蛋白质折叠
蛋白质聚集
淀粉样纤维
计算生物学
分子动力学
淀粉样蛋白(真菌学)
折叠(DSP实现)
功能(生物学)
亨廷顿蛋白
化学
生物
淀粉样β
生物化学
医学
细胞生物学
疾病
基因
工程类
电气工程
病理
突变体
无机化学
计算化学
作者
Aziza Rahman,Bondeepa Saikia,Chimi Rekha Gogoi,Anupaul Baruah
标识
DOI:10.1016/j.pbiomolbio.2022.08.007
摘要
Aberrant protein folding known as protein misfolding is counted as one of the striking factors of neurodegenerative diseases. The extensive range of pathologies caused by protein misfolding, aggregation and subsequent accumulation are mainly classified into either gain of function diseases or loss of function diseases. In order to seek for novel strategies for treatment and diagnosis of neurodegenerative diseases, insights into the mechanism of misfolding and aggregation is essential. A comprehensive knowledge on the factors influencing misfolding and aggregation is required as well. An extensive experimental study on protein aggregation is somewhat challenging due the insoluble and noncrystalline nature of amyloid fibrils. Thus there has been a growing use of computational approaches including Thermodynamic Integration, Monte Carlo simulation, docking simulation, Molecular dynamics simulation in the study of protein misfolding and aggregation. The review presents a discussion on molecular dynamics simulation alone as to how it has emerged as a promising tool in the understanding of protein misfolding and aggregation in general, detailing upon three different aspects considering four misfold prone proteins in particular. It is noticeable that all four proteins considered in this review i.e prion, superoxide dismutase1, Huntingtin and Amyloid beta are linked to chronic neurodegenerative diseases with debilitating effects. Initially the review elaborates on the factors influencing the misfolding and aggregation. Next, it addresses our current understanding of the amyloid structures and the associated aggregation mechanisms, finally, summarizing the contribution of this computational tool in the search for therapeutic strategies against the respective protein-deposition diseases.
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