Hinokione: an abietene diterpene with pancreatic ß cells regeneration and hypoglycemic activity, and other derivatives with novel structures from the woods of Agathis dammara.

In this study, 14 abietene and pimarene diterpenoids were isolated from the woods of Agathis dammara. Among them, 4 new compounds, dammarone A-C and dammaric acid A (1-4), were firstly reported, respectively. The structure of the new compounds was determined by HR ESI-MS and 1D/2D NMR spectroscopy, and their absolute configuration was determined by electronic circular dichroism (ECD) exciton chirality method. The hypoglycemic effect of all compounds was evaluated by transgenic zebrafish model, and the structure-activity relationship was discussed. Hinokione (7, HO) has low toxicity and significant hypoglycemic effects on zebrafish, the mechanism is mainly by promoting the differentiation of zebrafish pancreatic endocrine precursor cells (PEP cells) into ß cells, thereby promoting the regeneration of pancreatic ß cells.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Enhancing Predictions of Drug Solubility through Multidimensional Structural Characterization Exploitation.

Solubility is not only a significant physical property of molecules but also a vital factor in smallmolecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resources. The accurate prediction of drug solubility using computational methods has long been a goal for researchers. In this study, we introduce MSCSol, a solubility prediction model that integrates multidimensional molecular structure information. We incorporate a graph neural network with geometric vector perceptrons (GVP-GNN) to encode 3D molecular structures, representing spatial arrangement and orientation of atoms, as well as atomic sequences and interactions. We also employ Selective Kernel Convolution combined with Global and Local attention mechanisms to capture molecular features context at different scales. Additionally, various descriptors are calculated to enrich the molecular representation. For the 2D and 3D structural data of molecules, we design different data augmentation strategies to enhance generalization ability and prevent the model from learning irrelevant information. Extensive experiments on benchmark and independent datasets demonstrate MSCSol's superior performance. Ablation studies further confirm the effectiveness of different modules. Interpretability analysis highlights the importance of various atomic groups and substructures for solubility and verifies that our model effectively captures functional molecular structures and higher-order knowledge. The source code and datasets are freely available at https://github.com/ZiyuFanCSU/MSCSol.

The effectiveness and feasibility of ball-milled powdered activated carbon (BPAC) for removal of organic pesticides in conventional drinking water treatment process.

In the conventional drinking water treatment process (CDWTP), powdered activated carbon (PAC) is commonly used for removing organic pesticides, or other organic contaminants. However, the hydraulic retention time (HRT) in CDWTP is insufficient for fulfilling PAC adsorption equilibrium to realize its full capacity. This study examined the adsorption kinetics, adsorption thermal dynamics, and removal efficiency for six organic pesticides using the ball-milled PAC (BPAC) with varying particle sizes in CDWTP. Based on the experiments with the pesticides of atrazine, diazinon, dimethoate, fenitrothion, isoproturon and thiometon, the results indicated that as the particle size reduced from around 38 µm for the commercial PAC to 1 µm for the BPAC, the adsorption rates for hydrophobic pesticides increased up to twentyfold. Diffusional adsorption from the bulk solution to the external PAC surface is the most likely predominant mechanism. This could allow a sufficient pesticides' adsorption within the limited HRT and to achieve a great depth removal of these toxic compounds. However, the addition of BPAC with a diameter of 1 µm was observed to significantly increase residual particles in treated water after the conventional treatment process. With a further systematic evaluation of both adsorption rate and particle penetration, a particle size of around 6 µm BPAC was considered a practical compromise between the adsorption rate and particle penetration for real application. Results from five surface waters of different water quality indicated that, compared to commercial PAC, application of 6 µm BPAC could achieve up to a 75% reduction in adsorbent dosage while maintaining around the same pesticide removal efficiencies. Additionally, thermodynamic analyses suggest that adsorption of these pesticides could be enthalpically or entropically driven depending on the degree of pesticide hydrophobicity.

Differential selection of yield and quality traits has shaped genomic signatures of cowpea domestication and improvement.

Cowpeas (tropical legumes) are important in ensuring food and nutritional security in developing countries, especially in sub-Saharan Africa. Herein, we report two high-quality genome assemblies of grain and vegetable cowpeas and we re-sequenced 344 accessions to characterize the genomic variations landscape. We identified 39 loci for ten important agronomic traits and more than 541 potential loci that underwent selection during cowpea domestication and improvement. In particular, the synchronous selections of the pod-shattering loci and their neighboring stress-relevant loci probably led to the enhancement of pod-shattering resistance and the compromise of stress resistance during the domestication from grain to vegetable cowpeas. Moreover, differential selections on multiple loci associated with pod length, grain number per pod, seed weight, pod and seed soluble sugars, and seed crude proteins shaped the yield and quality diversity in cowpeas. Our findings provide genomic insights into cowpea domestication and improvement footprints, enabling further genome-informed cultivar improvement of cowpeas.

Ferroptosis: An important mechanism of disease mediated by the gut-liver-brain axis.

AIMS: As a unique iron-dependent non-apoptotic cell death, Ferroptosis is involved in the pathogenesis and development of many human diseases and has become a research hotspot in recent years. However, the regulatory role of ferroptosis in the gut-liver-brain axis has not been elucidated. This paper summarizes the regulatory role of ferroptosis and provides theoretical basis for related research. MATERIALS AND METHODS: We searched PubMed, CNKI and Wed of Science databases on ferroptosis mediated gut-liver-brain axis diseases, summarized the regulatory role of ferroptosis on organ axis, and explained the adverse effects of related regulatory effects on various diseases. KEY FINDINGS: According to our summary, the main way in which ferroptosis mediates the gut-liver-brain axis is oxidative stress, and the key cross-talk of ferroptosis affecting signaling pathway network is Nrf2/HO-1. However, there were no specific marker between different organ axes mediate by ferroptosis. SIGNIFICANCE: Our study illustrates the main ways and key cross-talk of ferroptosis mediating the gut-liver-brain axis, providing a basis for future research.

Multipatterned Chondrocytes' Scaffolds by FL-MOPL with a BSA-GMA Hydrogel to Regulate Chondrocytes' Morphology.

Repairing articular cartilage damage is challenging due to its low regenerative capacity. In vitro, cartilage regeneration is a potential strategy for the functional reconstruction of cartilage defects. A hydrogel is an advanced material for mimicking the extracellular matrix (ECM) due to its hydrophilicity and biocompatibility, which is known as an ideal scaffold for cartilage regeneration. However, chondrocyte culture in vitro tends to dedifferentiate, leading to fibrosis and reduced mechanical properties of the newly formed cartilage tissue. Therefore, it is necessary to understand the mechanism of modulating the chondrocytes' morphology. In this study, we synthesize photo-cross-linkable bovine serum albumin-glycidyl methacrylate (BSA-GMA) with 65% methacrylation. The scaffolds are found to be suitable for chondrocyte growth, which are fabricated by homemade femtosecond laser maskless optical projection lithography (FL-MOPL). The large-area chondrocyte scaffolds have holes with interior angles of triangle (T), quadrilateral (Q), pentagon (P), hexagonal (H), and round (R). The FL-MOPL polymerization mechanism, swelling, degradation, and biocompatibility of the BSA-GMA hydrogel have been investigated. Furthermore, cytoskeleton and nucleus staining reveals that the R-scaffold with larger interior angle is more effective in maintaining chondrocyte morphology and preventing dedifferentiation. The scaffold's ability to maintain the chondrocytes' morphology improves as its shape matches that of the chondrocytes. These results suggest that the BSA-GMA scaffold is a suitable candidate for preventing chondrocyte differentiation and supporting cartilage tissue repair and regeneration. The proposed method for chondrocyte in vitro culture by developing biocompatible materials and flexible fabrication techniques would broaden the potential application of chondrocyte transplants as a viable treatment for cartilage-related diseases.

Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model.

AIMS: To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions. METHODS: A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation. RESULTS: Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure. CONCLUSIONS: The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

Cardiac Resolvin D2 ameliorates sepsis-induced cardiomyopathy via inhibiting Caspase-11/GSDMD dependent pyroptosis.

BACKGROUND: Sepsis-induced cardiomyopathy (SICM) is common complication in septic patients with a high mortality and is characterized by an abnormal inflammation response, which was precisely regulated by endogenous specialized pro-resolving mediators (SPMs). However, the metabolic changes of cardiac SPMs during SICM and the roles of SPMs subset in the development of SICM remain unknown. METHODS: In this work, the SPMs concentration was assessed using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) of SICM mice and SICM patients. The cardiac function was measured by echocardiography after the treatment of a SPMs subset, termed Resolvin D2 (RvD2). Caspase-11-/-, GSDMD-/- and double deficient (Caspase-11-/-GSDMD-/-) mice were used to clarify the mechanisms of RvD2 in SICM. RESULTS: We found that endogenous cardiac SPMs were disorders and RvD2 was decreased significantly and correlated with left ventricular ejection fraction (LVEF) and ß-BNP, cTnT in Lipopolysaccharide/Cecum ligation and puncture (CLP) induced SICM models. Treatment with RvD2 attenuated lethality, cardiac dysfunction and cardiomyocytes death during SICM. Mechanistically, RvD2 alleviated SICM via inhibiting Caspase-11/GSDMD-mediated cardiomyocytes pyroptosis. Finally, the plasma levels of RvD2 were also decreased and significantly correlated with IL-1ß, ß-BNP, cTnT and LVEF in patients with SICM. Of note, plasma RvD2 level is indicator of SICM patients from healthy controls or sepsis patients. CONCLUSION: These findings suggest that decreased cardiac RvD2 may involve in the pathogenesis of SICM. In addition, treatment with RvD2 represents a novel therapeutic strategy for SICM by inhibiting cardiomyocytes pyroptosis.

CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object Detection.

The effectiveness of the SAR object detection technique based on Convolutional Neural Networks (CNNs) has been widely proven, and it is increasingly used in the recognition of ship targets. Recently, efforts have been made to integrate transformer structures into SAR detectors to achieve improved target localization. However, existing methods rarely design the transformer itself as a detector, failing to fully leverage the long-range modeling advantages of self-attention. Furthermore, there has been limited research into multi-class SAR target detection. To address these limitations, this study proposes a SAR detector named CCDN-DETR, which builds upon the framework of the detection transformer (DETR). To adapt to the multiscale characteristics of SAR data, cross-scale encoders were introduced to facilitate comprehensive information modeling and fusion across different scales. Simultaneously, we optimized the query selection scheme for the input decoder layers, employing IOU loss to assist in initializing object queries more effectively. Additionally, we introduced constrained contrastive denoising training at the decoder layers to enhance the model's convergence speed and improve the detection of different categories of SAR targets. In the benchmark evaluation on a joint dataset composed of SSDD, HRSID, and SAR-AIRcraft datasets, CCDN-DETR achieves a mean Average Precision (mAP) of 91.9%. Furthermore, it demonstrates significant competitiveness with 83.7% mAP on the multi-class MSAR dataset compared to CNN-based models.

TRPV4 antagonist suppresses retinal ganglion cell apoptosis by regulating the activation of CaMKII and TNF-α expression in a chronic ocular hypertension rat model.

Glaucoma is characterized by a progressive loss of retinal ganglion cells (RGCs), leading to irreversible visual function impairment. Sustained increase in intraocular pressure represents a major risk factor for glaucoma, yet the underlying mechanisms of RGC apoptosis induced by intraocular pressure remains unclear. This study aims to investigate the role of TRPV4 in RGC apoptosis in a rat model of chronic ocular hypertension (COH) and the underlying molecular mechanism. In the COH rat models, we evaluated the visual function, retinal pathological changes and RGC apoptosis. TRPV4 expression and downstream signaling molecules were also detected. We found that RGC density decreased and RGC apoptosis was induced in COH eyes compared with control eyes. TRPV4 expression increased significantly in response to elevated IOP. TRPV4 inhibition by the TRPV4 antagonist HC-067047 (HC-067) suppressed RGC apoptosis and protected visual function. HC-067 treatment upregulated the phosphorylation of CaMKII in both control and COH eyes. Finally, HC-067 treatment suppressed the production of TNF-α induced by ocular hypertension. The TRPV4 antagonist HC-067 might suppress RGC apoptosis by regulating the activation of CaMKII and inhibiting the production of TNF-α in the COH model. This indicated that TRPV4 antagonists may be a potential and novel therapeutic strategy for glaucoma.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Genetic insights into repurposing statins for hyperthyroidism prevention: a drug-target Mendelian randomization study.

Background: Current therapeutic measures for thyroid dysfunction are limited and often accompanied by adverse effects. The use of lipid-lowering drugs like statins has recently been associated with lower thyroid eye diseases risk. Objective: To investigate the implications of genetically proxied lipid-lowering drugs on thyroid dysfunction. Methods: In this drug-target Mendelian randomization (MR) study, we utilized genetic variants within drug target genes associated with low-density lipoprotein (LDL) or triglyceride (TG), derived from a genome-wide association study (GWAS) meta-analysis (N ≤ 188,577), to simulate lifelong drug interventions. Genetic summary statistics for thyroid dysfunction outcomes were retrieved from GWAS datasets of Thyroid Omics Consortium (N ≤ 54,288) and UK Biobank (N = 484,598). Inverse-variance-weighted MR (IVW-MR) method was performed as primary analysis, followed by validation in colocalization analysis. A subsequent two-step MR analysis was conducted to identify biomarkers mediating the identified drug-outcome association. Results: In IVW-MR analysis, genetic mimicry of 3-hydroxy-3-methylglutarylcoenzyme reductase (HMGCR) inhibitors (e.g. statins) was significantly associated with lower risk of hyperthyroidism in two independent datasets (OR1, 0.417 per 1-mmol/L lower in LDL-C; 95% CI 0.262 to 0.664; P1 = 2.262 × 10-4; OR2 0.996; 95% CI 0.993-0.998; P2 = 0.002). Two-step MR analysis revealed eighteen biomarkers linked to genetic mimicry of HMGCR inhibition, and identified insulin-like growth factor 1 (IGF-1) levels mediating 2.108% of the negative causal relationship between HMGCR inhibition and hyperthyroidism. Conclusion: This study supports HMGCR inhibition as a promising therapeutic strategy for hyperthyroidism and suggests its underlying mechanisms may extend beyond lipid metabolism. Further investigations through laboratory studies and clinical trials are necessary to confirm and elucidate these findings.

Unraveling the role of low-density lipoprotein-related genes in lung adenocarcinoma: Insights into tumor microenvironment and clinical prognosis.

BACKGROUND: The hypothesized link between low-density lipoprotein (LDL) and oncogenesis has garnered significant interest, yet its explicit impact on lung adenocarcinoma (LUAD) remains to be elucidated. This investigation aims to demystify the function of LDL-related genes (LRGs) within LUAD, endeavoring to shed light on the complex interplay between LDL and carcinogenesis. METHODS: Leveraging single-cell transcriptomics, we examined the role of LRGs within the tumor microenvironment (TME). The expression patterns of LRGs across diverse cellular phenotypes were delineated using an array of computational methodologies, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore. CellChat facilitated the exploration of distinct cellular interactions within LDL_low and LDL_high groups. The findmarker utility, coupled with Pearson correlation analysis, facilitated the identification of pivotal genes correlated with LDL indices. An integrative approach to transcriptomic data analysis was adopted, utilizing a machine learning framework to devise an LDL-associated signature (LAS). This enabled the delineation of genomic disparities, pathway enrichments, immune cell dynamics, and pharmacological sensitivities between LAS stratifications. RESULTS: Enhanced cellular crosstalk was observed in the LDL_high group, with the CoxBoost+Ridge algorithm achieving the apex c-index for LAS formulation. Benchmarking against 144 extant LUAD models underscored the superior prognostic acuity of LAS. Elevated LAS indices were synonymous with adverse outcomes, diminished immune surveillance, and an upsurge in pathways conducive to neoplastic proliferation. Notably, a pronounced susceptibility to paclitaxel and gemcitabine was discerned within the high-LAS cohort, delineating prospective therapeutic corridors. CONCLUSION: This study elucidates the significance of LRGs within the TME and introduces an LAS for prognostication in LUAD patients. Our findings accentuate putative therapeutic targets and elucidate the clinical ramifications of LAS deployment.

Whole Genome Duplication Events Likely Contributed to the Aquatic Adaptive Evolution of Parkerioideae.

As the only aquatic lineage of Pteridaceae, Parkerioideae is distinct from many xeric-adapted species of the family and consists of the freshwater Ceratopteris species and the only mangrove ferns from the genus Acrostichum. Previous studies have shown that whole genome duplication (WGD) has occurred in Parkerioideae at least once and may have played a role in their adaptive evolution; however, more in-depth research regarding this is still required. In this study, comparative and evolutionary transcriptomics analyses were carried out to identify WGDs and explore their roles in the environmental adaptation of Parkerioideae. Three putative WGD events were identified within Parkerioideae, two of which were specific to Ceratopteris and Acrostichum, respectively. The functional enrichment analysis indicated that the lineage-specific WGD events have played a role in the adaptation of Parkerioideae to the low oxygen concentrations of aquatic habitats, as well as different aquatic environments of Ceratopteris and Acrostichum, such as the adaptation of Ceratopteris to reduced light levels and the adaptation of Acrostichum to high salinity. Positive selection analysis further provided evidence that the putative WGD events may have facilitated the adaptation of Parkerioideae to changes in habitat. Moreover, the gene family analysis indicated that the plasma membrane H+-ATPase (AHA), vacuolar H+-ATPase (VHA), and suppressor of K+ transport growth defect 1 (SKD1) may have been involved in the high salinity adaptation of Acrostichum. Our study provides new insights into the evolution and adaptations of Parkerioideae in different aquatic environments.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design.

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Targeting NEDD8 suppresses surgical stress-facilitated metastasis of colon cancer via restraining regulatory T cells.

Regulatory T cells (Tregs) are a key determinant for the immunosuppressive and premetastatic niche for cancer progression after surgery resection. However, the precise mechanisms regulating Tregs function during surgical stress-facilitated cancer metastasis remain unknown. This study aims to unravel the mechanisms and explore potential strategies for preventing surgical stress-induced metastasis by targeting NEDD8. Using a surgical stress mouse model, we found that surgical stress results in the increased expression of NEDD8 in Tregs. NEDD8 depletion abrogates postoperative lung metastasis of colon cancer cells by inhibiting Treg immunosuppression and thereby partially recovering CD8+T cell and NK cell-mediated anti-tumor immunity. Furthermore, Treg mitophagy and mitochondrial respiration exacerbated in surgically stressed mice were attenuated by NEDD8 depletion. Our observations suggest that cancer progression may result from surgery-induced enhancement of NEDD8 expression and the subsequent immunosuppressive function of Tregs. More importantly, depleting or inhibiting NEDD8 can be an efficient strategy to reduce cancer metastasis after surgery resection by regulating the function of Tregs.

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides.

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications.

A view of the pan-genome of domesticated Cowpea (Vigna unguiculata [L.] Walp.).

Cowpea, Vigna unguiculata L. Walp., is a diploid warm-season legume of critical importance as both food and fodder in sub-Saharan Africa. This species is also grown in Northern Africa, Europe, Latin America, North America, and East to Southeast Asia. To capture the genomic diversity of domesticates of this important legume, de novo genome assemblies were produced for representatives of six subpopulations of cultivated cowpea identified previously from genotyping of several hundred diverse accessions. In the most complete assembly (IT97K-499-35), 26,026 core and 4963 noncore genes were identified, with 35,436 pan genes when considering all seven accessions. GO terms associated with response to stress and defense response were highly enriched among the noncore genes, while core genes were enriched in terms related to transcription factor activity, and transport and metabolic processes. Over 5 million single nucleotide polymorphisms (SNPs) relative to each assembly and over 40 structural variants >1 Mb in size were identified by comparing genomes. Vu10 was the chromosome with the highest frequency of SNPs, and Vu04 had the most structural variants. Noncore genes harbor a larger proportion of potentially disruptive variants than core genes, including missense, stop gain, and frameshift mutations; this suggests that noncore genes substantially contribute to diversity within domesticated cowpea.