Formation of Protein Networks between Mucins: Molecular Dynamics Study Based on the Interaction Energy of the System

Natalia Kruszewska, Piotr Bełdowski, Piotr Weber, Steven Yuvan, Marcin Drechny and Marcin Kośmieja
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Natalia Kruszewska: Institute of Mathematics and Physics, UTP University of Science and Technology, Kaliskiego 7, PL-85796 Bydgoszcz, Poland
Piotr Bełdowski: Institute of Mathematics and Physics, UTP University of Science and Technology, Kaliskiego 7, PL-85796 Bydgoszcz, Poland
Piotr Weber: Atomic and Optical Physics Division, Department of Atomic, Molecular and Optical Physics, Faculty of Applied Physics and Mathematics, Gdańsk University of Technology, G. Narutowicza 11/12, 80-233 Gdańsk, Poland
Steven Yuvan: Department of Physics, East Carolina University, Greenville, NC 27858, USA
Marcin Drechny: Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, Kaliskiego 7, PL-85796 Bydgoszcz, Poland
Marcin Kośmieja: Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, Kaliskiego 7, PL-85796 Bydgoszcz, Poland

Energies, 2019, vol. 12, issue 18, 1-18

Abstract: Molecular dynamics simulations have been performed for a model aqueous solution of mucin. As mucin is a central part of lubricin, a key component of synovial fluid, we investigate its ability to form cross-linked networks. Such network formation could be of major importance for the viscoelastic properties of the soft-matter system and crucial for understanding the lubrication mechanism in articular cartilage. Thus, the inter- and intra-molecular interaction energies between the residues of mucin are analyzed. The results indicate that the mucin concentration significantly impacts its cross-linking behavior. Between 160 g/L and 214 g/L, there seems to be a critical concentration above which crowding begins to alter intermolecular interactions and their energies. This transition is further supported by the mean squared displacement of the molecules. At a high concentration, the system starts to behave subdiffusively due to network development. We also calculate a sample mean squared displacement and p -variation tests to demonstrate how the statistical nature of the dynamics is likewise altered for different concentrations.

Keywords: mucin; hydrogen and hydrophobic interactions; biopolymers; molecular dynamics; stochastic models; crowding effect; interaction energies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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