In silico modelling and structural dynamics of PSY1R kinase domain and its interaction with SERK1 kinase

Abstract

Due to plants’ immobility, they are continuously faced with different challenges from their surroundings that also include a wide range of pathogens dealing in the environment. Hence by unraveling the mystery behind how plants tackle all these adversities and understanding the theory behind it would be a great step towards comprehending the mechanism that makes plant disease resistant. The two most important ways by which defenses are activated in plants are by structural interaction between the pathogen-associated molecular pattern (PAMP) known as the pattern-triggered immunity (PTI) and secondly via effectors known as effector-triggered immunity (ETI). In PTI, PRRs detecting PAMPs are employed along with the co-receptor proteins to combat the pathogens. PSY1R, a leucine-rich repeat receptor-like kinase ( LRR-RLK) which has only been shown to act to regulate cell expansion and growth previously, has been recently found to have an important role in the immunity system of the plant by impacting in an antagonistic manner on the plant triggered immunity (PTI). This research aims to acquire a better understanding of the 3D structure of PSY1R receptor kinase and to predict its probable active sites for developing a clear perception of its impact on plant immunity by observing its interaction with receptor SERK1 kinase. Keeping this purpose in mind, modelling of these kinase proteins followed by docking and molecular dynamics (MD) simulation using GROMACS software suite have been performed. Different tools and servers have been used for the modelling of PSY1R and SERK1 kinase generating a number of 3D models. After undergoing verification processes, followed by observing the RMSD and radius of gyration graph at 5ns MD simulation the best model for PSY1R kinase and SERK1 kinase have been selected for docking. The docking result shows that PSY1R and SERK1 kinases interact on different levels with forming different kinds of bonds both before and after the MD simulation conducted at 10ns. The interactions between the two proteins were also analyzed using the protein interaction calculator (PIC) and it has been found that after the MD simulation the number of hydrogen bonds formed between them almost became half. Starting with 57 H-bonds before the simulation, whereas only 21 afterward. These changes are significant indicators of conformational changes that take place over the simulation period and are vital in understanding the early events of PTI by the receptor kinase protein.