Lithium chloride (LiCl) has been widely researched and utilized as a therapeutic option for bipolar disorder (BD). Several pathways, including cell signaling and signal transduction pathways in mammalian cells, are shown to be regulated by LiCl. LiCl can negatively control the expression and activity of PGM2, a phosphoglucomutase that influences sugar metabolism in yeast. In the presence of galactose, when yeast cells are challenged by LiCl, the phosphoglucomutase activity of PGM2p is decreased, causing an increase in the concentration of toxic galactose metabolism intermediates that result in cell sensitivity. Here, we report that the null yeast mutant strains DBP7∆ and YRF1-6∆ exhibit increased LiCl sensitivity on galactose-containing media. Additionally, we demonstrate that DBP7 and YRF1-6 modulate the translational level of PGM2 mRNA, and the observed alteration in translation seems to be associated with the 5′-untranslated region (UTR) of PGM2 mRNA. Furthermore, we observe that DBP7 and YRF1-6 influence, to varying degrees, the translation of other mRNAs that carry different 5′-UTR secondary structures.

Maintaining translation fidelity is a critical step within the process of gene expression. It requires the involvement of numerous regulatory elements to ensure the synthesis of functional proteins. The efficient termination of protein synthesis can play a crucial role in preserving this fidelity. Here, we report on investigating a protein of unknown function, YNR069C (also known as BSC5), for its activity in the process of translation. We observed a significant increase in the bypass of premature stop codons upon the deletion of YNR069C. Interestingly, the genomic arrangement of this ORF suggests a compatible mode of expression reliant on translational readthrough, incorporating the neighboring open reading frame. We also showed that the deletion of YNR069C results in an increase in the rate of translation. Based on our results, we propose that YNR069C may play a role in translation fidelity, impacting the overall quantity and quality of translation. Our genetic interaction analysis supports our hypothesis, associating the role of YNR069C to the regulation of protein synthesis.

Natural peptides derived from plants have been an important source of medical substances for several decades. Due to their mechanism of action, chemical potential, and favourable side effect profile, these peptides represent a safer alternative to synthetic pharmaceutical treatments. In this study, we report the discovery of a natural peptide derived from the Brassica napus (Canola) proteome that exhibits high functional similarity to an artificial intelligence (AI)-generated peptide that is designed to bind to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike 1 (S1) protein receptor-binding domain (RBD) region. The results of a series of experiments including molecular docking simulations, as well as binding and inhibition assays suggest that the natural peptide exhibits functions similar to those of the AI-generated peptide in binding to the RBD region and disrupting its interaction with the human host receptor angiotensin-converting enzyme 2 (ACE2). This study demonstrates the potential of AI-designed peptides to facilitate the identification of natural peptides with similar functional properties.

Translation is a fundamental process in biology, and understanding its mechanisms is crucial to comprehending cellular functions and diseases. The regulation of this process is closely linked to the structure of mRNA, as these regions prove vital to modulating translation efficiency and control. Thus, identifying and investigating these fundamental factors that influence the processing and unwinding of structured mRNAs would be of interest due to the widespread impact in various fields of biology. To this end, we employed a computational approach and identified genes that may be involved in the translation of structured mRNAs. The approach is based on the enrichment of interactions and co-expression of genes with those that are known to influence translation and helicase activity. The in silico prediction found CAF20 and ECM32 to be highly ranked candidates that may play a role in unwinding mRNA. The activities of neither CAF20 nor ECM32 have previously been linked to the translation of PGM2 mRNA or other structured mRNAs. Our follow-up investigations with these two genes provided evidence of their participation in the translation of PGM2 mRNA and several other synthetic structured mRNAs.

Synthetic peptides are increasingly used in cosmetic products due to their potential to regulate biological processes related to skin aging, pigmentation, inflammation, extracellular matrix maintenance, and other aspects of skin health. However, the mechanisms of many cosmetic peptides are still not fully understood. Their activities can also be limited by poor stability, skin penetration, off-target effects, and limited clinical evidence. This review discusses the major classes of cosmetic peptides and the molecular mechanisms through which they are proposed to exert their activities. It also examines the main approaches used for peptide discovery. These include discovery from natural and biologically derived sources, library-based screening, rational design, and classical computational methods. More recent artificial intelligence (AI)-guided approaches are then discussed, with particular attention to structure-based and sequence-based peptide design. AI provides new opportunities to search much larger peptide sequence spaces and to prioritize candidates before experimental testing. Structure-based methods can use information about target binding sites to guide peptide design, while sequence-based approaches can identify interaction patterns without requiring detailed 3D structural information. These methods may also help improve peptide specificity, reduce potential off-target interactions, and identify naturally occurring peptides with useful properties. Although the application of AI to cosmetic peptide discovery is still at an early stage, it may provide a useful addition to existing approaches. Future progress will depend on better skin-relevant datasets, improved experimental validation, and design strategies that consider not only target binding, but also selectivity, stability, toxicity, delivery, and formulation compatibility.