Exploring the impact of variations in the mucolipin1 protein that result in mucolipidosis type 4 using the technique of molecular docking and dynamics simulation.
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引用次数: 0
Abstract
Mucolipidosis type IV (MLIV) is classified as an exceptionally autosomal recessive condition due to a change in MCOLN1 that encodes the mucolipin-1 protein. ML-1 is a membrane protein comprising 6 Trans regions, which are situated at the LELs, a serine lipase area, and a nuclear localization sign. The characteristic features of the ML4 patients are mental retardation and skeletal deformities due to an increase in lipid molecules in the brain, other tissues, and organs. The fundamental goal of the work is to identify the most significant amino acid variants via a computational pipeline. The twenty-three amino acid variants that are responsible for the condition were retrieved from the public domain: L106P and L447P amino acid variants, with the following categories: extremely conserved, highly pathogenic, most interfering with protein function, more structurally unstable, and having promising Phenotyping characteristics was scrutinized from the series of bioinformatics tools that denote its significant nature. A docking and dynamics study was initiated to identify the interaction profiling and interatomic simulation between the Native, L106P, and L447P and the ligand ML-SA1 (it was known to ease the fatty acid buildup in lysosomes of cellular models of Mucolipidosis type IV) and had a score of -6.19, -5.12, and -5.21 kcal/mol, followed by a duplicate 100-ns run trajectory results, which assisted in detecting the stable nature of all the complex structures. Hence, this work helps to recognize the significant role of the scrutinized amino acid variants and, on the other hand, the stable nature of the ligand using standard computational tools.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.