One way we can try to understand the phenomenon of allostery in proteins is to observe how certain parts of a protein move in correlation with others over time. Since proteins are vey complex molecules, and protein MD simulations have millions of individual steps, we have to find the way to observe a small portion of those connected motions from sea of unrelated motions. The ALOKOMP project [1] is specifically dedicated to understanding the nature of allostery and allosteric communication in one class of enzymes (PNPs) [2]. To handle the extensive data in the database, we leverage programmatic methods and visualization techniques that capture the underlying relationships within the data. Novel time-dependent Ramachandran diagrams (Figure 1.) were created to capture the conformational changes of individual amino acids throughout the trajectory. By combining these diagrams for all amino acids, a comprehensive depiction of the protein's conformational changes was achieved. Correlation analysis was conducted on the phi and psi angles of the amino acids, revealing correlated and anti-correlated movements within the protein. As expected, a significant portion of the protein exhibited random movement, but also some underlying patterns were observed, indicating possible hubs of allosteric communication. Throughout the study, various Python visualization tools were utilized to analyze and effectively present the obtained data. The research contributes to the understanding of allostery in PNPs and provides valuable insights into the structural dynamics of these enzymes.