In eukaryotes, protein phosphorylation is specifically catalyzed by numerous protein kinases (PKs), faithfully orchestrates various biological processes, and reversibly determines cellular dynamics and plasticity. Here we report an updated algorithm of Group-based Prediction System (GPS) 5.0 to improve the performance for predicting kinase-specific phosphorylation sites (p-sites). Two novel methods, position weight determination (PWD) and scoring matrix optimization (SMO), were developed. Compared with other existing tools, GPS 5.0 exhibits a highly competitive accuracy. Besides serine/threonine or tyrosine kinases, GPS 5.0 also supports the prediction of dual-specificity kinase-specific p-sites. In the classical module of GPS 5.0, 617 individual predictors were constructed for predicting p-sites of 479 human PKs. To extend the application of GPS 5.0, a species-specific module was implemented to predict kinase-specific p-sites for 44,795 PKs in 161 eukaryotes. The online service and local packages of GPS 5.0 are freely available for academic research at http://gps.biocuckoo.cn.

Dysfunction of macroautophagy/autophagy has been postulated as a major cellular toxicological response to nanomaterials. It has been reported that excessive autophagy activation, induced by silica nanoparticles (SiNPs), contributes to autophagy dysfunction, whereas little is known how SiNPs trigger autophagy activation. Here, we treated normal rat kidney (NRK) cells using 3 different sizes of SiNPs (16, 29, and 51 nm) and observed that 16-nm SiNPs, with a final concentration of 60 μg/mL, dramatically induce autophagy activation without reducing cell viability. We further conducted a transcriptomic, proteomic, and phosphoproteomic profiling, and detected 23 autophagy-related (Atg) genes and 35 autophagy regulators regulated on at least one omic layer. To identify key regulators from the multi-omics data, we developed a new algorithm of computational prediction of master autophagy-regulating kinases (cMAK) to detect 21 candidates and revealed the CDK7-CDK4 cascade to be functional. The silence or inhibition of Cdk7 or Cdk4 significantly attenuated autophagic activation but not influenced autophagic flux blockage induced by 16-nm SiNPs. Further computational modeling indicated that the CDK7-CDK4 signaling axis potentially triggers autophagy activation by phosphorylating RB1 (RB transcriptional corepressor 1), activating two critical transcription factors, E2F1 (E2F transcription factor 1) and FOXO3 (forkhead box O3), and enhancing the transcriptional levels of at least 8 Atg genes and autophagy regulators in response to SiNPs. Our studies not only established a powerful method for predicting regulatory kinases from the multi-omics data but also revealed a potential mechanism of SiNP-triggered autophagy activation through modulating the CDK7-CDK4 cascade.

Post-translational modifications (PTMs) are critical regulators of protein function, and their disruption is a key mechanism by which missense variants contribute to disease. Accurate PTM site prediction using deep learning can help identify PTM-altering variants, but progress has been limited by the lack of large, high-quality training datasets. Here, we introduce PTMAtlas, a curated compendium of 397,524 PTM sites generated through systematic reprocessing of 241 public mass-spectrometry datasets, and DeepMVP, a deep learning framework trained on PTMAtlas to predict PTM sites for phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation. DeepMVP substantially outperforms existing tools across all six PTM types. Its application to predicting PTM-altering missense variants shows strong concordance with experimental results, validated using literature-curated variants and cancer proteogenomic datasets. Together, PTMAtlas and DeepMVP provide a robust platform for PTM research and a scalable framework for assessing the functional consequences of coding variants through the lens of PTMs.

Among different age groups, middle-aged individuals are particularly susceptible to obesity, with a 22% higher risk of all-cause mortality. However, the underlying mechanisms remain unclear. In this study, we identify adipose progenitor cells (APCs) in the white adipose tissue (WAT) of middle-aged subjects as potential causes of midlife obesity. Specifically, the extracellular vesicles (EVs) derived from APCs display an impaired ability to mitigate the inflammaging of adipose tissue macrophages (ATMs) in middle-aged individuals. Mechanistically, these EVs, lacking miR-145-5p, fail to suppress the expression of L-selectin in ATMs, thereby facilitating their M1 program via the NF-κB signaling pathway. In contrast, EVs from young APCs effectively inhibit M1 macrophage polarization. Accordingly, targeted liposomes are designed to deliver miR-145-5p mimics to ATMs, which effectively prevent the obesity in middle-aged mice. Collectively, our findings highlight the role of APC-derived EVs in midlife obesity and propose miR-145-5pas a promising therapeutic target for clinical applications.

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 fluorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy. Abbreviations: Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.

Proteostasis is fundamental for maintaining organismal health. However, the mechanisms underlying its dynamic regulation and how its disruptions lead to diseases are largely unclear. Here, we conduct in-depth propionylomic profiling in Drosophila, and develop a small-sample learning framework to prioritize the propionylation at lysine 17 of H2B (H2BK17pr) to be functionally important. Mutating H2BK17 which eliminates propionylation leads to elevated total protein level in vivo. Further analyses reveal that H2BK17pr modulates the expression of 14.7–16.3% of genes in the proteostasis network, and determines global protein level by regulating the expression of genes involved in the ubiquitin-proteasome system. In addition, H2BK17pr exhibits daily oscillation, mediating the influences of feeding/fasting cycles to drive rhythmic expression of proteasomal genes. Our study not only reveals a role of lysine propionylation in regulating proteostasis, but also implements a generally applicable method which can be extended to other issues with little prior knowledge.