Zhang Hongqi, Liu Shanghua, Su Wei, Xie Xueqin, Yu Junwen, Dao Fuying, Yang Mi, Lyu Hao, Lin Hao
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore.
BMC Biol. 2025 May 28;23(1):142. doi: 10.1186/s12915-025-02243-6.
Neuropeptides (NPs) are critical signaling molecules involved in various physiological and behavioral processes, including development, metabolism, and memory. They function within both the nervous and endocrine systems and have emerged as promising therapeutic targets for a range of diseases. Despite their significance, the accurate identification of NPs remains a challenge, necessitating the development of more effective computational approaches.
In this study, we introduce NeuroScale, a multi-channel neural network model leveraging evolutionary scale modeling (ESM) for the precise prediction of NPs. By integrating the GoogLeNet framework, NeuroScale effectively captures multi-scale NP features, enabling robust and accurate classification. Extensive benchmarking demonstrates its superior performance, consistently achieving an area under the receiver operating characteristic curve (AUC) exceeding 0.97. Additionally, we systematically analyzed the impact of protein sequence similarity thresholds and multi-scale sequence lengths on model performance, further validating NeuroScale's robustness and generalizability.
NeuroScale represents a significant advancement in neuropeptide prediction, offering both high accuracy and adaptability to diverse sequence characteristics. Its ability to generalize across different sequence similarity thresholds and lengths underscores its potential as a reliable tool for neuropeptide discovery and peptide-based drug development. By providing a scalable and efficient deep learning framework, NeuroScale paves the way for future research in neuropeptide function, disease mechanisms, and therapeutic applications.
神经肽（NPs）是参与各种生理和行为过程的关键信号分子，包括发育、代谢和记忆。它们在神经系统和内分泌系统中均发挥作用，并已成为一系列疾病有前景的治疗靶点。尽管其具有重要意义，但神经肽的准确识别仍然是一项挑战，因此需要开发更有效的计算方法。
在本研究中，我们引入了NeuroScale，这是一种利用进化尺度建模（ESM）对神经肽进行精确预测的多通道神经网络模型。通过整合GoogLeNet框架，NeuroScale有效地捕捉了多尺度神经肽特征，实现了稳健且准确的分类。广泛的基准测试证明了其卓越的性能，在受试者工作特征曲线（AUC）下面积始终超过0.97。此外，我们系统地分析了蛋白质序列相似性阈值和多尺度序列长度对模型性能的影响，进一步验证了NeuroScale的稳健性和通用性。
NeuroScale代表了神经肽预测方面的重大进展，兼具高精度和对不同序列特征的适应性。它在不同序列相似性阈值和长度上的泛化能力突出了其作为神经肽发现和基于肽的药物开发可靠工具的潜力。通过提供一个可扩展且高效的深度学习框架，NeuroScale为神经肽功能、疾病机制及治疗应用的未来研究铺平了道路。
