Wang Lei, Zeng Zilu, Xue Zhidong, Wang Yan
Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China.
School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Comput Struct Biotechnol J. 2023 Dec 5;23:309-315. doi: 10.1016/j.csbj.2023.12.004. eCollection 2024 Dec.
Neuropeptides play critical roles in many biological processes such as growth, learning, memory, metabolism, and neuronal differentiation. A few approaches have been reported for predicting neuropeptides that are cleaved from precursor protein sequences. However, these models for cleavage site prediction of precursors were developed using a limited number of neuropeptide precursor datasets and simple precursors representation models. In addition, a universal method for predicting neuropeptide cleavage sites that can be applied to all species is still lacking. In this paper, we proposed a novel deep learning method called DeepNeuropePred, using a combination of pre-trained language model and Convolutional Neural Networks for feature extraction and predicting the neuropeptide cleavage sites from precursors. To demonstrate the model's effectiveness and robustness, we evaluated the performance of DeepNeuropePred and four models from the NeuroPred server in the independent dataset and our model achieved the highest AUC score (0.916), which are 6.9%, 7.8%, 8.8%, and 10.9% higher than Mammalian (0.857), insects (0.850), Mollusc (0.842) and Motif (0.826), respectively. For the convenience of researchers, we provide a web server (http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp).
神经肽在许多生物过程中发挥着关键作用，如生长、学习、记忆、新陈代谢和神经元分化。已经报道了一些用于预测从前体蛋白序列中切割出来的神经肽的方法。然而，这些用于预测前体切割位点的模型是使用有限数量的神经肽前体数据集和简单的前体表示模型开发的。此外，仍然缺乏一种可应用于所有物种的预测神经肽切割位点的通用方法。在本文中，我们提出了一种名为DeepNeuropePred的新型深度学习方法，它结合了预训练语言模型和卷积神经网络进行特征提取，并从前体中预测神经肽切割位点。为了证明该模型的有效性和稳健性，我们在独立数据集中评估了DeepNeuropePred和来自NeuroPred服务器的四个模型的性能，我们的模型获得了最高的AUC分数（0.916），分别比哺乳动物模型（0.857）、昆虫模型（0.850）、软体动物模型（0.842）和Motif模型（0.826）高6.9%、7.8%、8.8%和10.9%。为方便研究人员，我们提供了一个网络服务器（http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp）。
