Serum taurine affects lung cancer progression by regulating tumor immune escape mediated by the immune microenvironment.

INTRODUCTION: Taurine is a naturally occurring sulfonic acid involved in various physiological and pathological processes, such as the regulation of calcium signaling, immune function, inflammatory response, and cellular aging. It has the potential to predict tumor malignant transformation and formation. Our previous work discovered the elevated taurine in lung cancer patients. However, the precise impact and mechanism of elevated serum taurine levels on lung cancer progression and the suitability of taurine or taurine-containing drinks for lung cancer patients remain unclear. OBJECTIVES: Our study aimed to systematically investigate the role of taurine in lung cancer, with the ultimate goal of contributing novel strategies for lung cancer treatment. METHODS: Lung cancer C57 and nude mice models, RNA sequencing, and stable transfection were applied to explored the effects and mechanisms of taurine on lung cancer. Tissues of 129 non-small cell lung cancer (NSCLC) patients derived from 2014 to 2017 for immunohistochemistry were collected in Taihe Hospital. RESULTS: Low doses of taurine, as well as taurine-infused beverages at equivalent doses, significantly enhanced lung tumor growth. Equally intriguing is that the promoting effect of taurine on lung cancer progression wanes as the dosage increases. The Nuclear factor erythroid 2-like 1 (Nfe2l1 or Nrf1)-reactive oxygen species (ROS)-PD-1 axis may be a potential mechanism for dual role of taurine in lung cancer progression. However, taurine's impacts on lung cancer progression and the anti-tumor function of Nfe2l1 were mainly determined by the immune competence. Taurine inhitited lung tumor growth probably by inhibiting NF-κB-mediated inflammatory responses in nude mice rather than by affecting Nfe2l1 function. As patients age increased, Nfe2l1 gene and protein gradually returned to the levels observed in healthy individuals, but lost its anti-lung cancer effects. CONCLUSIONS: Taurine emerges as a potential biomarker for lung cancer progression, predicting poor prognosis and unsuitability for specific patients. Lung cancer patients, especially young patients, should be conscious of potential effects of taurine-containing drinks. Conversely, taurine or its drinks may be more suitable for older or immune-deficient patients.

Discovery of potent LRRK2 inhibitors by ensemble virtual screening strategy and bioactivity evaluation.

Leucine-rich repeat kinase 2 (LRRK2) has been reported to be associated with familial and idiopathic Parkinson's disease (PD) risk and is a promising target for drug discovery against PD. To identify novel and effective LRRK2 inhibitors, an ensemble virtual screening strategy by combining fingerprint similarity, complex-based pharmacophore and structure-based molecular docking was proposed and applied. Using this strategy, we finally selected 25 compounds from ∼1.7 million compounds for in vitro and in vivo tests. Firstly, the kinase inhibitory activity tests of compounds based on ADP-Glo assay identified three most potent compounds LY2023-19, LY2023-24 and LY2023-25 with IC50 of 556.4 nM, 218.1 nM and 22.4 nM for LRRK2 G2019S mutant, respectively. The further cellular experiments also indicated that three hit compounds significantly inhibited Ser935 phosphorylation of both wide-type and G2019S LRRK2 with IC50 ranging from 27 nM to 1674 nM in HEK293T cells. The MD simulations of three compounds and G2019S LRRK2 showed the hydrogen bond formed by Glu1948 and Ala1950 is crucial for the binding of LRRK2. Afterwards, 6-OHDA-induced PD zebrafish model was constructed to evaluate the neuroprotective effects of hit compounds. The locomotion of the 6-OHDA treated zebrafish larvae was improved after treatment with LY2023-24. The obtained results can provide valuable guidance for the development of PD drugs by targeting LRRK2.

Study on interaction with high-abundant blood proteins and identification of low-abundant proteins to 5-phenyl-1-(p-tolyl)-1 H-1,2,3-triazole by serum proteomics.

In this study, a comprehensive investigation was undertaken to elucidate a simple triazole compound, 5-phenyl-1-(p-tolyl)-1 H-1,2,3-triazole (PPTT), its interactions with high-abundant proteins and identification of low-abundant proteins by serum proteomics. Employing a combination of spectroscopic techniques and computational chemistry, the interactions between PPTT and three high-abundance blood globular proteins, namely human serum albumin (HSA), human immunoglobulin G (HIgG), and hemoglobin (BHb), were explored, thereby ascertaining their binding constants and thermodynamic parameters at the molecular level. Subsequently, based on the differential proteomics, utilizing two-dimensional gel electrophoresis (2-DE) in conjunction with matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF-MS), the research team isolated and identified differentially expressed low-abundance proteins in human blood serum samples following exposure to PPTT. The results showed that there were twenty highly expressed proteins identified from blood serum samples intervened by PPTT. Combining bioinformatics techniques, these proteins were classified, providing preliminary insights like preproprotein or precursors inhibiting the activity of elastase, defending and regulating the immune system, carrying lipid, and other functions into their biological functionalities. One of the differential proteins, apolipoprotein A-1 (ApoA-1) protein, was selected as a possible target to explore the mechanism of action of PPTT intervention on the related signaling pathways involved in human hepatocellular carcinomas(Hep G2) cells. These research findings offer scientifically sound guidance for further in-depth exploration, development, and application of the 1,2,3-triazole compound.

Structural characterization and mechanisms of macrophage immunomodulatory activity of a novel polysaccharide with a galactose backbone from the processed Polygonati Rhizoma.

A purified polysaccharide with a galactose backbone (SPR-1, Mw 3,622 Da) was isolated from processed Polygonati Rhizoma with black beans (PRWB) and characterized its chemical properties. The backbone of SPR-1 consisted of [(4)-ß-D-Galp-(1]9 â†’ 4,6)-ß-D-Galp-(1 â†’ 4)-α-D-GalpA-(1 â†’ 4)-α-D-GalpA-(1 â†’ 4)-α-D-Glcp-(1 â†’ 4,6)-α-D-Glcp-(1 â†’ 4)-α/ß-D-Glcp, with a branch chain of R1: ß-D-Galp-(1 â†’ 3)-ß-D-Galp-(1→ connected to the →4,6)-ß-D-Galp-(1→ via O-6, and a branch chain of R2: α-D-Glcp-(1 â†’ 6)-α-D-Glcp-(1→ connected to the →4,6)-α-D-Glcp-(1→ via O-6. Immunomodulatory assays showed that the SPR-1 significantly activated macrophages, and increased secretion of NO and cytokines (i.e., IL-1ß and TNF-α), as well as promoted the phagocytic activities of cells. Furthermore, isothermal titration calorimetry (ITC) analysis and molecular docking results indicated high-affinity binding between SPR-1 and MD2 with the equilibrium dissociation constant (K D) of 18.8 µM. It was suggested that SPR-1 activated the immune response through Toll-like receptor 4 (TLR4) signaling and downstream responses. Our research demonstrated that the SPR-1 has a promising candidate from PRWB for the TLR4 agonist to induce immune response, and also provided an easily accessible way that can be used for PR deep processing.

Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites.

Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications. We introduce EasIFA, an enzyme active site annotation algorithm that fuses latent enzyme representations from the Protein Language Model and 3D structural encoder, and then aligns protein-level information with the knowledge of enzymatic reactions using a multi-modal cross-attention framework. EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, achieving a speed increase ranging from 650 to 1400 times while enhancing annotation quality. This makes EasIFA a suitable replacement for conventional tools in both industrial and academic settings. EasIFA can also effectively transfer knowledge gained from coarsely annotated enzyme databases to smaller, high-precision datasets, highlighting its ability to model sparse and high-quality databases. Additionally, EasIFA shows potential as a catalytic site monitoring tool for designing enzymes with desired functions beyond their natural distribution.

Identification of novel LRRK2 inhibitors by structure-based virtual screening and alchemical free energy calculation.

The Leucine-rich repeat kinase 2 (LRRK2) target has been identified as a promising drug target for Parkinson's disease (PD) treatment. This study focuses on optimizing the activity of LRRK2 inhibitors using alchemical relative binding free energy (RBFE) calculations. Initially, we assessed various free energy calculation methods across different LRRK2 kinase inhibitor scaffolds. The results indicate that alchemical free energy calculations are promising for prospective predictions on LRRK2 inhibitors, especially for the aminopyrimidine scaffold with an RMSE of 1.15 kcal mol-1 and Rp of 0.83. Following this, we optimized a potent LRRK2 kinase inhibitor identified from previous virtual screenings, featuring a novel scaffold. Guided by RBFE predictions using alchemical methods, this optimization led to the discovery of compound LY2023-001. This compound, with a [1,2,4]triazolo[5,6-b]indole scaffold, exhibited enhanced inhibitory activity against G2019S LRRK2 (IC50 = 12.9 nM). Molecular dynamics (MD) simulations revealed that LY2023-001 formed stable hydrogen bonds with Glu1948, and Ala1950 in the G2019S LRRK2 protein. Additionally, its phenyl substituents engage in strong electrostatic interactions with Lys1906 and van der Waals interactions with Leu1885, Phe1890, Val1893, Ile1933, Met1947, Leu1949, Leu2001, Ala2016, and Asp2017. Our findings underscore the potential of computational methods in the successful optimization of small molecules, offering important insights for the development of novel LRRK2 inhibitors.

Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.

MultiCBlo: Enhancing predictions of compound-induced inhibition of cardiac ion channels with advanced multimodal learning.

Predicting compound-induced inhibition of cardiac ion channels is crucial and challenging, significantly impacting cardiac drug efficacy and safety assessments. Despite the development of various computational methods for compound-induced inhibition prediction in cardiac ion channels, their performance remains limited. Most methods struggle to fuse multi-source data, relying solely on specific dataset training, leading to poor accuracy and generalization. We introduce MultiCBlo, a model that fuses multimodal information through a progressive learning approach, designed to predict compound-induced inhibition of cardiac ion channels with high accuracy. MultiCBlo employs progressive multimodal information fusion technology to integrate the compound's SMILES sequence, graph structure, and fingerprint, enhancing its representation. This is the first application of progressive multimodal learning for predicting compound-induced inhibition of cardiac ion channels, to our knowledge. The objective of this study was to predict the compound-induced inhibition of three major cardiac ion channels: hERG, Cav1.2, and Nav1.5. The results indicate that MultiCBlo significantly outperforms current models in predicting compound-induced inhibition of cardiac ion channels. We hope that MultiCBlo will facilitate cardiac drug development and reduce compound toxicity risks. Code and data are accessible at: https://github.com/taowang11/MultiCBlo. The online prediction platform is freely accessible at: https://huggingface.co/spaces/wtttt/PCICB.

Discovery of an Exceptionally Orally Bioavailable and Potent HPK1 PROTAC with Enhancement of Antitumor Efficacy of Anti-PD-L1 Therapy.

HPK1, a well-known negative regulator of T cell receptors, can cause T cell dysfunction when abnormally activated. In this study, a PROTAC C3 was designed and synthesized by optimizing the physicochemical properties of the warhead, linker, and CRBN ligand. C3 demonstrated significant HPK1 degradation with a DC50 of 21.26 nM, excellent oral absorption with a Cmax of 10,899.92 ng/mL, and a bioavailability (F %) of 81.7%. C3 also showed degradation selectivity and potent immune activation effects. Proteomic and WB analyses revealed that immune-activating effect of C3 is attributed to the inhibition of SLP76 and NF-κB signaling pathways, as well as the enhancement of MAPK signaling pathway transduction. In vivo efficacy study demonstrated that oral administration of C3 in combination with anti-PDL1 antibody significantly inhibited tumor growth (tumor growth inhibition = 65.58%). These findings suggest that C3, a novel HPK1 PROTAC, holds promise as a therapeutic agent for tumor immunotherapy.

Discovery of a potent CDKs/FLT3 PROTAC with enhanced differentiation and proliferation inhibition for AML.

AML is an aggressive malignancy of immature myeloid progenitor cells. Discovering effective treatments for AML through cell differentiation and anti-proliferation remains a significant challenge. Building on previous studies on CDK2 PROTACs with differentiation-inducing properties, this research aims to enhance CDKs degradation through structural optimization to facilitate the differentiation and inhibit the proliferation of AML cells. Compound C3, featuring a 4-methylpiperidine ring linker, effectively degraded CDK2 with a DC50 value of 18.73 ± 10.78 nM, and stimulated 72.77 ± 3.51 % cell differentiation at 6.25 nM in HL-60 cells. Moreover, C3 exhibited potent anti-proliferative activity against various AML cell types. Degradation selectivity analysis indicated that C3 could be endowed with efficient degradation of CDK2/4/6/9 and FLT3, especially FLT3-ITD in MV4-11 cells. These findings propose that C3 combined targeting CDK2/4/6/9 and FLT3 with enhanced differentiation and proliferation inhibition, which holds promise as a potential treatment for AML.

Discovery of a Highly Potent and Selective BRD9 PROTAC Degrader Based on E3 Binder Investigation for the Treatment of Hematological Tumors.

BRD9 is a pivotal epigenetic factor involved in cancers and inflammatory diseases. Still, the limited selectivity and poor phenotypic activity of targeted agents make it an atypically undruggable target. PROTAC offers an alternative strategy for overcoming the issue. In this study, we explored diverse E3 ligase ligands for the contribution of BRD9 PROTAC degradation. Through molecular docking, binding affinity analysis, and structure-activity relationship study, we identified a highly potent PROTAC E5, with excellent BRD9 degradation (DC50 = 16 pM) and antiproliferation in MV4-11 cells (IC50 = 0.27 nM) and OCI-LY10 cells (IC50 = 1.04 nM). E5 can selectively degrade BRD9 and induce cell cycle arrest and apoptosis. Moreover, the therapeutic efficacy of E5 was confirmed in xenograft tumor models, accompanied by further RNA-seq analysis. Therefore, these results may pave the way and provide the reference for the discovery and investigation of highly effective PROTAC degraders.

Pathways and mechanism of MRTX1133 binding to KRAS G12D elucidated by molecular dynamics simulations and Markov state models.

KRAS G12D is the most common oncogenic mutation identified in several types of cancer. Therefore, design of inhibitors targeting KRAS G12D represents a promising strategy for anticancer therapy. MRTX1133 is a highly potent inhibitor (approximate experiment Kd ≈ 0.0002 nM) of KRAS G12D and is currently in Phase 1/2 study, however, pathways of the compound binding to KRAS G12D has remained unknown, and the mechanism underlying the complicated dynamic process are challenging to capture experimentally, which hinder the structure-based anti-cancer drug design. Here, using MRTX1133 as a probe, unbiased molecular dynamics (MD) was used to simulate the process of MRTX1133 spontaneously binding to KRAS G12D. In six of 42 independent MD simulation (a total of 99 µs), MRTX1133 was observed to successfully associate with KRAS G12D. The kinetically metastable states refer to the potential pathways of MRTX1133 binding to KRAS G12D were revealed by Markov state models (MSM) analysis. Additionally, 8 key residues that are essential for MRTX1133 recognition and tight binding at the preferred low energy states were identified by MM/GBSA analysis. In sum, this study provides a new perspective on understanding the pathways and mechanism of MRTX1133 binding to KRAS G12D.

Advances in spatial transcriptomics and its applications in cancer research.

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.

Development of a Decafluorobiphenyl Cyclized Peptide Targeting the NEMO-IKKα/ß Interaction that Enhances Cell Penetration and Attenuates Lipopolysaccharide-Induced Acute Lung Injury.

Aberrant canonical NF-κB signaling has been implicated in diseases, such as autoimmune disorders and cancer. Direct disruption of the interaction of NEMO and IKKα/ß has been developed as a novel way to inhibit the overactivation of NF-κB. Peptides are a potential solution for disrupting protein-protein interactions (PPIs); however, they typically suffer from poor stability in vivo and limited tissue penetration permeability, hampering their widespread use as new chemical biology tools and potential therapeutics. In this work, decafluorobiphenyl-cysteine SNAr chemistry, molecular modeling, and biological validation allowed the development of peptide PPI inhibitors. The resulting cyclic peptide specifically inhibited canonical NF-κB signaling in vitro and in vivo, and presented positive metabolic stability, anti-inflammatory effects, and low cytotoxicity. Importantly, our results also revealed that cyclic peptides had huge potential in acute lung injury (ALI) treatment, and confirmed the role of the decafluorobiphenyl-based cyclization strategy in enhancing the biological activity of peptide NEMO-IKKα/ß inhibitors. Moreover, it provided a promising method for the development of peptide-PPI inhibitors.

Improving drug response prediction via integrating gene relationships with deep learning.

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Monitoring dynamics of Kyagar Glacier surge and repeated draining of Ice-dammed lake using multi-source remote sensing.

Glacier surges, a primary factor contributing to various glacial hazards, has long captivated the attention of the global glaciological community. This study delves into the dynamics of Kyagar Glacier surging and the associated drainage features of its Ice-dammed lake, employing high temporal resolution optical imagery. Our findings indicate that the surge on Kyagar Glacier began in late spring and early summer of 2014 and concluded during the summer of 2016. This surge resulted in the transfer of 0.321 ± 0.012 km3 of glacier mass from the reservoir zone to the receiving zone, leading to the formation of an ice-dammed lake at the glacier's terminus. The lake experienced five outbursts between 2015 and 2019, with the largest discharge occurring in 2017. And the maximum water depth during this period was 112 ± 11 m, resulting in a water storage volume of (158.37 ± 28.32) × 106 m3. On the other hand, our analysis of the relationship between glacier surface velocity and albedo, coupled with an examination of subglacial dynamics, revealed that increased precipitation during the active phase of the Kyagar Glacier results in accumulation of mass in the upper glacier. This accumulation induces changes in basal shear stress, triggering the glacier's transition into an unstable state. Consequently, glacier deformation rates escalate, surface crevasses proliferate, potentially providing conduits for surface meltwater to infiltrate the glacier bed. This, in turn, leaded to elevated basal water pressure, initiating glacier sliding. Furthermore, we postulated that the repetitive drainage of Kyagar Ice-dammed lake was primarily influenced by the opening and closing of subglacial drainage pathways and variations in inflow volumes. Future endeavors necessitate rigorous field observations to enhance glacier surge simulations, deepening our comprehension of glacier surge mechanisms and mitigating the impact of associated glacial hazards.

Multiscale calculations reveal new insights into the reaction mechanism between KRASG12C and α, ß-unsaturated carbonyl of covalent inhibitors.

Utilizing α,ß-unsaturated carbonyl group as Michael acceptors to react with thiols represents a successful strategy for developing KRASG12C inhibitors. Despite this, the precise reaction mechanism between KRASG12C and covalent inhibitors remains a subject of debate, primarily due to the absence of an appropriate residue capable of deprotonating the cysteine thiol as a base. To uncover this reaction mechanism, we first discussed the chemical reaction mechanism in solvent conditions via density functional theory (DFT) calculation. Based on this, we then proposed and validated the enzymatic reaction mechanism by employing quantum mechanics/molecular mechanics (QM/MM) calculation. Our QM/MM analysis suggests that, in biological conditions, proton transfer and nucleophilic addition may proceed through a concerted process to form an enolate intermediate, bypassing the need for a base catalyst. This proposed mechanism differs from previous findings. Following the formation of the enolate intermediate, solvent-assisted tautomerization results in the final product. Our calculations indicate that solvent-assisted tautomerization is the rate-limiting step in the catalytic cycle under biological conditions. On the basis of this reaction mechanism, the calculated kinact/ki for two inhibitors is consistent well with the experimental results. Our findings provide new insights into the reaction mechanism between the cysteine of KRASG12C and the covalent inhibitors and may provide valuable information for designing effective covalent inhibitors targeting KRASG12C and other similar targets.

Tanshinone IIA induces ER stress and JNK activation to inhibit tumor growth and enhance anti-PD-1 immunotherapy in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) remains at the forefront of new cancer cases, and there is an urgent need to find new treatments or improve the efficacy of existing therapies. In addition to the application in the field of cerebrovascular diseases, recent studies have revealed that tanshinone IIA (Tan IIA) has anticancer activity in a variety of cancers. PURPOSE: To investigate the potential anticancer mechanism of Tan IIA and its impact on immunotherapy in NSCLC. METHODS: Cytotoxicity and colony formation assays were used to detect the Tan IIA inhibitory effect on NSCLC cells. This research clarified the mechanisms of Tan IIA in anti-tumor and programmed death-ligand 1 (PD-L1) regulation by using flow cytometry, transient transfection, western blotting and immunohistochemistry (IHC) methods. Besides, IHC was also used to analyze the nuclear factor of activated T cells 1 (NFAT2) expression in NSCLC clinical samples. Two animal models including xenograft mouse model and Lewis lung cancer model were used for evaluating tumor suppressive efficacy of Tan IIA. We also tested the efficacy of Tan IIA combined with programmed cell death protein 1 (PD-1) inhibitors in Lewis lung cancer model. RESULTS: Tan IIA exhibited good NSCLC inhibitory effect which was accompanied by endoplasmic reticulum (ER) stress response and increasing Ca2+ levels. Moreover, Tan IIA could suppress the NFAT2/ Myc proto oncogene protein (c-Myc) signaling, and it also was able to control the Jun Proto-Oncogene(c-Jun)/PD-L1 axis in NSCLC cells through the c-Jun N-terminal kinase (JNK) pathway. High NFAT2 levels were potential factors for poor prognosis in NSCLC patients. Finally, animal experiments data showed a stronger immune activation phenotype, when we performed treatment of Tan IIA combined with PD-1 monoclonal antibody. CONCLUSION: The findings of our research suggested a novel mechanism for Tan IIA to inhibit NSCLC, which could exert anti-cancer effects through the JNK/NFAT2/c-Myc pathway. Furthermore, Tan IIA could regulate tumor PD-L1 levels and has the potential to improve the efficacy of PD-1 inhibitors.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization.

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.