Kang Yan, Zhang Huadong, Wang Xinchao, Yang Yun, Jia Qi
National Pilot School of Software, Yunnan University, Kunming, 650091, Yunnan, China; Yunnan Key Laboratory of Software Engineering, China.
National Pilot School of Software, Yunnan University, Kunming, 650091, Yunnan, China.
Anal Biochem. 2024 Jul;690:115491. doi: 10.1016/j.ab.2024.115491. Epub 2024 Mar 7.
Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained promising prediction performance, it is still a significant challenge to accurately detect complex and diverse functions simultaneously with the quick increase of multi-functional bioactive peptides. In contrast to previous research on multi-functional bioactive peptide prediction based solely on sequence, we propose a novel multimodal dual-branch (MMDB) lightweight deep learning model that designs two different branches to effectively capture the complementary information of peptide sequence and structural properties. Specifically, a multi-scale dilated convolution with Bi-LSTM branch is presented to effectively model the different scales sequence properties of peptides while a multi-layer convolution branch is proposed to capture structural information. To the best of our knowledge, this is the first effective extraction of peptide sequence features using multi-scale dilated convolution without parameter increase. Multimodal features from both branches are integrated via a fully connected layer for multi-label classification. Compared to state-of-the-art methods, our MMDB model exhibits competitive results across metrics, with a 9.1% Coverage increase and 5.3% and 3.5% improvements in Precision and Accuracy, respectively.
生物活性肽可以抑制食品中的氧化过程和微生物腐败，并且在治疗各种疾病和紊乱方面发挥重要作用。虽然大多数方法聚焦于单一功能的生物活性肽并取得了有前景的预测性能，但随着多功能生物活性肽的快速增加，同时准确检测其复杂多样的功能仍然是一项重大挑战。与以往仅基于序列的多功能生物活性肽预测研究不同，我们提出了一种新颖的多模态双分支（MMDB）轻量级深度学习模型，该模型设计了两个不同的分支来有效捕捉肽序列和结构特性的互补信息。具体而言，提出了一个带有双向长短期记忆网络（Bi-LSTM）分支的多尺度扩张卷积，以有效建模肽的不同尺度序列特性，同时提出了一个多层卷积分支来捕捉结构信息。据我们所知，这是首次在不增加参数情况下使用多尺度扩张卷积有效提取肽序列特征。来自两个分支的多模态特征通过全连接层进行集成，用于多标签分类。与现有方法相比，我们的MMDB模型在各项指标上均展现出具有竞争力的结果，覆盖度提高了9.1%，精确率和准确率分别提高了5.3%和3.5%。
