Explore the vast range of titles available.

Abrogating tumor angiogenesis by inhibiting vascular endothelial growth factor receptor‐2 (VEGFR2) has been established as a therapeutic strategy for treating cancer. However, because of their low selectivity, most small molecule inhibitors of VEGFR2 tyrosine kinase show unexpected adverse effects and limited anticancer efficacy. In the present study, we detailed the pharmacological properties of anlotinib, a highly potent and selective VEGFR2 inhibitor, in preclinical models. Anlotinib occupied the ATP‐binding pocket of VEGFR2 tyrosine kinase and showed high selectivity and inhibitory potency (IC50 <1 nmol/L) for VEGFR2 relative to other tyrosine kinases. Concordant with this activity, anlotinib inhibited VEGF‐induced signaling and cell proliferation in HUVEC with picomolar IC50 values. However, micromolar concentrations of anlotinib were required to inhibit tumor cell proliferation directly in vitro. Anlotinib significantly inhibited HUVEC migration and tube formation; it also inhibited microvessel growth from explants of rat aorta in vitro and decreased vascular density in tumor tissue in vivo. Compared with the well‐known tyrosine kinase inhibitor sunitinib, once‐daily oral dose of anlotinib showed broader and stronger in vivo antitumor efficacy and, in some models, caused tumor regression in nude mice. Collectively, these results indicate that anlotinib is a well‐tolerated, orally active VEGFR2 inhibitor that targets angiogenesis in tumor growth, and support ongoing clinical evaluation of anlotinib for a variety of malignancies. Anlotinib showed potent (picomolar) inhibitory activity and selectivity toward VEGFR2 (~75–10 000‐fold selectivity vs other tyrosine kinases). Compared with other known VEGFR2 inhibitors, anlotinib showed superior anti‐angiogenesis and broad‐spectrum in vivo antitumor efficacy.

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users’ data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data. Peak table processing is essential for metabolomics, but finding the best workflow is challenging. This protocol describes NOREVA, an out-of-the-box software tool that can process and evaluate thousands of workflows in a single experiment.

PROteolysis TArgeting Chimeras (PROTACs) has been exploited to degrade putative protein targets. However, the antitumor performance of PROTACs is impaired by their insufficient tumour distribution. Herein, we present de novo designed polymeric PROTAC (POLY-PROTAC) nanotherapeutics for tumour-specific protein degradation. The POLY-PROTACs are engineered by covalently grafting small molecular PROTACs onto the backbone of an amphiphilic diblock copolymer via the disulfide bonds. The POLY-PROTACs self-assemble into micellar nanoparticles and sequentially respond to extracellular matrix metalloproteinase-2, intracellular acidic and reductive tumour microenvironment. The POLY-PROTAC NPs are further functionalized with azide groups for bioorthogonal click reaction-amplified PROTAC delivery to the tumour tissue. For proof-of-concept, we demonstrate that tumour-specific BRD4 degradation with the bioorthogonal POLY-PROTAC nanoplatform combine with photodynamic therapy efficiently regress tumour xenografts in a mouse model of MDA-MB-231 breast cancer. This study suggests the potential of the POLY-PROTACs for precise protein degradation and PROTAC-based cancer therapy. Proteolysis targeting chimeras (PROTACs) have emerged as promising cancer therapy agents but have suffered from systemic toxicity issues. Here, the authors report on the creation of polymeric PROTAC nanoparticles for tumour targeting delivery and demonstrate protein degradation in vivo, in combination with photodynamic therapy.

Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available. Conventional structure-based drug design pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, the authors report a sequence-to-drug concept that discovers drug-like small molecule modulators directly from protein sequences.

Amphotericin B (AmB) causes toxicity to the erythrocyte membrane, leading to hemolysis, which limits the clinically effective dose for AmB intravenous therapy in invasive fungal infections. The molecular mechanism by which AmB adheres to the membrane of erythrocytes is the key factor in causing AmB to be toxic to the membrane of erythrocytes, but it is not yet fully understood; the mechanism by which AmB adheres to the liquid microdomains with higher fluidity formed by cholesterol and unsaturated phospholipids remains especially unclear. This study examined the adsorption of AmB at different concentrations, 5, 45, 85, and 125 μg/mL, on unsaturated phospholipid membranes containing 50 mol% cholesterol. The thermodynamic properties and structure of DOPC monolayers and DOPC/cholesterol mixed monolayers at different concentrations of AmB have been investigated using the Langmuir monolayer model and the BAM method. The impact of varying concentrations of AmB on the hydrophilic and hydrophobic domains of the DOPC bilayers and the DOPC/cholesterol mixed bilayers have also been discussed using large unilamellar vesicle liposomes and fluorescence techniques. It is shown that for AmB concentrations greater than 5 μg/mL, with an increase in AmB’s concentration, the reorganization time for the DOPC/cholesterol monolayer increases, and the elastic modulus of the DOPC/cholesterol mixed monolayer decreases. In particular, when AmB’s concentration is higher than 85 μg/mL, the liquid-condensed phase domains on the DOPC/cholesterol monolayer reduce significantly and the liquid-expanded phase domain enlarges from the BAM images. When the AmB concentration reaches 5 μg/mL, the disorder of the hydrophobic and hydrophilic domains of the DOPC/cholesterol bilayer increases as the AmB concentration increases. The way in which AmB interacts with the DOPC/cholesterol mixed membrane is related to the concentration of AmB. The higher the concentration of AmB, the more likely it is to remove cholesterol from the unsaturated phospholipid membrane. The results are helpful to understand the mechanism of AmB’s toxicity to the erythrocyte’s membrane, which has a guiding value for seeking ways to reduce the AmB’s toxicity.

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen’s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine. While chemical-induced transcriptional profiles reveal drug mechanisms, inherent noise limits their utility. Here, authors present TranSiGen, a deep representation learning model that denoises and reconstructs these profiles, demonstrating its efficacy in downstream drug discovery tasks.

Metabolic reprogramming plays a pivotal role in the development and progression of tumors. Tumor cells rely on glycolysis as their primary energy production pathway and effectively utilize biomolecules generated by the pentose phosphate pathway (PPP) for efficient biosynthesis. However, the role of 6-phosphogluconate dehydrogenase (6PGD), a crucial enzyme in the PPP, remains unexplored in esophageal squamous cell carcinoma (ESCC). In this study, we observed a significant upregulation of 6PGD expression in ESCC tissues, which correlated with an unfavorable prognosis among patients. The experiments demonstrated that knockdown of 6PGD induces oxidative stress and suppresses ESCC cell proliferation. Mechanistically, this is achieved through AMPK activation and subsequent inhibition of downstream mTOR phosphorylation. Moreover, physcion has been found to inhibit 6PGD activity and exert its anti-ESCC effect via the AMPK/mTOR pathway. Subsequently, we conducted both in vitro and in vivo experiments to validate the anticancer efficacy of combining metformin, an AMPK activator, with physcion. The results demonstrated a significantly enhanced inhibition of ESCC growth. This study elucidates the impact of 6PGD on ESCC cell proliferation along with its underlying molecular mechanisms, highlighting its potential as a therapeutic target for ESCC. Furthermore, we investigated a novel approach for improved anti-tumor therapy involving physcion and metformin. These findings will contribute new insights to clinical treatment strategies for ESCC while providing a theoretical foundation for developing molecular targeted therapies.

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5’-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins. Carbohydrates are essential for regulating various biological processes. Here, the authors developed DeepGlycanSite, a deep learning model that accurately predicts carbohydrate-binding sites on proteins, offering insights into carbohydrate regulation of therapeutically important proteins.

The low immunogenicity, insufficient infiltration of T lymphocytes, and dismal response to immune checkpoint blockade therapy pose major difficulties in immunotherapy of pancreatic cancer. Photoimmunotherapy by photodynamic therapy (PDT) can induce an antitumor immune response by triggering immunogenic cell death in the tumor cells. Notwithstanding, PDT‐driven oxygen consumption and microvascular damage can further aggravate hypoxia to exaggerates glycolysis, leading to lactate accumulation and immunosuppressive tumor microenvironment. Herein, a supramolecular prodrug nanoplatform codelivering a photosensitizer and a prodrug of bromodomain‐containing protein 4 inhibitor (BRD4i) JQ1 for combinatory photoimmunotherapy of pancreatic cancer are demonstrated. The nanoparticles are fabricated by host–guest complexation between cyclodextrin‐grafted hyaluronic acid (HA‐CD) and adamantine‐conjugated heterodimers of pyropheophorbide a (PPa) and JQ1, respectively. HA can achieve active tumor targeting by recognizing highly expressed CD44 on the surface of pancreatic tumors. PPa‐mediated PDT can enhance the immunogenicity of the tumor cells and promote intratumoral infiltration of the cytotoxic T lymphocytes. Meanwhile, JQ1 combats PDT‐mediated immune evasion through inhibiting expression of c‐Myc and PD‐L1, which are key regulators of tumor glycolysis and immune evasion. Collectively, this study presents a novel strategy to enhance photoimmunotherapy of the pancreatic cancer by provoking T cells activation and overcoming adaptive immune resistance. Supramolecular prodrug nanosystem composed of β‐cyclodextrin‐grafted hyaluronic acid and reduction‐activatable adamantine‐JQ1/adamantine‐Pheophorbide A conjugates is reported. The prodrug nanoparticle induces immunogenic cell death in the pancreatic tumor cells under near‐infrared laser irradiation, and consequently promoted intratumoral infiltration of the cytotoxic T lymphocytes. Meanwhile, JQ1 relieves glycolysis‐mediated immune evasion and photodynamic therapy‐triggered PD‐L1 overexpression for durable regression of the pancreatic tumor.

Lamins are the ancient type V intermediate filament proteins contributing to diverse biological functions, such as the maintenance of nuclear morphology, stabilization of chromatin architecture, regulation of cell cycle progression, regulation of spatial-temporal gene expressions, and transduction of mechano-signaling. Deregulation of lamins is associated with abnormal nuclear morphology and chromatin disorganization, leading to a variety of diseases such as laminopathy and premature aging, and might also play a role in cancer. Accumulating evidence indicates that lamins are functionally regulated by post-translational modifications (PTMs) including farnesylation, phosphorylation, acetylation, SUMOylation, methylation, ubiquitination, and O-GlcNAcylation that affect protein stabilization and the association with chromatin or associated proteins. The mechanisms by which these PTMs are modified and the relevant functionality become increasingly appreciated as understanding of these changes provides new insights into the molecular mechanisms underlying the laminopathies concerned and novel strategies for the management. In this review, we discussed a range of lamin PTMs and their roles in both physiological and pathological processes, as well as potential therapeutic strategies by targeting lamin PTMs.