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Influence of Amino Acid Properties for Characterizing Amyloid Peptides in Human Proteome

机译:氨基酸性质对人类蛋白质组中淀粉样肽表征的影响

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Amyloidosis denotes the medical disorders associated with deposition of insoluble protein fibrillar aggregates and it is associated with various human diseases. Presence of aggregation prone regions plays an important role in determining the aggregation propensity of a protein, hence understanding the characteristics of these regions is of keen interest in academia and industry. In this work, we have identified 465 aggregation prone regions with 353 unique peptides in human proteome. Evaluation of the performance of available methods for identifying these 353 peptides showed a sensitivity in the range of 15% to 90%. Further, we identified the amino acid properties enthalpy, entropy, free energy and hydrophobicity are important for promoting aggregation. Utilizing these properties, we have developed a model for distinguishing between amyloid forming and non-amyloid peptides, which showed an accuracy of 71% with a balance between sensitivity and specificity. We suggest that the results obtained in this work could be effectively used to improve the prediction performance of existing methods.
机译:淀粉样变性病是指与不溶性蛋白质原纤维聚集物沉积有关的医学疾病,它与各种人类疾病有关。易于聚集的区域的存在在确定蛋白质的聚集倾向中起着重要的作用,因此了解这些区域的特征在学术界和工业界都引起了浓厚的兴趣。在这项工作中,我们确定了人类蛋白质组中的465个倾向于肽聚合的区域和353个独特的肽段。对用于鉴定这353种肽的可用方法的性能评估表明,其灵敏度在15%至90%的范围内。此外,我们确定了焓,熵,自由能和疏水性的氨基酸性质对于促进聚集很重要。利用这些特性,我们开发了一种区分淀粉样蛋白形成和非淀粉样蛋白肽的模型,该模型显示出71%的准确度以及敏感性和特异性之间的平衡。我们建议在这项工作中获得的结果可以有效地用于提高现有方法的预测性能。

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