Antidiabetic Potential of Tea and Its Active Compounds: From Molecular Mechanism to Clinical Evidence.

Diabetes mellitus (DM) is a chronic endocrine disorder that poses a long-term risk to human health accompanied by serious complications. Common antidiabetic drugs are usually accompanied by side effects such as hepatotoxicity and nephrotoxicity. There is an urgent need for natural dietary alternatives for diabetic treatment. Tea (Camellia sinensis) consumption has been widely investigated to lower the risk of diabetes and its complications through restoring glucose metabolism homeostasis, safeguarding pancreatic ß-cells, ameliorating insulin resistance, ameliorating oxidative stresses, inhibiting inflammatory response, and regulating intestinal microbiota. It is indispensable to develop effective strategies to improve the absorption of tea active compounds and exert combinational effects with other natural compounds to broaden its hypoglycemic potential. The advances in clinical trials and population-based investigations are also discussed. This review primarily delves into the antidiabetic potential and underlying mechanisms of tea active compounds, providing a theoretical basis for the practical application of tea and its active compounds against diabetes.

Mussel-inspired antimicrobial hydrogel with cellulose nanocrystals/tannic acid modified silver nanoparticles for enhanced calvarial bone regeneration.

Bacterial infection is a serious challenge in the treatment of open bone defects, and reliance on antibiotic therapy may contribute to the emergence of drug-resistant bacteria. To solve this problem, this study developed a mineralized hydrogel (PVA-Ag-PHA) with excellent antibacterial properties and osteogenic capabilities. Silver nanoparticles (CNC/TA@AgNPs) were greenly synthesized using natural macromolecular cellulose nanocrystals (CNC) and plant polyphenolic tannins (TA) as stabilizers and reducing agents respectively, and then introduced into polyvinyl alcohol (PVA) and polydopamine-modified hydroxyapatite (PDA@HAP) hydrogel. The experimental results indicate that the PVA-Ag-PHA hydrogel, benefiting from the excellent antibacterial properties of CNC/TA@AgNPs, can not only eliminate Staphylococcus aureus and Escherichia coli, but also maintain a sustained sterile environment. At the same time, the HAP modified by PDA is uniformly dispersed within the hydrogel, thus releasing and maintaining stable concentrations of Ca2+ and PO43- ions in the local environment. The porous structure of the hydrogel with excellent biocompatibility creates a suitable bioactive environment that facilitates cell adhesion and bone regeneration. The experimental results in the rat critical-sized calvarial defect model indicate that the PVA-Ag-PHA hydrogel can effectively accelerate the bone healing process. Thus, this mussel-inspired hydrogel with antibacterial properties provides a feasible solution for the repair of open bone defects, demonstrating the considerable potential for diverse applications in bone repair.

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology.

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus.

Green preparation, safety control and intelligent processing of high-quality tea extract.

Tea contains a variety of bioactive components, including catechins, amino acids, tea pigments, caffeine and tea polysaccharides, which exhibit multiple biological activities. These functional components in tea provide a variety of unique flavors, such as bitterness, astringency, sourness, sweetness and umami, which meet the demand of people for natural plant drinks with health benefits and pleasant flavor. Meanwhile, the traditional process of tea plantation, manufacturing and circulation are often accompanied by the safety problems of pesticide residue, heavy metal, organic solvents and other exogenous risks. High-quality tea extract refers to the special tea extract obtained by enriching the specific components of tea. Through green and efficient extraction technologies, diversed high-quality tea extracts such as high-fragrance and high-amino acid tea extracts, low-caffeine and high-catechin tea extracts, high-bioavailability and high-theaflavin tea extracts, high-antioxidant and high-tea polysaccharide tea extracts, high-umami-taste and low-bitter and astringent taste tea extracts are produced. Furthermore, rapid detection, green control and intelligent processing are applied to monitor the quality of tea in real-time, which guarantee the stability and safety of high-quality tea extracts with enhanced efficiency. These emerging technologies will realize the functionalization and specialization of high-quality tea extracts, and promote the sustainable development of tea industry.

Main high-quality tea extracts and their preparation methods were introduced.Potential pollutants in the processing of tea extracts and their detection methods were proposed.Emerging intelligent processing technologies of tea extract were summarized.The applications of high-quality tea extracts in food industry were explored.Future trends of tea extracts and relevant suggestions were presented.

Gallic acid-grafted chitosan antibacterial hydrogel incorporated with polydopamine-modified hydroxyapatite for enhancing bone healing.

An open critical-size bone defect is a major medical problem because of the difficulty in self-healing, leading to an increased risk of bacterial infection owing to wound exposure, resulting in treatment failure. Herein, a composite hydrogel was synthesized by chitosan, gallic acid, and hyaluronic acid, termed "CGH." Hydroxyapatite was modified with polydopamine (PDA@HAP) and introduced to CGH to obtain a mussel-inspired mineralized hydrogel (CGH/PDA@HAP). The CGH/PDA@HAP hydrogel exhibited excellent mechanical performances, including self-healing and injectable properties. Owing to its three-dimensional porous structure and polydopamine modifications, the cellular affinity of the hydrogel was enhanced. When adding PDA@HAP into CGH, Ca2+ and PO4 3- could release and then promoted differentiation of BMSCs into osteoblasts. Without any osteogenic agent or stem cells, the area of new bone at the site of defect was enhanced and the newly formed bone had a dense trabecular structure after implanting of the CGH/PDA@HAP hydrogel for 4 and 8 weeks. Moreover, the growth of Staphylococcus aureus and Escherichia coli was effectively inhibited through the grafting of gallic acid onto chitosan. Above, this study provides a reasonable alternative strategy to manage open bone defects.

Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation.

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.

Advances in Hypoxia-Inducible Factor-1α Stabilizer Deferoxamine in Tissue Engineering.

Deferoxamine (DFO) is an iron chelator with FDA approval for the clinical treatment of iron excess. As a well-established stabilizer of hypoxia-inducible factor-1α (HIF-1α), DFO can efficiently upregulate HIF-1α and relevant downstream angiogenic factors, leading to accelerated vascularization. Moreover, as increasing studies have focused on DFO as a hypoxia-mimetic agent in recent years, it has been shown that DFO exhibited multiple functions, including stem cell regulation, immunoregulation, provascularization, and pro-osteogenesis. On the contrary, DFO can bind excess iron ions in wounds of chronic inflammation, while serving as an antioxidant with the characteristic of removing reactive oxygen species. Collectively, these characteristics make DFO a potent modulator in tissue engineering for increasing tissue integration of biomaterials in vivo and facilitating wound healing. This review outlines the activity of DFO as a representative hypoxia-mimetic agent in cells as well as the evolution of its application in tissue engineering. It can be concluded that DFO is a medication with tremendous promise and application value in future trends, which can optimize biomaterials and existing tissue engineering techniques for tissue regeneration.

Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach.

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.

Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities.

MOTIVATION: Antimicrobial peptides (AMPs) have the potential to inhibit multiple types of pathogens and to heal infections. Computational strategies can assist in characterizing novel AMPs from proteome or collections of synthetic sequences and discovering their functional abilities toward different microbial targets without intensive labor. RESULTS: Here, we present a deep learning-based method for computer-aided novel AMP discovery that utilizes the transformer neural network architecture with knowledge from natural language processing to extract peptide sequence information. We implemented the method for two AMP-related tasks: the first is to discriminate AMPs from other peptides, and the second task is identifying AMPs functional activities related to seven different targets (gram-negative bacteria, gram-positive bacteria, fungi, viruses, cancer cells, parasites and mammalian cell inhibition), which is a multi-label problem. In addition, asymmetric loss was adopted to resolve the intrinsic imbalance of dataset, particularly for the multi-label scenarios. The evaluation showed that our proposed scheme achieves the best performance for the first task (96.85% balanced accuracy) and has a more unbiased prediction for the second task (79.83% balanced accuracy averaged across all functional activities) when compared with that of strategies without imbalanced learning or deep learning. AVAILABILITY AND IMPLEMENTATION: The source code and data of this study are available at https://github.com/BiOmicsLab/TransImbAMP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications.

Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version. Due to the increasing number of studies on the mechanism of PTMs in the past few years, a great deal of upstream regulatory proteins of PTM substrate sites have been revealed. The updated dbPTM thus collates regulatory information from databases and literature, and merges them into a protein-protein interaction network. To enhance the understanding of the association between PTMs and molecular functions/cellular processes, the functional annotations of PTMs are curated and integrated into the database. In addition, the existing PTM-related resources, including annotation databases and prediction tools are also renewed. Overall, in this update, we would like to provide users with the most abundant data and comprehensive annotations on PTMs of proteins. The updated dbPTM is now freely accessible at https://awi.cuhk.edu.cn/dbPTM/.

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches.

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase-substrate interactions.

Ubiquitination is an important post-translational modification, which controls protein turnover by labeling malfunctional and redundant proteins for proteasomal degradation, and also serves intriguing non-proteolytic regulatory functions. E3 ubiquitin ligases, whose substrate specificity determines the recognition of target proteins of ubiquitination, play crucial roles in ubiquitin-proteasome system. UbiNet 2.0 is an updated version of the database UbiNet. It contains 3332 experimentally verified E3-substrate interactions (ESIs) in 54 organisms and rich annotations useful for investigating the regulation of ubiquitination and the substrate specificity of E3 ligases. Based on the accumulated ESIs data, the recognition motifs in substrates for each E3 were also identified and a functional enrichment analysis was conducted on the collected substrates. To facilitate the research on ESIs with different categories of E3 ligases, UbiNet 2.0 performed strictly evidence-based classification of the E3 ligases in the database based on their mechanisms of ubiquitin transfer and substrate specificity. The platform also provides users with an interactive tool that can visualize the ubiquitination network of a group of self-defined proteins, displaying ESIs and protein-protein interactions in a graphical manner. The tool can facilitate the exploration of inner regulatory relationships mediated by ubiquitination among proteins of interest. In summary, UbiNet 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about experimentally validated ESIs and a visualized tool for the construction of ubiquitination regulatory networks available at http://awi.cuhk.edu.cn/~ubinet/index.php.

Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies.

As the current worldwide outbreaks of the SARS-CoV-2, it is urgently needed to develop effective therapeutic agents for inhibiting the pathogens or treating the related diseases. Antimicrobial peptides (AMP) with functional activity against coronavirus could be a considerable solution, yet there is no research for identifying anti-coronavirus (anti-CoV) peptides with the computational approach. In this study, we first investigated the physiochemical and compositional properties of the collected anti-CoV peptides by comparing against three other negative sets: antivirus peptides without anti-CoV function (antivirus), regular AMP without antivirus functions (non-AVP) and peptides without antimicrobial functions (non-AMP). Then, we established classifiers for identifying anti-CoV peptides between different negative sets based on random forest. Imbalanced learning strategies were adopted due to the severe class-imbalance within the datasets. The geometric mean of the sensitivity and specificity (GMean) under the identification from antivirus, non-AVP and non-AMP reaches 83.07%, 85.51% and 98.82%, respectively. Then, to pursue identifying anti-CoV peptides from broad-spectrum peptides, we designed a double-stages classifier based on the collected datasets. In the first stage, the classifier characterizes AMPs from regular peptides. It achieves an area under the receiver operating curve (AUCROC) value of 97.31%. The second stage is to identify the anti-CoV peptides between the combined negatives of other AMPs. Here, the GMean of evaluation on the independent test set is 79.42%. The proposed approach is considered as an applicable scheme for assisting the development of novel anti-CoV peptides. The datasets and source codes used in this study are available at https://github.com/poncey/PreAntiCoV.

Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian.

Lysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict efficiently only on histone or non-histone protein. Therefore, this work aims to give a more balanced performance in different species, here plant (non-histone) and mammalian (histone) are involved. SVM (support vector machine) and RF (random forest) were employed in this study. According to the results of cross-validations, the RF classifier based on EGAAC attribute achieved the best predictive performance which performs competitively good as existed methods, meanwhile more robust when dealing with imbalanced datasets. Moreover, an independent test was carried out, which compared the performance of this study and existed methods based on the same features or the same classifier. The classifiers of SVM and RF could achieve best performances with 92% sensitivity, 88% specificity, 90% accuracy, and an MCC of 0.80 in the mammalian dataset, and 77% sensitivity, 83% specificity, 70% accuracy and 0.54 MCC in a relatively small dataset of mammalian and a large-scaled plant dataset respectively. Moreover, a cross-species independent testing was also carried out in this study, which has proved the species diversity in plant and mammalian.

Structure of the apoptosome: mechanistic insights into activation of an initiator caspase from Drosophila.

Apoptosis is executed by a cascade of caspase activation. The autocatalytic activation of an initiator caspase, exemplified by caspase-9 in mammals or its ortholog, Dronc, in fruit flies, is facilitated by a multimeric adaptor complex known as the apoptosome. The underlying mechanism by which caspase-9 or Dronc is activated by the apoptosome remains unknown. Here we report the electron cryomicroscopic (cryo-EM) structure of the intact apoptosome from Drosophila melanogaster at 4.0 Å resolution. Analysis of the Drosophila apoptosome, which comprises 16 molecules of the Dark protein (Apaf-1 ortholog), reveals molecular determinants that support the assembly of the 2.5-MDa complex. In the absence of dATP or ATP, Dronc zymogen potently induces formation of the Dark apoptosome, within which Dronc is efficiently activated. At 4.1 Å resolution, the cryo-EM structure of the Dark apoptosome bound to the caspase recruitment domain (CARD) of Dronc (Dronc-CARD) reveals two stacked rings of Dronc-CARD that are sandwiched between two octameric rings of the Dark protein. The specific interactions between Dronc-CARD and both the CARD and the WD40 repeats of a nearby Dark protomer are indispensable for Dronc activation. These findings reveal important mechanistic insights into the activation of initiator caspase by the apoptosome.

Mechanistic insights into CED-4-mediated activation of CED-3.

Programmed cell death in Caenorhabditis elegans requires activation of the caspase CED-3, which strictly depends on CED-4. CED-4 forms an octameric apoptosome, which binds the CED-3 zymogen and facilitates its autocatalytic maturation. Despite recent advances, major questions remain unanswered. Importantly, how CED-4 recognizes CED-3 and how such binding facilitates CED-3 activation remain completely unknown. Here we demonstrate that the L2' loop of CED-3 directly binds CED-4 and plays a major role in the formation of an active CED-4-CED-3 holoenzyme. The crystal structure of the CED-4 apoptosome bound to the L2' loop fragment of CED-3, determined at 3.2 Å resolution, reveals specific interactions between a stretch of five hydrophobic amino acids from CED-3 and a shallow surface pocket within the hutch of the funnel-shaped CED-4 apoptosome. Structure-guided biochemical analysis confirms the functional importance of the observed CED-4-CED-3 interface. Structural analysis together with published evidence strongly suggest a working model in which two molecules of CED-3 zymogen, through specific recognition, are forced into the hutch of the CED-4 apoptosome, consequently undergoing dimerization and autocatalytic maturation. The mechanism of CED-3 activation represents a major revision of the prevailing model for initiator caspase activation.

Molecular mechanism for inhibition of a critical component in the Arabidopsis thaliana abscisic acid signal transduction pathways, SnRK2.6, by protein phosphatase ABI1.

Subclass III SnRK2s (SnRK2.6/2.3/2.2) are the key positive regulators of abscisic acid (ABA) signal transduction in Arabidopsis thaliana. The kinases, activated by ABA or osmotic stress, phosphorylate stress-related transcription factors and ion channels, which ultimately leads to the protection of plants from dehydration or high salinity. In the absence of stressors, SnRK2s are subject to negative regulation by group A protein phosphatase type 2Cs (PP2C), whereas the underlying molecular mechanism remains to be elucidated. Here we report the crystal structure of the kinase domain of SnRK2.6 at 2.6-Å resolution. Structure-guided biochemical analyses identified two distinct interfaces between SnRK2.6 and ABI1, a member of group A PP2Cs. Structural modeling suggested that the two interfaces lock SnRK2.6 and ABI1 in an orientation such that the activation loop of SnRK2.6 is posited to the catalytic site of ABI1 for dephosphorylation. These studies revealed the molecular basis for PP2Cs-mediated inhibition of SnRK2s and provided important insights into the downstream signal transduction of ABA.