Abstract
The physicochemical basis of lubrication of articular cartilage is still not fully understood. However, the synergy between components of the synovial fluid can be a crucial factor that could explain this phenomenon. This work presents a nonlinear data analysis technique named the recurrence method, applied to the system involved two of such synovial fluid’s components: albumin and chondroitin sulfate (CS) immersed in a water environment. This analysis s performed in order to obtain the “statistical fingerprint” of the dynamics of the interaction between the molecules, and to answer whether the variables are more deterministic or more random. The system simulation has been prepared by molecular docking method followed by molecular dynamics simulations. The already mentioned recurrence method has been applied to time series of the energy of binding, and time series of number of intermolecular hydrogen bonds, as these features describe well binding between the two molecules. In detail, the time delay approach and embedded dimension approach have been applied to extract meaningful records from time series. Then, by means of recurrence plots and entropy approach, we discuss the similarities and differences between the molecular systems consisting of CS-4 and CS-6 molecules. Our main finding is higher affinity of chondroitin sulfate IV to albumin as compared with chondroitin sulfate VI.
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Acknowledgements
This work is supported by the internal grant BN-10/19 of the Institute of Mathematics and Physics of the Bydgoszcz University of Science and Technology (PB,AG,NK). This work is also supported by the grant of National Science Centre in Poland (Miniatura Grant) 2019/03/X/ST3/01482 (PW). Calculations were carried out at the Academic Computer Centre in Gdańsk.
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Bełdowski, P., Weber, P., Gadomski, A., Sionkowski, P., Kruszewska, N., Domino, K. (2024). Studies of the Interaction Dynamics in Albumin–Chondroitin Sulfate Systems by Recurrence Method. In: Awrejcewicz, J. (eds) Perspectives in Dynamical Systems II — Numerical and Analytical Approaches. DSTA 2021. Springer Proceedings in Mathematics & Statistics, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-56496-3_5
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