Effect of a Low Electrostatic Environment on the Helical Structures of Peptides and Proteins Using Flexible Water Models: An In Silico Study

利用柔性水模型研究低静电环境对肽和蛋白质螺旋结构的影响:一项计算机模拟研究

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Abstract

The electrostatic representation of the molecular environment surrounding membrane proteins is a topic that has not been addressed in the field of molecular simulations. The forces produced by such environments play a decisive role in processes such as GPCR activation, molecular recognition between membrane components, and interactions with ligands, directly impacting their dynamics and physiological function. Based on the FBA/ϵ and TIP4P/ϵ(flex) parameters, we have constructed two new flexible water models to produce low dielectric constants in order to study their effect on the structural properties of protein-membrane complexes. These new low electrostatic water (LEw) models were tested on five helical peptides and two helical-type integral membrane proteins (IMPs) by using molecular dynamics simulations and other in silico tools. Our results show that LEw models enhance intramolecular interactions by producing more hydrogen bonds within the protein structures, leading to greater compaction and conservation of their secondary structures. In the case of IMPs, a low electrostatic solvent leads to greater interaction between the transmembrane domains, preventing their opening and structural deformation. Furthermore, although these models increased their interactions with the membrane, an improvement in properties such as thickness, area per lipid, and lateral diffusion was observed. These novel models would enable for a more accurate description and understanding of the various interactions between membrane proteins, potentially leading to the development of more effective drugs targeting these therapeutic targets. Furthermore, this new approach could be applied in the study of more complex membrane models. This work highlights the importance of developing new water models that improve the molecular description of the environment surrounding cell membranes and enable us to generate more reliable computer results.

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