Bioinformatic identification of previously unrecognized amyloidogenic proteins.
Bioinformatic identification of previously unrecognized amyloidogenic proteins.
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DOI：
10.1016/j.jbc.2022.101920
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发表时间：
2022-05
影响因子：
4.8
通讯作者：
Eisenberg, David S.
中科院分区：
文献类型：
--
作者：
Rosenberg, Gregory M.;Murray, Kevin A.;Salwinski, Lukasz;Hughes, Michael P.;Abskharon, Romany;Eisenberg, David S.
蛋白质的低复杂性结构域（LCD）已被证明是自缔合的，并且这些结构域内的致病性突变常常驱使蛋白质进入与疾病相关的淀粉样蛋白聚集。这些结构域可能特别容易发生淀粉样蛋白突变，因为它们通常本质上是无序的，并且在自缔合中起作用。因此，问题出现了是否在人类蛋白质组的LCD中寻找致病突变可以导致与淀粉样疾病相关的其他蛋白质的鉴定。在这里，我们采取计算的方法来确定记录的致病突变液晶显示器内，可能有利于淀粉样蛋白的形成。使用这种方法，我们确定了许多已知的淀粉样蛋白突变，包括几个这样的突变蛋白质以前未被确定为淀粉样蛋白。在后一组中，我们专注于TRK融合基因蛋白（TFG）中的两个突变，已知在蛋白质分泌和先天免疫中发挥作用，这与两种不同的周围神经病变相关。我们发现这两种突变都增加了TFG形成淀粉样纤维的倾向。因此，我们得出结论，TFG是一种新的淀粉样蛋白，并提出与其突变形式相关的疾病可能是淀粉样变性。
Low-complexity domains (LCDs) of proteins have been shown to self-associate, and pathogenic mutations within these domains often drive the proteins into amyloid aggregation associated with disease. These domains may be especially susceptible to amyloidogenic mutations because they are commonly intrinsically disordered and function in self-association. The question therefore arises whether a search for pathogenic mutations in LCDs of the human proteome can lead to identification of other proteins associated with amyloid disease. Here, we take a computational approach to identify documented pathogenic mutations within LCDs that may favor amyloid formation. Using this approach, we identify numerous known amyloidogenic mutations, including several such mutations within proteins previously unidentified as amyloidogenic. Among the latter group, we focus on two mutations within the TRK-fused gene protein (TFG), known to play roles in protein secretion and innate immunity, which are associated with two different peripheral neuropathies. We show that both mutations increase the propensity of TFG to form amyloid fibrils. We therefore conclude that TFG is a novel amyloid protein and propose that the diseases associated with its mutant forms may be amyloidoses.
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DOI：
10.1126/science.aan6398
发表时间：
2018-02-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
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通讯作者：
Eisenberg DS
影响因子：
16
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通讯作者：
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DOI：
10.1073/pnas.1504822112
发表时间：
2015-06-09
影响因子：
11.1
作者：
Elbaum-Garfinkle, Shana;Kim, Younghoon;Brangwynne, Clifford P.
通讯作者：
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影响因子：
14.9
作者：
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通讯作者：
Hamosh, Ada
DOI：
10.1073/pnas.1908263116
发表时间：
2019-08-20
影响因子：
11.1
作者：
Kedia, Niraja;Arhzaouy, Khalid;Bieschke, Jan
通讯作者：
Bieschke, Jan