Explore the vast range of titles available.

Bitter peptides are small molecular peptides produced by the hydrolysis of proteins under acidic, alkaline, or enzymatic conditions. These peptides can enhance food flavor and offer various health benefits, with attributes such as antihypertensive, antidiabetic, antioxidant, antibacterial, and immune-regulating properties. They show significant potential in the development of functional foods and the prevention and treatment of diseases. This review introduces the diverse sources of bitter peptides and discusses the mechanisms of bitterness generation and their physiological functions in the taste system. Additionally, it emphasizes the application of bioinformatics in bitter peptide research, including the establishment and improvement of bitter peptide databases, the use of quantitative structure–activity relationship (QSAR) models to predict bitterness thresholds, and the latest advancements in classification prediction models built using machine learning and deep learning algorithms for bitter peptide identification. Future research directions include enhancing databases, diversifying models, and applying generative models to advance bitter peptide research towards deepening and discovering more practical applications.

Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA. In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm. The model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.

Background CRISPR-Cas12a (formerly Cpf1) is an RNA-guided endonuclease with distinct features that have expanded genome editing capabilities. Cas12a-mediated genome editing is temperature sensitive in plants, but a lack of a comprehensive understanding on Cas12a temperature sensitivity in plant cells has hampered effective application of Cas12a nucleases in plant genome editing. Results We compared AsCas12a, FnCas12a, and LbCas12a for their editing efficiencies and non-homologous end joining (NHEJ) repair profiles at four different temperatures in rice. We found that AsCas12a is more sensitive to temperature and that it requires a temperature of over 28 °C for high activity. Each Cas12a nuclease exhibited distinct indel mutation profiles which were not affected by temperatures. For the first time, we successfully applied AsCas12a for generating rice mutants with high frequencies up to 93% among T0 lines. We next pursued editing in the dicot model plant Arabidopsis , for which Cas12a-based genome editing has not been previously demonstrated. While LbCas12a barely showed any editing activity at 22 °C, its editing activity was rescued by growing the transgenic plants at 29 °C. With an early high-temperature treatment regime, we successfully achieved germline editing at the two target genes, GL2 and TT4, in Arabidopsis transgenic lines. We then used high-temperature treatment to improve Cas12a-mediated genome editing in maize. By growing LbCas12a T0 maize lines at 28 °C, we obtained Cas12a-edited mutants at frequencies up to 100% in the T1 generation. Finally, we demonstrated DNA binding of Cas12a was not abolished at lower temperatures by using a dCas12a-SRDX-based transcriptional repression system in Arabidopsis . Conclusion Our study demonstrates the use of high-temperature regimes to achieve high editing efficiencies with Cas12a systems in rice, Arabidopsis , and maize and sheds light on the mechanism of temperature sensitivity for Cas12a in plants.

MicroRNAs (miRNAs) are small non-coding RNAs that play important roles in plant development and stress responses. Loss-of-function analysis of miRNA genes has been traditionally challenging due to lack of appropriate knockout tools. In this study, single miRNA genes (OsMIR408 and OsMIR528) and miRNA gene families (miR815a/b/c and miR820a/b/c) in rice were targeted by CRISPR-Cas9. We showed single strand conformation polymorphism (SSCP) is a more reliable method than restriction fragment length polymorphism (RFLP) for identifying CRISPR-Cas9 generated mutants. Frequencies of targeted mutagenesis among regenerated T0 lines ranged from 48 to 89% at all tested miRNA target sites. In the case of miRNA528, three independent guide RNAs (gRNAs) all generated biallelic mutations among confirmed mutant lines. When targeted by two gRNAs, miRNA genes were readily to be deleted at a frequency up to 60% in T0 rice lines. Thus, we demonstrate CRISPR-Cas9 is an effective tool for knocking out plant miRNAs. Single-base pair (bp) insertion/deletion mutations (indels) in mature miRNA regions can lead to the generation of functionally redundant miRNAs. Large deletions at either the mature miRNA or the complementary miRNA were found to readily abolish miRNA function. Utilizing mutants of and , we find that knocking out a single miRNA can result in expression profile changes of many other seemingly unrelated miRNAs. In a case study on , we reveal it is a positive regulator in salt stress. Our work not only provides empirical guidelines on targeting miRNAs with CRISPR-Cas9, but also brings new insights into miRNA function and complex cross-regulation in rice.

Background Small interfering RNA (siRNA) is a powerful tool for gene silencing, but its clinical application is limited by instability and potential immunogenicity. While chemical modification is essential to overcome these hurdles, data on chemically modified siRNAs are currently scattered, hindering rational drug design and development. Results We developed CMsiRNAdb, a comprehensive database integrating data resources, analytical tools, and efficacy prediction for chemically modified siRNAs. We consolidated 43,153 experimentally validated sequences and silencing efficiency data derived from 90 patents, covering 36 modification types and 13 therapeutic target genes. The database offers multi-dimensional retrieval, visualization, and batch download functions. Furthermore, we developed ModMapper, a Trie tree-based tool for precise identification of modification sites, and integrated the Cm-siRPred model for efficacy evaluation. CMsiRNAdb is freely accessible at https://cellknowledge.com.cn/CMsiRNAdb/ . Conclusion CMsiRNAdb provides critical data support and analytical tools for the rational design and rapid optimization of siRNA drugs. By standardizing data and offering predictive capabilities, it significantly advances the development of nucleic acid therapeutics.

Type 2 diabetes mellitus (T2DM) is a prevalent and complex metabolic disorder characterized by insulin resistance, progressive β-cell dysfunction, and severe systemic complications. Advances in single-cell multi-omics—transcriptomics, chromatin accessibility profiling, and integrative analyses—have offered unprecedented insights into the cellular heterogeneity and regulatory networks of pancreatic islets. We highlight recent discoveries in islet cell heterogeneity and β-cell pathophysiology, with a particular focus on dysfunction and dedifferentiation. We further underscore the computational frameworks that enable these discoveries, spanning data preprocessing, multi-omics integration, and machine learning-driven analyses, which collectively enable the dissection of disease-relevant cell subpopulations and the reconstruction of developmental and regulatory trajectories. We also examine how impaired signaling within islets and chronic adipose inflammation contribute to T2DM pathogenesis. Finally, we discuss key challenges in clinical translation—including limited population diversity in single-cell atlases and the interpretability of computational models—and propose future directions toward precision diagnostics and therapeutic innovation in T2DM.

Conotoxins, a diverse family of disulfide-rich peptides derived from the venom of Conus species, have gained prominence in biomedical research due to their highly specific interactions with ion channels, receptors, and neurotransmitter systems. Their pharmacological properties make them valuable molecular tools and promising candidates for therapeutic development. However, traditional conotoxin classification and functional characterization remain labor-intensive, necessitating the increasing adoption of computational approaches. In particular, machine learning (ML) techniques have facilitated advancements in sequence-based classification, functional prediction, and de novo peptide design. This review explores recent progress in applying ML and deep learning (DL) to conotoxin research, comparing key databases, feature extraction techniques, and classification models. Additionally, we discuss future research directions, emphasizing the integration of multimodal data and the refinement of predictive frameworks to enhance therapeutic discovery.

Key message A method based on DNA single-strand conformation polymorphism is demonstrated for effective genotyping of CRISPR/Cas9-induced mutants in rice. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 9 (Cas9) has been widely adopted for genome editing in many organisms. A large proportion of mutations generated by CRISPR/Cas9 are very small insertions and deletions (indels), presumably because Cas9 generates blunt-ended double-strand breaks which are subsequently repaired without extensive end-processing. CRISPR/Cas9 is highly effective for targeted mutagenesis in the important crop, rice. For example, homozygous mutant seedlings are commonly recovered from CRISPR/Cas9-treated calli. However, many current mutation detection methods are not very suitable for screening homozygous mutants that typically carry small indels. In this study, we tested a mutation detection method based on single-strand conformational polymorphism (SSCP). We found it can effectively detect small indels in pilot experiments. By applying the SSCP method for CRISRP-Cas9-mediated targeted mutagenesis in rice, we successfully identified multiple mutants of OsROC5 and OsDEP1 . In conclusion, the SSCP analysis will be a useful genotyping method for rapid identification of CRISPR/Cas9-induced mutants, including the most desirable homozygous mutants. The method also has high potential for similar applications in other plant species.

scRiskCell is an interpretable intelligent computational framework that leverages nearly 500,000 islet cell expression profiles from 106 donors across different continuous disease states. By calculating the intrinsic relationship between donor disease states and cell expression profiles, it assigns a pseudo‐cell state index to each cell. Sorting the pseudo‐indexes of cells enables the identification of risk cells truly disrupted by the disease. Importantly, scRiskCell reveals the dynamic aggregation pattern of risk cells during disease progression, providing mechanistic insights for early disease prediction and clinical dynamic monitoring of disease progression.

In China, traditional Chinese medicine (TCM) has been widely used for coronavirus infectious disease 2019 (COVID‐19) prevention, treatment, and recovery and has played a part in the battle against the disease. A variety of TCM treatments have been recommended for different stages of COVID‐19. But, to the best of our knowledge, a comprehensive database for storing and organizing anti‐COVID TCM treatments is still lacking. Herein, we developed TCM2COVID, a manually curated resource of anti‐COVID TCM formulas, natural products (NPs), and herbs. The current version of TCM2COVID (1) documents over 280 TCM formulas (including over 300 herbs) with detailed clinical evidence and therapeutic mechanism information; (2) records over 80 NPs with detailed potential therapeutic mechanisms; and (3) launches a useful web server for querying, analyzing and visualizing documented formulas similar to those supplied by the user (formula similarity analysis). In summary, TCM2COVD provides a user‐friendly and practical platform for documenting, querying, and browsing anti‐COVID TCM treatments, and will help in the development and elucidation of the mechanisms of action of new anti‐COVID TCM therapies to support the fight against the COVID‐19 epidemic. TCM2COVID is freely available at http://zhangy-lab.cn/tcm2covid/. TCM2COVID documents over 280 traditional Chinese medicine formulas (including over 300 herbs) with detailed clinical evidence and therapeutic mechanism information; TCM2COVID records over 80 natural products with detailed potential therapeutic mechanisms; TCM2COVID launches a useful web server for querying, analyzing, and visualizing documented formulas similar to those supplied by the user (formula similarity analysis). Highlights TCM2COVID documents over 280 TCM formulas (including over 300 herbs) with detailed clinical evidence and therapeutic mechanism information. TCM2COVID records over 80 NPs with detailed potential therapeutic mechanisms. TCM2COVID launches a useful web server for querying, analyzing, and visualizing documented formulas similar to those supplied by the user (formula similarity analysis).