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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Bibliographic Details
Published in:Interdisciplinary sciences : computational life sciences Vol. 17; no. 2; pp. 277 - 292
Main Authors: Cai, Jinling, Zhao, Jianping, Bin, Yannan, Xia, Junfeng, Zheng, Chunhou
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.06.2025
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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
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ISSN:1913-2751
1867-1462
1867-1462
DOI:10.1007/s12539-024-00666-3