Enhancing peptide identification in metaproteomics through curriculum learning in deep learning

Shichao Feng, Bailu Zhang, Huan Wang, Yi Xiong, Athena Tian, Xiaohui Yuan, Chongle Pan () and Xuan Guo ()
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Shichao Feng: University of North Texas
Bailu Zhang: University of North Texas
Huan Wang: Huazhong Agricultural University
Yi Xiong: University of Oklahoma
Athena Tian: Emory University
Xiaohui Yuan: University of North Texas
Chongle Pan: University of Oklahoma
Xuan Guo: University of North Texas

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Metaproteomics offers a powerful window into the active functions of microbial communities, but accurately identifying peptides remains challenging due to the size and incompleteness of protein databases derived from metagenomes. These databases often contain vastly more sequences than those from single organisms, creating a computational bottleneck in peptide-spectrum match (PSM) filtering. Here we present WinnowNet, a deep learning-based method for PSM filtering, available in two versions: one using transformers and the other convolutional neural networks. Both variants are designed to handle the unordered nature of PSM data and are trained using a curriculum learning strategy that moves from simple to complex examples. WinnowNet consistently achieves more true identifications at equivalent false discovery rates compared to leading tools, including Percolator, MS2Rescore, and DeepFilter, and outperforms filters integrated into popular analysis pipelines. It also uncovers more gut microbiome biomarkers related to diet and health, highlighting its potential to support advances in personalized medicine.

Date: 2025
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DOI: 10.1038/s41467-025-63977-z

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