微泡
分泌途径
内质网
分泌物
高尔基体
分泌蛋白
计算生物学
生物
深度学习
人工智能
外体
卷积神经网络
蛋白质测序
计算机科学
肽序列
细胞生物学
生物化学
基因
小RNA
作者
Yonglin Zhang,Lezheng Yu,Ming Yang,Bo Han,Jie Luo,Runyu Jing
标识
DOI:10.1002/pmic.202300184
摘要
Abstract Unconventional secretory proteins (USPs) are vital for cell‐to‐cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome‐mediated secretory proteins is crucial for gaining valuable insights into the regulation of non‐classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.
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