Department of Crystallography and Biophysics, University of Madras, Chennai 600025, India, Biotherapeutics Pharmaceutical Sciences, Pfizer Inc., Chesterfield, MO 63017, USA and Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, India.
Bioinformatics. 2014 Jul 15;30(14):1983-90. doi: 10.1093/bioinformatics/btu167. Epub 2014 Mar 28.
Distinguishing between amyloid fibril-forming and amorphous β-aggregating aggregation-prone regions (APRs) in proteins and peptides is crucial for designing novel biomaterials and improved aggregation inhibitors for biotechnological and therapeutic purposes.
Adjacent and alternate position residue pairs in hexapeptides show distinct preferences for occurrence in amyloid fibrils and amorphous β-aggregates. These observations were converted into energy potentials that were, in turn, machine learned. The resulting tool, called Generalized Aggregation Proneness (GAP), could successfully distinguish between amyloid fibril-forming and amorphous β-aggregating hexapeptides with almost 100 percent accuracies in validation tests performed using non-redundant datasets.
Accuracies of the predictions made by GAP are significantly improved compared with other methods capable of predicting either general β-aggregation or amyloid fibril-forming APRs. This work demonstrates that amino acid side chains play important roles in determining the morphological fate of β-mediated aggregates formed by short peptides.
区分蛋白质和肽中的淀粉样纤维形成和无定形β-聚集倾向区域（APR）对于设计新型生物材料和改进生物技术和治疗目的的聚集抑制剂至关重要。
六肽中的相邻和交替位置残基对在淀粉样纤维和无定形β-聚集物中的出现具有明显的偏好。这些观察结果被转化为能量势，然后进行机器学习。由此产生的工具称为广义聚集倾向（GAP），可以成功区分形成淀粉样纤维的六肽和无定形β-聚集的六肽，在使用非冗余数据集进行的验证测试中，准确率几乎达到 100％。
与能够预测一般β-聚集或淀粉样纤维形成 APR 的其他方法相比，GAP 做出的预测的准确性有了显著提高。这项工作表明，氨基酸侧链在确定由短肽形成的β介导的聚集物的形态命运方面起着重要作用。
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