Time dependent dihedral angle oscillations of the spike protein of SARS-CoV-2 reveal favored frequencies of dihedral angle rotations.

The spike protein of SARS-CoV-2 is critical to viral infection of human host cells which ultimately results in COVID-19. In this study we analyzed the behavior of dihedral angles (phi and psi) of the wild-type spike protein over time from molecular dynamics and identified that their oscillations are dominated by a few discrete, relatively low frequencies in the 23-63 MHz range with 42.969 MHz being the most prevalent frequency sampled by the oscillations. We thus observed the spike protein to favor certain frequencies more than others. Gaps in the tally of all observed frequencies for low-abundance amino acids also suggests that the frequency components of dihedral angle oscillations may be a function of position in the primary structure since relatively more abundant amino acids lacked gaps. Lastly, certain residues identified in the literature as constituting the inside of a druggable pocket, as well as others identified as allosteric sites, are observed in our data to have distinctive time domain profiles. This motivated us to propose additional residues with similar time domain profiles, which may be of potential interest to the vaccine and drug design communities for further investigation. Thus these findings indicate that there is a particular frequency domain profile for the spike protein hidden within the time domain data and this information, perhaps with the suggested residues, might provide additional insight into therapeutic development strategies for COVID-19 and beyond.

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach.

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.