Bing Wang

Accurate protein subcellular localization (PSL) is essential for proteins to perform their biological functions, whereas protein mis-localization can disrupt cellular processes and contribute to various diseases, particularly cancer. Although spatial proteomics technologies have enabled systematic studies of PSL, their high cost and technical complexity limit large-scale analysis of mis-localized proteins (MLPs) in cancer. To address this challenge, we propose DeepMLP, a proteomics-driven deep learning framework that identifies MLPs across cancers using large-scale mass spectrometry-based proteomics data. It constructs pathway-aware protein representations via a cross-attention mechanism and, by integrating these representations with dynamic protein-protein interaction (PPI) networks through graph attention networks, synergistically enhances the ability to uncover potential cancer MLPs. Benchmarking demonstrated that DeepMLP consistently outperformed state-of-the-art methods in both accuracy and stability for PSL prediction in normal and tumor conditions. Furthermore, we systematically identified potential MLPs across pan-cancer types and further uncovered a subset of potential mis-localized protein kinases. Functional enrichment analyses of these MLPs revealed their significant involvement in cancer-related metabolic and signaling pathways, highlighting their potential biological relevance in tumorigenesis.

Extracellular vesicles (EVs) carry complex glycoproteins critical mediators of disease progression and hold great potential for liquid biopsy applications. However, their low abundance presents significant analytical challenges. This study develop a versatile platform for EV N-glycoproteomic analysis (EVGpro), which enables tandem EV capture, EV protein digestion, and enrichment of N-glycopeptides. Functionalized magnetic nanochains with cell-penetrating peptides (octa-arginine, R₈) enable efficient EV isolation, followed by on-chain hydrolysis to expose PEG₂₀₀₀ for ultrasensitive glycopeptide enrichment. The streamlined EVGpro platform identified up to 17 times more EV glycoproteins than ultracentrifugation across seven biofluids, profiling 398 EV glycoproteins from just 1 μL tear. Applied to seminal plasma, EVGpro uncover disease-specific glycoprotein signatures and identified previously unreported subtypes of asthenozoospermia. This robust and scalable approach advances the sensitive detection of disease biomarkers and molecular subtypes from diverse biofluids to enable precision medicine.

Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry–based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)–based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.

Peroxisomes are organelles enclosed by a single membrane and are present in various species. The abruption of peroxisomes is correlated with peroxisome biogenesis disorders and single peroxisomal enzyme deficiencies that induce diverse diseases in different organs. However, little is known about the protein compositions and corresponding roles of heterogeneous peroxisomes in various organs. Through transcriptomic and proteomic analyses, we observed heterogenous peroxisomal components among different organs, as well as between testicular somatic cells and different developmental stages of germ cells. As Pex3 is expressed in both germ cells and Sertoli cells, we generated Pex3 germ cell- and Sertoli cell-specific knockout mice. While Pex3 deletion in Sertoli cells did not affect spermatogenesis, the deletion in germ cells resulted in male sterility, manifested as the destruction of intercellular bridges between spermatids and the formation of multinucleated giant cells. Proteomic analysis of the Pex3-deleted spermatids revealed defective expressions of peroxisomal proteins and spermiogenesis-related proteins. These findings provide new insights that PEX3-dependent peroxisomes are essential for germ cells undergoing spermiogenesis, but not for Sertoli cells.

The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins across subcellular fractions provides us a high-throughput approach to predict unknown PSLs based on known PSLs. However, the accuracy of PSL annotations in spatial proteomics is limited by the performance of existing PSL predictors based on traditional machine learning algorithms. In this study, we present a novel deep learning framework named DeepSP for PSL prediction of an MS-based spatial proteomics data set. DeepSP constructs the new feature map of a difference matrix by capturing detailed changes between different subcellular fractions of protein occupancy profiles and uses the convolutional block attention module to improve the prediction performance of PSL. DeepSP achieved significant improvement in accuracy and robustness for PSL prediction in independent test sets and unknown PSL prediction compared to current state-of-the-art machine learning predictors. As an efficient and robust framework for PSL prediction, DeepSP is expected to facilitate spatial proteomics studies and contributes to the elucidation of protein functions and the regulation of biological processes.

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target–decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.