Identification of plant vacuole proteins by using graph neural network and contact maps.

Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .

ProSE-Pero: Peroxisomal Protein Localization Identification Model Based on Self-Supervised Multi-Task Language Pre-Training Model.

BACKGROUND: Peroxisomes are membrane-bound organelles that contain one or more types of oxidative enzymes. Aberrant localization of peroxisomal proteins can contribute to the development of various diseases. To more accurately identify and locate peroxisomal proteins, we developed the ProSE-Pero model. METHODS: We employed three methods based on deep representation learning models to extract the characteristics of peroxisomal proteins and compared their performance. Furthermore, we used the SVMSMOTE balanced dataset, SHAP interpretation model, variance analysis (ANOVA), and light gradient boosting machine (LightGBM) to select and compare the extracted features. We also constructed several traditional machine learning methods and four deep learning models to train and test our model on a dataset of 160 peroxisomal proteins using tenfold cross-validation. RESULTS: Our proposed ProSE-Pero model achieves high performance with a specificity (Sp) of 93.37%, a sensitivity (Sn) of 82.41%, an accuracy (Acc) of 95.77%, a Matthews correlation coefficient (MCC) of 0.8241, an F1 score of 0.8996, and an area under the curve (AUC) of 0.9818. Additionally, we extended our method to identify plant vacuole proteins and achieved an accuracy of 91.90% on the independent test set, which is approximately 5% higher than the latest iPVP-DRLF model. CONCLUSIONS: Our model surpasses the existing In-Pero model in terms of peroxisomal protein localization and identification. Additionally, our study showcases the proficient performance of the pre-trained multitasking language model ProSE in extracting features from protein sequences. With its established validity and broad generalization, our model holds considerable potential for expanding its application to the localization and identification of proteins in other organelles, such as mitochondria and Golgi proteins, in future investigations.

Ultrathin δ-MnO2 nanoflakes with Na+ intercalation as a high-capacity cathode for aqueous zinc-ion batteries.

Pristine δ-MnO2 as the typical cathode for rechargeable zinc-ion batteries (ZIBs) suffers from sluggish reaction kinetics, which is the key issue to prepare high-performance manganese-based materials. In this work, Na+ incorporated into layered δ-MnO2 (NMO) was prepared for ZIB cathodes with high capacity, high energy density, and excellent durable stability. By an effective fabricated strategy of hydrothermal synthesis, a three-dimensional interconnected δ-MnO2 nanoflake network with Na+ intercalation showed a uniform array arrangement and high conductivity. Also, the H+ insertion contribution in the NMO cathode to the discharge capacity confirmed the fast electrochemical charge transfer kinetics due to the enhanced ion conductivity from the insertion of Na+ into the interlayers of the host material. Consequently, a neutral aqueous NMO-based ZIB revealed a superior reversible capacity of 335 mA h g-1, and an impressive durability over 1000 cycles, and a peak gravimetric energy output of 459 W h kg-1. As a proof of concept, the as-fabricated quasi-solid-state ZIB exhibited a remarkable capacity of 284 mA h g-1 at a current density of 0.5 A g-1, and good practicability. This research demonstrated a significant enhancement of the electrochemical performance of MnO2-based ZIBs by the intercalation of Na+ to regulate the microstructure and boost the electrochemical kinetics of the δ-MnO2 cathode, thus providing a new insight for high-performance aqueous ZIBs.