ArticleComputational and structural biotechnology journal2024
DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Top-Down Proteomics of Zebrafish Brain Regions Using Capillary Zone Electrophoresis-Tandem Mass Spectrometry.Journal of proteome research · 2026Article
- BLMPred: Predicting linear B-cell epitopes using pre-trained protein language models and machine learning.Computational and structural biotechnology journal · 2026Article
- Insect neuropeptides as agents for pest control: potential and challenges.Biological research · 2025Review
- Discovery of peptides as key regulators of metabolic and cardiovascular crosstalk.Cell reports · 2025Review
- Understanding peptide hormones: from precursor proteins to bioactive molecules.Trends in biochemical sciences · 2025Review
- NeuroScale: evolutional scale-based protein language models enable prediction of neuropeptides.BMC biology · 2025Article
- Computational approaches for identifying neuropeptides: A comprehensive review.Molecular therapy. Nucleic acids · 2025Review
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4 authors.
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Abstract
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).
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