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FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence

机译:FoldAmyloid:从蛋白质序列预测淀粉样蛋白生成区域的方法

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Motivation: Amyloidogenic regions in polypeptide chains are very important because such regions are responsible for amyloid formation and aggregation. It is useful to be able to predict positions of amyloidogenic regions in protein chains.Results: Two characteristics (expected probability of hydrogen bonds formation and expected packing density of residues) have been introduced by us to detect amyloidogenic regions in a protein sequence. We demonstrate that regions with high expected probability of the formation of backbone-backbone hydrogen bonds as well as regions with high expected packing density are mostly responsible for the formation of amyloid fibrils. Our method (FoldAmyloid) has been tested on a dataset of 407 peptides (144 amyloidogenic and 263 non-amyloidogenic peptides) and has shown good performance in predicting a peptide status: amyloidogenic or non-amyloidogenic. The prediction based on the expected packing density classified correctly 75% of amyloidogenic peptides and 74% of non-amyloidogenic ones. Two variants (averaging by donors and by acceptors) of prediction based on the probability of formation of backbone-backbone hydrogen bonds gave a comparable efficiency. With a hybrid-scale constructed by merging the above three scales, our method is correct for 80% of amyloidogenic peptides and for 72% of non-amyloidogenic ones. Prediction of amyloidogenic regions in proteins where positions of amyloidogenic regions are known from experimental data has also been done. In the proteins, our method correctly finds 10 out of 11 amyloidogenic regions.
机译:动机:多肽链中产生淀粉样蛋白的区域非常重要,因为此类区域负责淀粉样蛋白的形成和聚集。结果:我们引入了两个特征(预期的氢键形成概率和预期的残基堆积密度)来检测蛋白质序列中的淀粉样蛋白形成区域。我们证明具有高预期的形成骨干-骨架氢键的可能性的区域以及具有高预期的堆积密度的区域主要负责淀粉样蛋白原纤维的形成。我们的方法(FoldAmyloid)已在407个肽(144个产生淀粉样的肽和263个非产生淀粉样的肽)的数据集上进行了测试,并在预测肽状态(产生淀粉样或非淀粉样的)方面表现出了良好的性能。基于预期堆积密度的预测正确地将75%的淀粉样肽和74%的非淀粉样肽分类。基于骨架-主链氢键形成可能性的预测的两种变体(供体和受体平均)提供了可比较的效率。通过将上述三个比例合并而构成的混合比例,我们的方法适用于80%的淀粉样肽和72%的非淀粉样肽。还已经完成了从实验数据中已知淀粉样蛋白生成区域位置的蛋白质中淀粉样蛋白生成区域的预测。在蛋白质中,我们的方法可以正确地在11个淀粉样蛋白生成区域中找到10个。

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