A method for probing the mutational landscape of amyloid structure.
A method for probing the mutational landscape of amyloid structure.
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DOI：
10.1093/bioinformatics/btr238
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发表时间：
2011-07-01
期刊：
影响因子：
--
通讯作者：
Berger B
中科院分区：
文献类型：
--
作者：
O'Donnell CW;Waldispühl J;Lis M;Halfmann R;Devadas S;Lindquist S;Berger B
动机：所有种类的蛋白质都可以自我组装成高度有序的β片聚集体，即淀粉样原纤维，这在生物学和临床上都很重要。然而，纤维的特定分子结构可以根据序列和环境条件发生巨大变化，突变可以极大地改变淀粉样蛋白的功能和致病性。实验结构的确定已经被证明是极其困难的，只有少数基于核磁共振的模型被提出，这表明需要计算方法。结果：我们提出了AmyloidMutants，这是一种用于预测和分析野生型和突变型淀粉样蛋白结构的统计力学方法。基于蛋白质突变景观的前提下，AmyloidMutants大力量化序列突变对原纤维构象和稳定性的影响。在已知化学位移数据的非突变全长淀粉样蛋白结构上进行测试，AmyloidMutants的预测精度比现有工具提高了大约2倍。此外，AmyloidMutants是预测完整的超二级结构的唯一方法，能够准确区分拓扑结构不同的淀粉样蛋白构象，对应于相同的序列位置。AmyloidMutants应用于突变体预测，与最近基于部分化学位移数据的实验模型一致，确定了a β与其高毒性‘ Iowa ’突变体之间的全局构象转换。对突变的、酵母毒性的het - 5菌株的预测表明了类似的交替折叠。当应用于het - 5和核心天冬酰胺被谷氨酰胺取代的het - 5突变体时（这两种突变体在许多淀粉样蛋白中都含有高度淀粉样蛋白的化学相似残基），AmyloidMutants令人惊讶地预测了谷氨酰胺突变体形成淀粉样蛋白的能力大大降低。我们通过诱变实验证实了这一发现。可用性：我们的工具在网站http://amyloid.csail.mit.edu/上公开可用。联系:lindquist_admin@wi.mit.edu;bab@csail.mit.edu补充信息：补充数据可在Bioinformatics网站在线获得。
Motivation: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods. Results: We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic ‘Iowa’ mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments. Availability: Our tool is publically available on the web at http://amyloid.csail.mit.edu/. Contact: lindquist_admin@wi.mit.edu; bab@csail.mit.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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