c-Myc and FOXO3a-The Everlasting Decision Between Neural Regeneration and Degeneration

The transcription factors c-Myc and FoxO3a play significant roles in neurodegenerative processes, yet their interaction in neurological disorders remains largely unexplored. In contrast, much of the available information about their relationship comes from cancer research. While it is well-established that FoxO3a inhibits c-Myc activity, this interaction represents only a basic understanding of a far more complex dynamic, which includes exceptions under specific conditions and the involvement of additional regulatory factors. Given the critical need to address this gap for the treatment and prevention of neurodegenerative disorders, this review consolidates current knowledge on the joint roles of these two factors in neuropathology. It also highlights their conformational flexibility, post-translational modifications, and outlines potential directions for future research.

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism

Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understanding of the molecular function of proteins. Recent developments in the field of deep neural networks have led to a proliferation of deep learning-based methylation identification studies because of their fast and accurate prediction. In this paper, we propose DeepGpgs, an advanced deep learning model incorporating Gaussian prior and gated attention mechanism. We introduce a residual network channel to extract the evolutionary information of proteins. Then we combine the adaptive embedding with bidirectional long short-term memory networks to form a context-shared encoder layer. A gated multi-head attention mechanism is followed to obtain the global information about the sequence. A Gaussian prior is injected into the sequence to assist in predicting PTMs. We also propose a weighted joint loss function to alleviate the false negative problem. We empirically show that DeepGpgs improves Matthews correlation coefficient by 6.3% on the arginine methylation independent test set compared with the existing state-of-the-art methylation site prediction methods. Furthermore, DeepGpgs has good robustness in phosphorylation site prediction of SARS-CoV-2, which indicates that DeepGpgs has good transferability and the potential to be extended to other modification sites prediction. The open-source code and data of the DeepGpgs can be obtained from https://github.com/saizhou1/DeepGpgs.

PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis

With the development of biotechnology, a large number of phosphorylation sites have been experimentally confirmed and collected, but only a few of them have kinase annotations. Since experimental methods to detect kinases at specific phosphorylation sites are expensive and accidental, some computational methods have been proposed to predict the kinase of these sites, but most methods only consider single sequence information or single functional network information. In this study, a new method Predicting Kinase of Specific Phosphorylation Sites (PKSPS) is developed to predict kinases of specific phosphorylation sites in human proteins by combining PKSPS-Net with PKSPS-Seq, which considers protein-protein interaction (PPI) network information and sequence information. For PKSPS-Net, kinase-kinase and substrate-substrate similarity are quantified based on the topological similarity of proteins in the PPI network, and maximum weighted bipartite matching algorithm is proposed to predict kinase-substrate relationship. In PKSPS-Seq, phosphorylation sequence enrichment analysis is used to analyze the similarity of local sequences around phosphorylation sites and predict the kinase of specific phosphorylation sites (KSP). PKSPS has been proved to be more effective than the PKSPS-Net or PKSPS-Seq on different sets of kinases. Further comparison results show that the PKSPS method performs better than existing methods. Finally, the case study demonstrates the effectiveness of the PKSPS in predicting kinases of specific phosphorylation sites. The open source code and data of the PKSPS can be obtained from https://github.com/guoxinyunncu/PKSPS.

The main post-translational modifications and related regulatory pathways in the malaria parasite Plasmodium falciparum: An update

There are important challenges when investigating individual post-translational modifications (PTMs) or protein interaction network and delineating if PTMs or their changes and cross-talks are involved during infection, disease initiation or as a result of disease progression. Proteomics and in silico approaches now offer the possibility to complement each other to further understand the regulatory involvement of these modifications in parasites and infection biology. Accordingly, the current review highlights key expressed or altered proteins and PTMs are invisible switches that turn on and off the function of most of the proteins. PTMs include phosphorylation, glycosylation, ubiquitylation, palmitoylation, myristoylation, prenylation, acetylation, methylation, and epigenetic PTMs in P. falciparum which have been recently identified. But also other low-abundant or overlooked PTMs that might be important for the parasite's survival, infectivity, antigenicity, immunomodulation and pathogenesis. We here emphasize the PTMs as regulatory pathways playing major roles in the biology, pathogenicity, metabolic pathways, survival, host-parasite interactions and the life cycle of P. falciparum. Further validations and functional characterizations of such proteins might confirm the discovery of therapeutic targets and might most likely provide valuable data for the treatment of P. falciparum, the main cause of severe malaria in human.