Allostery is the phenomenon of change in one part of a protein affecting a distant part, and it is believed to be present in most proteins. In spite of its importance, we are still far from fully understanding how proteins achieve this long-distance communication. One way we can understand allostery is to observe how certain parts of a protein move in concert with others over time. This means that we follow the correlations of changes in time and from this we conclude that those changes that are close in time must somehow be connected in the so-called allosteric pathways. But since proteins are extremely complex molecules with many atoms, and protein simulations have millions of individual steps, it is necessary to find a way to find those that are connected from this sea of unrelated motions. Our studies focus on understanding allostery in one class of enzymes, purine nucleoside phosphorylases (PNPs), with a particular emphasis on hexameric PNPs in prokaryotic organisms and trimeric PNPs in eukaryotic organisms.1 As part of the ongoing project named Allosteric communication pathways in oligomeric enzymes (ALOKOMP2), an innovative way of monitoring the movement of proteins in time was developed through time-dependent diagrams capable of displaying huge numbers of points. The ALOKOMP project is specifically dedicated to understanding the nature of allostery and allosteric communication. A special database was constructed for this purpose and it combines static structures obtained from the Protein Data Bank (PDB)3 with dynamic data derived from molecular dynamics (MD) simulations. To handle the extensive data in the database, we leverage programmatic methods and visualization techniques that capture the underlying relationships within the data. Several analyses were performed, including structural alignment on the static data, as well as analysis of Ramachandran angles4 and correlations between them on the dynamic data. Novel time-dependent Ramachandran diagrams 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. The findings pave the way for further investigations into the functional implications of allostery and the development of potential therapeutic interventions. 1. Narczyk, M., Wojtys, M.I., Leščić Ašler, I., Žinić, B., Luić, M., Jagusztyn-Krynicka, E.K., Štefanić, Z., Bzowska, A. (2022) J. Enzyme Inhib. Med. Chem. 37:1 1083–1097. 2. https://alokomp.irb.hr/ (funded by Croatian Science Foundation, Grant number IP-2019-04-6764) 3. Berman, H., Henrick, K., Nakamura, H. (2003) Nat. Struc. Mol. Biol. 10 980. 4. Ramachandran, G.N., Ramakrishnan, C., Sasisekharan, V. (1963). J. Mol. Biol. 7 95–99.