期刊
BIOINFORMATICS
卷 30, 期 14, 页码 1983-1990出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu167
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资金
- Department of Biotechnology, Government of India [BT/PR7150/BID/7/424/2012]
Motivation: Distinguishing between amyloid fibril-forming and amorphous beta-aggregating aggregation-prone regions (APRs) in proteins and peptides is crucial for designing novel biomaterials and improved aggregation inhibitors for biotechnological and therapeutic purposes. Results: Adjacent and alternate position residue pairs in hexapeptides show distinct preferences for occurrence in amyloid fibrils and amorphous beta-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 beta-aggregating hexapeptides with almost 100 percent accuracies in validation tests performed using non-redundant datasets. Conclusion: Accuracies of the predictions made by GAP are significantly improved compared with other methods capable of predicting either general beta-aggregation or amyloid fibril-forming APRs. This work demonstrates that amino acid side chains play important roles in determining the morphological fate of beta-mediated aggregates formed by short peptides.
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