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MOTIVATION: In recent years, high-throughput sequencing technologies have made large-scale protein sequences accessible. However, their functional annotations usually rely on low-throughput and pricey experimental studies. Computational prediction models offer a promising alternative to accelerate this process. Graph neural networks have shown significant progress in protein research, but capturing long-distance structural correlations and identifying key residues in protein graphs remains challenging. RESULTS: In the present study, we propose a novel deep learning model named Hierarchical graph transformEr with contrAstive Learning (HEAL) for protein function prediction. The core feature of HEAL is its ability to capture structural semantics using a hierarchical graph Transformer, which introduces a range of super-nodes mimicking functional motifs to interact with nodes in the protein graph. These semantic-aware super-node embeddings are then aggregated with varying emphasis to produce a graph representation. To optimize the network, we utilized graph contrastive learning as a regularization technique to maximize the similarity between different views of the graph representation. Evaluation of the PDBch test set shows that HEAL-PDB, trained on fewer data, achieves comparable performance to the recent state-of-the-art methods, such as DeepFRI. Moreover, HEAL, with the added benefit of unresolved protein structures predicted by AlphaFold2, outperforms DeepFRI by a significant margin on Fmax, AUPR, and Smin metrics on PDBch test set. Additionally, when there are no experimentally resolved structures available for the proteins of interest, HEAL can still achieve better performance on AFch test set than DeepFRI and DeepGOPlus by taking advantage of AlphaFold2 predicted structures. Finally, HEAL is capable of finding functional sites through class activation mapping. AVAILABILITY AND IMPLEMENTATION: Implementations of our HEAL can be found at https://github.com/ZhonghuiGu/HEAL.

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

ABSTRACT Backgrounds Aims Methods Results Conclusions With the increasing demand for beauty and a healthy lifespan, studies regarding anti‐skin aging have drawn much more attention than ever before. Skin cellular senescence, the primary cause of skin aging, is characterized by a cell cycle arrest in proliferating cells along with a senescence‐associated secretory phenotype (SASP), which can be triggered by various internal or external stimuli.Recent studies have made significant progress in the fields of anti‐senescence and anti‐aging. However, little is known about the roles and functions of natural compounds, particularly flavonoids, in skin cellular senescence studies.In this study, using strategies including ionizing radiation (IR), senescence‐associated β galactosidase assay (SA‐β‐Gal), immunofluorescence (IF), flow cytometry, PCR array, as well as in vivo experiments, we investigated the effects and roles of troxerutin (Trx), a natural flavonoid, in skin keratinocyte senescence.We found that Trx delays skin keratinocyte senescence induced by IR. Mechanistically, Trx protects the skin keratinocyte cells from senescence by alleviating reactive oxygen species (ROS) accumulation, mitochondrial dysfunction, and DNA damage caused by IR. In addition, Trx was also proved to relieve skin senescence and SASP secretion in vivo induced by IR stimulation.Altogether, our findings pointed to a new function of Trx in delaying stress‐induced skin keratinocyte senescence, and should thus provide theoretical foundations for exploring novel strategies against skin aging. [ABSTRACT FROM AUTHOR]