Intrinsically disordered proteins in various hypotheses on the pathogenesis of Alzheimer's and Parkinson's diseases

被引:20
作者
Coskuner, Orkid [1 ]
Uversky, Vladimir N. [2 ,3 ]
机构
[1] Turkish German Univ, Mol Biotechnol, Istanbul, Turkey
[2] Univ S Florida, Morsani Coll Med, Dept Mol Med, Tampa, FL 33620 USA
[3] Russian Acad Sci, Inst Biol Instrumentat, Lab New Methods Biol, Moscow, Russia
来源
DANCING PROTEIN CLOUDS: INTRINSICALLY DISORDERED PROTEINS IN HEALTH AND DISEASE, PT A | 2019年 / 166卷
关键词
AMYLOID-BETA-PEPTIDE; FREE-ENERGY LANDSCAPES; SEQUENCE-BASED PREDICTION; ALPHA-SYNUCLEIN PROTEIN; A-BETA; MEMBRANE-BINDING; FORCE-FIELD; WILD-TYPE; MOLECULAR-DYNAMICS; IN-VITRO;
D O I
10.1016/bs.pmbts.2019.05.007
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Amyloid-beta (A beta) and alpha-synuclein (alpha S) are two intrinsically disordered proteins (IDPs) at the centers of the pathogenesis of Alzheimer's and Parkinson's diseases, respectively. Different hypotheses have been proposed for explanation of the molecular mechanisms of the pathogenesis of these two diseases, with these two IDPs being involved in many of these hypotheses. Currently, we do not know, which of these hypothesis is more accurate. Experiments face challenges due to the rapid conformational changes, fast aggregation processes, solvent and paramagnetic effects in studying these two IDPs in detail. Furthermore, pathological modifications impact their structures and energetics. Theoretical studies using computational chemistry and computational biology have been utilized to understand the structures and energetics of A beta and alpha S. In this chapter, we introduce A beta and alpha S in light of various hypotheses, and discuss different experimental and theoretical techniques that are used to study these two proteins along with their weaknesses and strengths. We suggest that a promising solution for studying A beta and alpha S at the center of varying hypotheses could be provided by developing new techniques that link quantum mechanics, statistical mechanics, thermodynamics, bioinformatics to machine learning. Such new developments could also lead to development in experimental techniques.
引用
收藏
页码:145 / 223
页数:79
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