Errico Silvia, Fani Giulia, Ventura Salvador, Schymkowitz Joost, Rousseau Frederic, Trovato Antonio, Vendruscolo Michele, Bemporad Francesco, Chiti Fabrizio
Department of Experimental and Clinical Biomedical Sciences "Mario Serio", Section of Biochemistry, University of Florence, 50134 Florence, Italy.
Institut de Biotecnologia i de Biomedicina (IBB) and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain.
Biochem J. 2025 Jan 22;482(2):87-101. doi: 10.1042/BCJ20240602.
Advances in solid-state nuclear magnetic resonance (ssNMR) spectroscopy and cryogenic electron microscopy (cryoEM) have revealed the polymorphic nature of the amyloid state of proteins. Given the association of amyloid with protein misfolding disorders, it is important to understand the principles underlying this polymorphism. To address this problem, we combined computational tools to predict the specific regions of the sequence forming the β-spine of amyloid fibrils with the availability of 30, 83 and 24 amyloid structures deposited in the Protein Data Bank (PDB) and Amyloid Atlas (AAt) for the amyloid β (Aβ) peptide, α-synuclein (αS), and the 4R isoforms of tau, associated with Alzheimer's disease, Parkinson's disease, and various tauopathies, respectively. This approach enabled a statistical analysis of sequences forming β-sheet regions in amyloid polymorphs. We computed for any given sequence residue n the fraction of PDB/AAt structures in which that residue adopts a β-sheet conformation (Fβ(n)) to generate an experimental, structure-based profile of Fβ(n) vs n, which represents the β-conformational preference of any residue in the amyloid state. The peaks in the respective Fβ(n) profiles of the three proteins, corresponding to sequence regions adopting more frequently the β-sheet structural core in the various fibrillar structures, align very well with the peaks identified with five predictive algorithms (ZYGGREGATOR, TANGO, PASTA, AGGRESCAN, and WALTZ). These results indicate that, despite amyloid polymorphism, sequence regions most often forming the structural core of amyloid have high hydrophobicity, high intrinsic β-sheet propensity and low electrostatic charge across the sequence, as rationalised and predicted by the algorithms.
固态核磁共振(ssNMR)光谱学和低温电子显微镜(cryoEM)的进展揭示了蛋白质淀粉样状态的多态性本质。鉴于淀粉样蛋白与蛋白质错误折叠疾病的关联,了解这种多态性背后的原理很重要。为了解决这个问题,我们结合计算工具,利用蛋白质数据库(PDB)和淀粉样蛋白图谱(AAt)中分别存储的30个、83个和24个淀粉样β(Aβ)肽、α-突触核蛋白(αS)以及与阿尔茨海默病、帕金森病和各种tau蛋白病相关tau蛋白4R异构体的淀粉样结构,预测形成淀粉样纤维β-脊柱的序列特定区域。这种方法能够对淀粉样多态体中形成β-折叠区域的序列进行统计分析。我们针对任何给定的序列残基n,计算该残基采用β-折叠构象的PDB/AAt结构的比例(Fβ(n)),以生成基于实验结构的Fβ(n)与n的图谱,该图谱代表淀粉样状态下任何残基的β-构象偏好。这三种蛋白质各自的Fβ(n)图谱中的峰值,对应于在各种纤维状结构中更频繁采用β-折叠结构核心的序列区域,与通过五种预测算法(ZYGGREGATOR、TANGO、PASTA、AGGRESCAN和WALTZ)识别出的峰值非常吻合。这些结果表明尽管存在淀粉样多态性,但最常形成淀粉样结构核心的序列区域具有高疏水性、高内在β-折叠倾向以及整个序列的低静电荷,正如算法所合理说明和预测的那样。