Structural dynamics of LDL receptor interactions with E498A and R499G variants of PCSK9

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Nur Alya Amirah Azhar, Yung-An Chua, Hapizah Nawawi, Siti Azma Jusoh
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引用次数: 0

Abstract

Context

The low-density lipoprotein receptor (LDLR) regulates cholesterol uptake by mediating the hepatic clearance of plasma low-density lipoprotein cholesterol (LDL-C). Proprotein convertase subtilisin/kexin type-9 (PCSK9) attenuates LDLR function by binding to the LDLR, leading to its lysosomal degradation and preventing the total depletion of circulating LDL-C. However, pathogenic PCSK9 variants can reduce LDLR availability, significantly increase plasma LDL-C levels. Despite this understanding, the detailed molecular mechanism of LDLR-PCSK9 interaction remains unclear due to the incomplete LDLR structure. This study uses molecular dynamics (MD) simulations to predict LDLR structural dynamics upon binding to PCSK9. Furthermore, PCSK9 variants, E498A and R499G, that were identified in Malaysian FH patients were investigated for their mutational effects. Throughout the simulations, PCSK9 remained stable, while LDLR explored a larger conformational space. The LDLR-PCSK9 wild-type (WT) complex showed minimal changes, while the LDLR-PCSK9(R499G) complex exhibited pronounced conformational rearrangement. The MM/GBSA analysis revealed that the LDLR-PCSK9(E498A) complex had the highest binding affinity (− 63.81 kcal/mol), followed by the WT complex (− 33.07 kcal/mol), and LDLR-PCSK9(R499G) (− 24.21 kcal/mol). These findings offer novel insights into the dynamic interactions between LDLR and PCSK9, highlighting the role of structural flexibility in their relationship. Further MD simulation studies with the complete LDLR structure as well as experimental validation are needed to elucidate the molecular mechanisms underlying LDLR-PCSK9-mediated cholesterol homeostasis.

Methods

The initial structure of the wild-type (WT) LDLR-PCSK9 complex was obtained from PDB ID 3P5C, and the PCSK9 mutant structures (E498A and R499G) were modeled using the SPDBV program. MD simulations for each complex—LDLR-PCSK9 WT, LDLR-PCSK9(E498A), and LDLR-PCSK9(R499G)—were conducted using the GROMACS package with the CHARMM36m force field. The simulations were performed at 310.15 K with 2-fs timesteps under the isothermal-isobaric (NPT) ensemble, with each run lasting 500 ns. Including triplicates, the total duration of MD simulation time for all complexes amounted to 3.5 μs.

LDL受体与PCSK9的E498A和R499G变体相互作用的结构动力学
低密度脂蛋白受体(LDLR)通过介导血浆低密度脂蛋白胆固醇(LDL-C)的肝脏清除来调节胆固醇摄取。蛋白转化酶subtilisin/ keexin type-9 (PCSK9)通过与LDLR结合,导致其溶酶体降解并阻止循环LDL-C的总消耗,从而减弱LDLR的功能。然而,致病性PCSK9变异可降低ldl - lr可用性,显著增加血浆LDL-C水平。尽管如此,由于LDLR结构不完整,LDLR- pcsk9相互作用的详细分子机制仍不清楚。本研究使用分子动力学(MD)模拟来预测LDLR与PCSK9结合后的结构动力学。此外,我们还研究了在马来西亚FH患者中发现的PCSK9变异E498A和R499G的突变效应。在整个模拟过程中,PCSK9保持稳定,而LDLR探索了更大的构象空间。LDLR-PCSK9野生型(WT)复合物变化不大,而LDLR-PCSK9(R499G)复合物表现出明显的构象重排。MM/GBSA分析结果显示,LDLR-PCSK9(E498A)配合物的结合亲和力最高(- 63.81 kcal/mol),其次是WT配合物(- 33.07 kcal/mol)和LDLR-PCSK9(R499G) (- 24.21 kcal/mol)。这些发现为LDLR和PCSK9之间的动态相互作用提供了新的见解,突出了结构灵活性在它们之间的关系中的作用。为了阐明LDLR- pcsk9介导胆固醇稳态的分子机制,需要进一步的LDLR结构完整的MD模拟研究和实验验证。方法从PDB ID 3P5C中获得野生型(WT) LDLR-PCSK9复合物的初始结构,并使用SPDBV程序对PCSK9突变体E498A和R499G进行建模。每个复合物LDLR-PCSK9 WT、LDLR-PCSK9(E498A)和LDLR-PCSK9(R499G)的MD模拟使用GROMACS包和CHARMM36m力场进行。模拟在310.15 K下进行,时间步长2-fs,在等温-等压(NPT)集合下进行,每次运行持续500 ns。包括重复数在内,所有复合物的MD模拟时间总和为3.5 μs。
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来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
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