

Presenter: Ruben Sanchez – Biochemistry, Biology

Faculty Mentor(s): Marina Guenza

Session: (In-Person) Poster Presentation

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) utilizes a spike protein to recognize the receptor protein Angiotensin-converting enzyme 2 (ACE2) of human cells to initiate COVID-19. It is known that the spike protein adopts an active (open) conformation from an inactive (closed) conformation to initiate its infectious cycle. But it is unknown whether the different variants have mutations that affect the protein dynamics of the spike protein. It was hypothesized that the amino acid mutations of more transmissible variants will have increased protein dynamics leading to a dramatized Monod-Wyman-Changeux model. Identifying and targeting these dynamics may lead to the development of pharmaceuticals that may inhibit the infectivity of the SARS-CoV-2 virus. Therefore, two variants of the spike protein were analyzed using molecular dynamic simulations and the Langevin Equation for Protein Dynamics (LE4PD) to quantitively analyze residue fluctuation within their respective spike proteins. LE4PD quantified the protein dynamics and demonstrated that the more infectious variants have higher fluctuations in their protein dynamics.

Identifying Areas of Enhanced Flexibility in the SARS-CoV-2 Spike Protein with Computational Methods

Presenter: Sonny Kusaka − Biochemistry

Faculty Mentor(s): Professor Marina Guenza

Session: (Virtual) Oral Panel—Health and Social Science

The SARS-CoV-2 virus responsible for the COVID-19 pandemic has become one of the most well-known and influential viruses of the 21st century. This research utilizes three different computational methods with varying predicted levels of detail both to compare the methods against one another as well as to analyze atomistic molecular dynamics simulations of the SARS-CoV-2 spike protein to look for regions of enhanced flexibility. Previously established theoretical models of protein binding indicate a correlation between local flexibility and increased binding capabilities, the likes of which are of interest because they may be of importance for the protein in performing its biological function. As the computational methods increase in predicted accuracy, so too do the level of detail in the dynamics of the spike protein that they model. These results show enhanced flexibility of the spike protein in the functional regions that have been previously described and published in literature, other flexible regions not previously documented in literature that may be of interest, and promising results for the future of coarse-grain analysis of large multi-subunit proteins.

Theoretical Study of the Molecular Dynamics of Diubiquitin

Presenter: Kimberly Davidson, Chemistry

Poster: A-5

Mentor: Marina Guenza, Chemistry

In eukaryotic cells, polyubiquitin chains attach themselves to proteins that are ready for proteolysis. When the proteolysis pathway is disturbed, diseases such as cancer can result. This study focuses on the molecular dynamics of diubiquitin on a small time scale. Diubiquitin contains two ubiquitin chains connected by an isopeptide bond between Gly76 and Lys48. We used GROMACS to simulate the protein chain for ~10 ns with an average RMSD of ~0.2 nm. A change in RMSD was observed at ~4 ns indicating a conformational change in diubiquitin. Calculation of T1 and T2 values revealed the theoretical spin-relaxation time for each residue. Further study of diubiquitin will be useful in understanding the proteolysis pathway and how disruption can occur.

The Atomistic Reconstruction of Coarse-Grained Polymeric Systems via Machine Learning Techniques

Presenter(s): Jake Olsen—Chemistry and Mathematics

Faculty Mentor(s): Marina Guenza, Jake Searcy

Session 1: It’s a Science Thing

Polymeric systems, things like proteins, DNA, and synthetic plastics, are of great interest for their applications in material design and the biomedical industry . Therefore, having time-efficient and inexpensive approaches to investigate these systems on multiple scales, from the microscopic to the macroscopic level, is of great importance and necessity . Molecular dynamics (MD) simulations are one such tool for investigating these systems; however, MD simulations that simulate polymer systems in their atomistic (AT) representation are unable to reach the time scale necessary so that the system exhibits the correct chain characteristics . Thus, coarse-graining (CG) methods, a process by which the local degrees of freedom are averaged out, are applied to improve upon computational time . Unfortunately, the computational gain is coupled with the loss of statistical information from the CG process . Therefore, to regain the lost AT information the CG trajectories need to be transformed back to an AT representation . This process is known as backmapping . Utilizing state- of-the-art machine learning techniques coupled with AT data, we have developed a backmapping procedure for CG polymeric systems . The model, centered around a recurrent neural network (RNN), shows strong agreement with the AT data across many statistical quantities prompting further investigation and development of the model .