Two-dimensional ultraviolet (2DUV) spectroscopic tools for identifying fibrillation propensity of protein residue sequences.
Two-dimensional ultraviolet (2DUV) spectroscopic tools for identifying fibrillation propensity of protein residue sequences.
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DOI：
10.1002/anie.201005093
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发表时间：
2010-12-10
影响因子：
16.6
通讯作者：
Mukamel, Shaul
中科院分区：
文献类型：
--
作者：
Jiang, Jun;Mukamel, Shaul
超过20种神经退行性疾病[1-3]与错误折叠蛋白淀粉样原纤维的形成和沉积有关。[4-8]我们知道，不同系统中的各种淀粉样蛋白具有共同的结构和纤维形成动力学。[9,10]这表明不同淀粉样蛋白疾病存在共同的分子机制。最近的一篇文章认为，“蛋白质聚集的最常见机制是将相对较短的序列片段结合到β-薄片状组装体中”据推测，蛋白质的纤颤倾向在很大程度上取决于它们的序列。[11-13]人们提出了许多理论工具来描述和表征这种序列依赖性。[11-16]他们成功地预测了各种蛋白质序列的纤颤倾向，这将有助于理解蛋白质形成纤颤的原因。淀粉样蛋白原纤维的研究将极大地受益于适当的工具，这些工具将蛋白质的纤颤倾向与理论和实验可获得的物理或化学性质联系起来。然而，目前在评估纤颤倾向的理论工具中可用的参数或因素无法在实验中获得。例如，TANGO[14]、Waltz[11]和Zyggregator[15]计算工具提供了不同的“聚合分数”，这些分数与实验可以获得的物理或化学性质无关。三维轮廓法[13]和PASTA算法[16]是基于蛋白质序列的能量函数。正如艾森伯格等人最近的一项工作所显示的那样，蛋白质形成原纤维的能力可以通过它们的自由能有效地预测。同样，要测量分离的蛋白质片段的自由能是极其困难的，而且目前的实验还不可能在几千卡/摩尔内进行所需的能量区分。此外，上述标准通常依赖于详细的结构信息，由于缺乏合适的原子分辨率探针，大多数聚集体无法获得这些信息。[4,6,7]
More than 20 neurodegenerative diseases [1–3] are associated with the formation and deposition of amyloid fibrils of misfolded proteins.[4–8] Various amyloid-forming proteins across diverse systems are known to share common structures and fibril formation kinetics.[9, 10] This suggests the existence of common molecular mechanisms for different amyloid diseases. A recent article argued that" The most common mechanism by which proteins aggregate consists of the incorporation of relatively short sequence segments into β-sheetlike assemblies".[11] It has been conjectured that the fibrillation propensities of proteins depend strongly on their sequences.[11–13] Many theoretical tools have been proposed to describe and characterize this sequence-dependence.[11–16] Their success in predicting the fibrillation propensity of various protein sequences should help understand why protein form fibrils.The study of amyloid fibrils would greatly benefit from adequate tools that correlate the fibrillation propensity of proteins with physical or chemical properties accessible by both theory and experiment. However, the parameters or factors currently available in the theoretical tools for evaluating the fibrillation propensity are not accessible experimentally. For instance, the TANGO [14], Waltz [11], and Zyggregator [15] computational tools provide different" aggregation scores", which are not connected to physical or chemical properties accessible by experiment. The 3D profile method [13] and the PASTA algorithm [16] are based on the energy function of protein sequences. As shown in a recent work of Eisenberg et. al.,[12] the ability of proteins to form fibrils can be effectively predicted by their free energies. Again, it is extremely hard to measure free energies of separated protein segments, and the required discrimination of energies within several kcal/mol is not possible by current experiments. Moreover, the above criteria normally rely on detailed structural information, which is not available for most aggregates due to the lack of suitable probes with atomic resolution.[4, 6, 7]
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影响因子：
6
作者：
Ajdarzadeh Oskouei, A.;Braem, O.;Chergui, M.
通讯作者：
Chergui, M.
影响因子：
5.6
作者：
Tartaglia, Gian Gaetano;Pawar, Amol P.;Vendruscolo, Michele
通讯作者：
Vendruscolo, Michele
影响因子：
2.9
作者：
Kaufmann, Kristian W.;Lemmon, Gordon H.;DeLuca, Samuel L.;Sheehan, Jonathan H.;Meiler, Jens
通讯作者：
Meiler, Jens
DOI：
10.1073/pnas.0805957106
发表时间：
2009-04-21
影响因子：
11.1
作者：
Shim, Sang-Hee;Gupta, Ruchi;Zanni, Martin T.
通讯作者：
Zanni, Martin T.
DOI：
10.1073/pnas.97.19.10383
发表时间：
2000-09-12
影响因子：
11.1
作者：
Kuhlman, B;Baker, D
通讯作者：
Baker, D