iAmyP: A Multi-view Learning for Amyloidogenic Hexapeptides Identification Based on Sequence Least Squares Programming

The development of peptide drug is hindered by the risk of amyloidogenic aggregation; if peptides tend to aggregate in this manner, they may be unsuitable for drug design. Computational methods aimed at predicting amyloidogenic sequences often face challenges in extracting high-quality features, and...

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Vydané v:	Interdisciplinary sciences : computational life sciences Ročník 17; číslo 2; s. 277 - 292
Hlavní autori:	Cai, Jinling, Zhao, Jianping, Bin, Yannan, Xia, Junfeng, Zheng, Chunhou
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Nature Singapore 01.06.2025 Springer Nature B.V
Predmet:	Algorithms Amino Acid Sequence Amino acids Amyloid - chemistry Amyloidogenesis Amyloidosis Biomedical and Life Sciences Computational Biology - methods Computational Biology/Bioinformatics Computational Science and Engineering Computer Appl. in Life Sciences Computer applications Datasets Drug development Health Sciences Humans Hydrophobic and Hydrophilic Interactions Hydrophobicity Learning Least squares Least-Squares Analysis Life Sciences Mathematical and Computational Physics Medicine Methods Oligopeptides - chemistry Optimization Original Research Article Peptides Physicochemical properties Regression analysis Software Statistics for Life Sciences Theoretical Theoretical and Computational Chemistry Feature fusion Amyloidogenic hexapeptide Multi-view learning Sequential least squares
ISSN:	1913-2751, 1867-1462, 1867-1462
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Shrnutí:	The development of peptide drug is hindered by the risk of amyloidogenic aggregation; if peptides tend to aggregate in this manner, they may be unsuitable for drug design. Computational methods aimed at predicting amyloidogenic sequences often face challenges in extracting high-quality features, and their predictive performance can be enchanced. To surmount these challenges, iAmyP was introduced as a specialized computational tool designed for predicting amyloidogenic hexapeptides. Utilizing multi-view learning, iAmyP incorporated sequence, structural, and evolutionary features, performing feature selection and feature fusion through recursive feature elimination and attention mechanisms. This amalgamation of features and subsequent feature selection and fusion lead to optimal performance facilitated by an optimization algorithm based on sequence least squares programming. Notably, iAmyP exhibited robust generalization for peptides with lengths of 7–10 amino acids. The role of hydrophobic amino acids in the aggregation process is critical, and a thorough analysis have significantly enhanced our insight into their significance in amyloidogenic hexapeptides. This tool represented an advancement in the development of peptide therapeutics by providing an understanding of amyloidogenic aggregation, establishing itself as a valuable framework for assessing amyloidogenic sequences. The data and code can be freely accessed at https://github.com/xialab-ahu/iAmyP . Graphical Abstract
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1913-2751 1867-1462 1867-1462
DOI:	10.1007/s12539-024-00666-3