ABSTRACT
The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.
Subject(s)
Proteins , Proteins/chemistry , Proteins/metabolism , Computational Biology/methods , Organelles/metabolism , Protein Conformation , Databases, Protein , Amino Acid SequenceABSTRACT
Active pharmaceutical ingredient (API) embedded dry powder for inhalation (AeDPI) shows higher drug loading and delivery dose for directly treating various lung infections. Inspired by the dandelion, we propose a novel kind of AeDPI microparticle structure fabricated by spray freeze drying technology, which would potentially enhance the alveoli deposition efficiency. When inhaling, such microparticles are expected to be easily broken-up into fragments containing API that acts as 'seed' and could be delivered to alveoli aided by the low density 'pappus' composed of excipient. Herein, itraconazole (ITZ), a first-line drug for treating pulmonary aspergillosis, was selected as model API. TPGS, an amphiphilic surfactant, was used to achieve stable primary ITZ nanocrystal (INc) suspensions for spray freeze drying. A series of microparticles were prepared, and the dandelion-like structure was successfully achieved. The effects of feed liquid compositions and freezing parameters on the microparticle size, morphology, surface energy, crystal properties and in vitro aerosol performance were systematically investigated. The optimal sample (SF(-50)D-INc7Leu3-2) in one-way experiment showed the highest fine particle fraction of â¼ 68.96â¯% and extra fine particle fraction of â¼ 36.87â¯%, equivalently â¼ 4.60â¯mg and â¼ 2.46â¯mg could reach the lung and alveoli, respectively, when inhaling 10â¯mg dry powders. The response surface methodology (RSM) analysis provided the optimized design space for fabricating microparticles with higher deep lung deposition performance. This study demonstrates the advantages of AeDPI microparticle with dandelion-like structure on promoting the delivery efficiency of high-dose drug to the deep lung.
ABSTRACT
Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.
Subject(s)
Drug Discovery , Quantum Theory , Drug Discovery/methods , Drug Design/methods , Prodrugs/chemistry , ThermodynamicsABSTRACT
Developing efficient photocatalysts for two-electron water splitting with simultaneous H2O2 and H2 generation shows great promise for practical application. Currently, the efficiency of two-electron water splitting is still restricted by the low utilization of photogenerated charges, especially holes, of which the transfer rate is much slower than that of electrons. Herein, Ru single atoms and RuOx clusters are co-decorated on ZnIn2S4 (RuOx/Ru-ZIS) to employ as multifunctional sites for efficient photocatalytic pure water splitting. Doping of Ru single atoms in the ZIS basal plane enhances holes abstraction from bulk ZIS by regulating the electronic structure, and RuOx clusters offer a strong interfacial electric field to remarkably promote the out-of-plane migration of holes from ZIS. Moreover, Ru single atoms and RuOx clusters also serve as active sites for boosting surface water oxidation. As a result, an excellent H2 and H2O2 evolution rates of 581.9 µmol g-1 h-1 and 464.4 µmol g-1 h-1 is achieved over RuOx/Ru-ZIS under visible light irradiation, respectively, with an apparent quantum efficiency (AQE) of 4.36% at 400 nm. This work paves a new way to increase charge utilization by manipulating photocatalyst using single atom and clusters.
ABSTRACT
BACKGROUND: Autoimmune enteropathy (AIE) is a rare disease whose diagnosis and long-term prognosis remain challenging, especially for adult AIE patients. AIM: To improve overall understanding of this disease's diagnosis and prognosis. METHODS: We retrospectively analyzed the clinical, endoscopic and histopathological characteristics and prognoses of 16 adult AIE patients in our tertiary medical center between 2011 and 2023, whose diagnosis was based on the 2007 diagnostic criteria. RESULTS: Diarrhea in AIE patients was characterized by secretory diarrhea. The common endoscopic manifestations were edema, villous blunting and mucosal hyperemia in the duodenum and ileum. Villous blunting (100%), deep crypt lymphocytic infiltration (67%), apoptotic bodies (50%), and mild intraepithelial lymphocytosis (69%) were observed in the duodenal biopsies. Moreover, there were other remarkable abnormalities, including reduced or absent goblet cells (duodenum 94%, ileum 62%), reduced or absent Paneth cells (duodenum 94%, ileum 69%) and neutrophil infiltration (duodenum 100%, ileum 69%). Our patients also fulfilled the 2018 diagnostic criteria but did not match the 2022 diagnostic criteria due to undetectable anti-enterocyte antibodies. All patients received glucocorticoid therapy as the initial medication, of which 14/16 patients achieved a clinical response in 5 (IQR: 3-20) days. Immunosuppressants were administered to 9 patients with indications of steroid dependence (6/9), steroid refractory status (2/9), or intensified maintenance medication (1/9). During the median of 20.5 months of follow-up, 2 patients died from multiple organ failure, and 1 was diagnosed with non-Hodgkin's lymphoma. The cumulative relapse-free survival rates were 62.5%, 55.6% and 37.0% at 6 months, 12 months and 48 months, respectively. CONCLUSION: Certain histopathological findings, including a decrease or disappearance of goblet and Paneth cells in intestinal biopsies, might be potential diagnostic criteria for adult AIE. The long-term prognosis is still unsatisfactory despite corticosteroid and immunosuppressant medications, which highlights the need for early diagnosis and novel medications.
Subject(s)
Glucocorticoids , Humans , Female , Male , Retrospective Studies , Adult , Middle Aged , Prognosis , Biopsy , Glucocorticoids/therapeutic use , Polyendocrinopathies, Autoimmune/diagnosis , Polyendocrinopathies, Autoimmune/immunology , Polyendocrinopathies, Autoimmune/pathology , Polyendocrinopathies, Autoimmune/drug therapy , Polyendocrinopathies, Autoimmune/therapy , Ileum/pathology , Ileum/immunology , Duodenum/pathology , Duodenum/immunology , Diarrhea/etiology , Diarrhea/diagnosis , Diarrhea/immunology , Intestinal Mucosa/pathology , Intestinal Mucosa/immunology , Immunosuppressive Agents/therapeutic use , Aged , Young Adult , Endoscopy, GastrointestinalABSTRACT
Gastric cancer (GC) is a malignant tumor with one of the lowest five-year survival rates. Traditional first-line treatment regimens, such as platinum drugs, have limited therapeutic efficacy in treating advanced GC and significant side effects, greatly reducing patient quality of life. In contrast, trastuzumab and other immune checkpoint inhibitors, such as nivolumab and pembrolizumab, have demonstrated consistent and reliable efficacy in treating GC. Here, we discuss the intrinsic characteristics of GC from a molecular perspective and provide a comprehensive review of classification and treatment advances in the disease. Finally, we suggest several strategies based on the intrinsic molecular characteristics of GC to aid in overcoming clinical challenges in the development of precision medicine and improve patient prognosis.
ABSTRACT
The variational quantum eigensolver (VQE) framework has been instrumental in advancing near-term quantum algorithms. However, parameter optimization remains a significant bottleneck for VQE, requiring a large number of measurements for successful algorithm execution. In this paper, we propose sequential optimization with approximate parabola (SOAP) as an efficient and robust optimizer specifically designed for parameter optimization of the unitary coupled-cluster ansatz on quantum computers. SOAP leverages sequential optimization and approximates the energy landscape as quadratic functions, minimizing the number of energy evaluations required to optimize each parameter. To capture parameter correlations, SOAP incorporates the average direction from previous iterations into the optimization direction set. Numerical benchmark studies on molecular systems demonstrate that SOAP achieves significantly faster convergence and greater robustness to noise compared with traditional optimization methods. Furthermore, numerical simulations of up to 20 qubits reveal that SOAP scales well with the number of parameters in the ansatz. The exceptional performance of SOAP is further validated through experiments on a superconducting quantum computer using a 2-qubit model system.
ABSTRACT
The NLRP3 inflammasome plays a pivotal role in various chronic inflammation-driven human diseases. However, no drugs specifically targeting NLRP3 inflammasome have been approved by the Food and Drug Administration (FDA) of the United States. In our current study, we showed that dimethyl fumarate (DMF) efficiently suppressed the activation of the NLRP3 inflammasome induced by multiple agonists and covalently modified Cys673 of NLRP3, thereby impeding the interaction between NLRP3 and NEK7. The inhibitory effect of DMF was nullified by anaplerosis of the Cys673 mutant (but not the wild-type) NLRP3 in Nlrp3-/- THP-1 cells. In vivo experiments, DMF demonstrated protective effects in the dextran sodium sulfate (DSS)-induced ulcerative colitis of WT mice, but not in Nlrp3-/- mice. In summary, our study identified DMF as a direct covalent inhibitor of NLRP3 and a potential candidate for the treatment of NLRP3 inflammasome-mediated diseases.
ABSTRACT
Skin wounds are susceptible to infection, leading to severe inflammatory reactions that can progress to chronic wounds, ultimately causing significant physical and mental distress to the patient. In this study, we propose an injectable composite hydrogel achieved through one-pot gelation of oxidized xyloglucan (OXG), cationic polyamide ε-poly-l-lysine (EPL), and surface amino-rich silicon nanoparticles (SiNPs). OXG exhibits commendable anti-inflammatory properties and provides crosslinking sites. SiNPs serve as mechanically reinforced crosslinkers, facilitating the construction of a dynamic Schiff base network. SiNPs significantly reduced the gelation time to 3 s and tripled the storage modulus of the hydrogels. Additionally, the combination of EPL and SiNPs demonstrated synergistic antimicrobial activity against both S. aureus and E. coli. Notably, the hydrogel effectively halted liver bleeding within 30 s. The hydrogel demonstrated outstanding shear-thinning and self-healing properties, crucial considerations for the design of injectable hydrogels. Furthermore, its efficacy was evaluated as a wound dressing in a mouse model with S. aureus infection. The results indicated that, compared to commercial products, the hydrogel exhibited a shorter wound healing time, decreased inflammation, thinner epithelium, increased hair follicles, enhanced neovascularization, and more substantial collagen deposition. These findings strongly suggest the promising potential of the proposed hydrogel as an effective wound dressing for the treatment of infected wounds.
Subject(s)
Anti-Bacterial Agents , Escherichia coli , Glucans , Hydrogels , Nanoparticles , Polylysine , Staphylococcus aureus , Wound Healing , Xylans , Glucans/chemistry , Glucans/pharmacology , Animals , Wound Healing/drug effects , Xylans/chemistry , Xylans/pharmacology , Hydrogels/chemistry , Hydrogels/pharmacology , Polylysine/chemistry , Polylysine/pharmacology , Mice , Nanoparticles/chemistry , Escherichia coli/drug effects , Staphylococcus aureus/drug effects , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/administration & dosage , Staphylococcal Infections/drug therapy , Cross-Linking Reagents/chemistry , Wound Infection/drug therapy , MaleABSTRACT
The treatment of infected wounds remains a challenging biomedical problem. Some bioactive small-molecule hydrogelators with unique rigid structures can self-assemble into supramolecular hydrogels for wound healing. However, they are still suffered from low structural stability and bio-functionality. Herein, a supramolecular hydrogel antibacterial dressing with a dual nanofibrillar network structure is proposed. A nanofibrillar network created by a small-molecule hydrogelator, puerarin extracted from the traditional Chinese medicine Pueraria, is interconnected with a secondary macromolecular silk fibroin nanofibrillar network induced by Ga ions via charge-induced supramolecular self-assembly. The resulting hydrogel features adequate mechanical strength for sustainable retention at wounds. Good biocompatibility and efficient bacterial inhibition are obtained when the Ga ion concentration is 0.05%. Otherwise, the substantial release of Ga ions and puerarin endows the hydrogel with excellent hemostatic and antioxidative properties. In vivo, evaluation of a mouse-infected wound model demonstrates that its healing effect outperformed that of a commercially available silver-containing wound dressing. The experimental group successfully achieves a 100% wound closure rate on day 10. This study sheds new light on the design of nanofibrillar hydrogels based on supramolecular self-assembly of naturally derived bioactive molecules as well as their clinical use for treating chronic infected wounds.
Subject(s)
Fibroins , Hydrogels , Isoflavones , Nanofibers , Wound Healing , Fibroins/chemistry , Animals , Isoflavones/chemistry , Isoflavones/pharmacology , Hydrogels/chemistry , Hydrogels/pharmacology , Mice , Wound Healing/drug effects , Nanofibers/chemistry , Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/pharmacology , Wound Infection/drug therapy , Wound Infection/microbiology , Bandages , Male , Staphylococcus aureus/drug effectsABSTRACT
BACKGROUND: Indoor bacterial communities may change with altitude because their major contributors, outdoor bacterial communities, vary with altitude. People's health effects from bacteria inhalation exposure can also vary with altitude because human respiratory physiology changes with oxygen content in air. Accordingly, adjusting indoor bacterial communities may help to acclimate newcomers from low-altitude environments to ultrahigh-altitude environments. To lay the groundwork for further research, we aimed to first elucidate the bacterial communities in ultrahigh-altitude residences and the effects of altitude on these communities. We collected 187 environmental samples from residential communities at ultrahigh altitudes of 3811-4651 m in Ngari, China and sequenced bacterial 16S rRNA genes. RESULTS: On one hand, when abundant genera in ultrahigh-altitude residences and those reported by previous studies on low-altitude residences were compared, nine genera were shared, whereas other five genera were abundant only at ultrahigh altitudes. On the other hand, when the bacterial communities of residences at different ultrahigh altitudes were further compared, the bacterial composition in indoor surface samples varied significantly with altitude. The relative abundance of five bacterial genera in indoor air samples and 10 genera and three phyla in indoor surface samples varied monotonically with altitude. CONCLUSIONS: Altitude may be a long-neglected factor that shapes residential bacterial communities and thus warrants attention.
Subject(s)
Air Pollution, Indoor , Altitude , Humans , Air Pollution, Indoor/analysis , RNA, Ribosomal, 16S/genetics , Environmental Monitoring , Bacteria/geneticsABSTRACT
INTRODUCTION: Early gastric cancer with current Helicobacter pylori infection (HpC-EGC) is common, but it is still unclear whether H. pylori eradication therapy (Hp-ET) or endoscopic submucosal dissection (ESD) should be performed first. We evaluated Hp-ETs short-term effects on horizontal boundary delineations of HpC-EGC in ESD. METHODS: Prospectively enrolled HpC-EGC patients were randomly assigned to eradication or control groups. Operation scopes of HpC-EGC lesions were delineated with marking dots at 5 mm out of the endoscopic demarcation line by an independent endoscopist, unaware of eradication status, before formal circumferential incision. As representatives, precise delineation rate, the shortest distance of all marking dots to the pathological demarcation line in all slices of one intact resected specimen (Dmin), and negative marking dot specimen rate were examined. RESULTS: Twenty-three HpC-EGC patients (25 lesions) were allocated to eradication group and 26 patients (27 lesions) were allocated to the control group with similar eradication success rates and all were differentiated type. With improving background mucosa inflammation after Hp-ET and similar gastritis-like epithelium rates, 10 lesions (40.0%) in the eradication group were of precise delineation compared to control group with 2 lesions (7.4%) (relative risk = 5.40, 95% CI 1.31-22.28). Dmin of eradication and control groups were 4.17 ± 2.52 mm and 2.67 ± 2.30 mm (p = 0.029), accompanied by 4 (14.8%) and none (0.0%) specimens that exhibited positive marking dots (p = 0.11), respectively. CONCLUSION: For HpC-EGC patients, administrating eradication medication before ESD is beneficial for the precise delineation of lesions and reducing the risk of positive horizontal resection margins.
Subject(s)
Endoscopic Mucosal Resection , Helicobacter Infections , Helicobacter pylori , Stomach Neoplasms , Humans , Helicobacter Infections/complications , Helicobacter Infections/drug therapy , Stomach Neoplasms/surgery , Stomach Neoplasms/pathology , Gastric Mucosa/surgery , Gastric Mucosa/pathologyABSTRACT
Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.
Subject(s)
Drug Discovery , Proteins , Ligands , Proteins/chemistry , Protein Binding , Affinity LabelsABSTRACT
Fructose 1, 6-diphosphate (FDP) is an endogenous intermediate in the glycolytic pathway, as well as an allosteric activator of phosphofructokinase (PFK). Based on the role in promoting glycolysis, FDP has been widely used as a therapeutic agent for mitigating the damage of endotoxemia and ischemia/reperfusion in clinical practice. However, the effect of exogenous FDP-induced glycolysis activation on insect carbohydrate metabolism and chitin synthesis remains largely unclear. Here, we investigated for the first time the effects of FDP-Na, an allosteric activator of PFK, on the growth and development of Hyphantria cunea larvae, a serious defoliator in agriculture and forestry, especially on glycolysis and chitin synthesis. The results showed that FDP-Na significantly restrained the growth and development of H. cunea larvae and resulted in larval lethality. After treatment with FDP-Na, hexokinase (HK), phosphofructokinase (PFK) and pyruvate kinase (PK) were significantly activated, and HcHK2, HcPFK, HcPK were dramatically upregulated, which suggested that FDP-Na enhanced glycolysis in H. cunea larvae. Meanwhile, FDP-Na also distinctly impacted chitin biosynthesis by disturbing transcriptions of genes in the chitin synthesis pathway, resulting in changes of chitin contents in the midgut and epidermis of H. cunea larvae. Therefore, we considered that FDP-Na caused the growth and development arrest, and impacted chitin biosynthesis, probably by disturbing in vivo glycolysis and carbohydrate metabolism in H. cunea larvae. The findings provide a new perspective on the mechanism by which glycolysis regulates insect growth and development, and lay the foundation for exploring the potential application of glycolysis activators in pest control as well.
Subject(s)
Diphosphates , Moths , Animals , Larva , Glycolysis , Phosphofructokinases , Chitin/pharmacologyABSTRACT
Natural polysaccharides are macromolecular substances with great potential owing to their wide biological activity and low toxicity. However, not all polysaccharides have significant pharmacodynamic activity; hence, appropriate chemical modification methods can be selected according to the unique structural characteristics of polysaccharides to assist in enhancing and promoting the presentation of their biological activities. This review summarizes research progress on modified polysaccharides, including common chemical modification methods, the change in biological activity following modification, and the factors affecting the biological activity of chemically modified polysaccharides. At the same time, the difficulties and challenges associated with the structural modification of natural polysaccharides are also outlined in this review. Thus, research on polysaccharide structure modification is critical for improving the development and utilization of sugar products.
Subject(s)
Polysaccharides , Polysaccharides/pharmacology , Structure-Activity RelationshipABSTRACT
Integration of photothermal materials and photocatalysts can effectively improve photocatalytic hydrogen production. However, the synergistic mechanism of photothermal effect and heterojunction still need to be deeply investigated. Herein, Co3O4@ZnIn2S4 (ZIS) core-shell heterojunction was constructed as a photothermal/ photocatalytic integrated system for photocatalytic hydrogen production. The photothermal effect induced by Co3O4 boosts the surface reaction kinetic of hydrogen evolution with an apparent activation energy decrease from 42.0 kJâ mol-1 to 33.5 kJâ mol-1. The photothermal effect also increases the charge concentrations of Co3O4@ZIS, which ameliorates the conductivity of Co3O4@ZIS and thus benefits to charge transfer. In addition, a p-n junction forms between Co3O4 and ZIS and provides a built-in electric field to enhance charge separate and prolong charge life time. Benefiting from the synergy of photothermal effect and heterojunction, the photocatalytic performance of Co3O4@ZIS is significantly improved with a highest hydrogen evolution rate of 4515 µmolâ g-1â h-1, which is about 3.5 times higher than that of pure ZIS. This work offers a full perspective to understand the photothermal/photocatalytic integrated conception for solar hydrogen production.
ABSTRACT
Acid-base dissociation constant (pKa) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pKa prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pKa (multi-fidelity modeling with subgraph pooling for pKa prediction), a novel pKa prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pKa prediction. To overcome the scarcity of accurate pKa data, low-fidelity data (computational pKa) was used to fit the high-fidelity data (experimental pKa) through transfer learning. The final MF-SuP-pKa model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pKa achieves superior performances to the state-of-the-art pKa prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pKa achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.
ABSTRACT
Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.
ABSTRACT
BACKGROUND: N6-methyladenosine (m6A) is the most frequent internal modification in eukaryotic RNA. Long noncoding RNAs (lncRNAs) are a new type of noncoding regulatory molecule with multiple cellular functions. Both are closely related to the occurrence and development of liver fibrosis (LF). However, the role of m6A-methylated lncRNAs in the progression of LF remains largely unknown. METHODS: In this study, HE and Masson staining were used to observe pathological changes in the liver, m6A-modified RNA immunoprecipitation sequencing (m6A-seq) was performed to systematically evaluate the m6A modification level of lncRNAs in LF mice, meRIP-qPCR and RT-qPCR were used to detect the m6A methylation level and RNA expression level of the target lncRNAs. RESULTS: A total of 415 m6A peaks were detected in 313 lncRNAs in liver fibrosis tissues. There were 98 significantly different m6A peaks in LF, which were located on 84 lncRNAs, of which 45.2% of the lncRNA length was between 200-400 bp. At the same time, the first three chromosomes of these methylated lncRNAs were chromosomes 7, 5 and 1. RNA sequencing identified 154 differentially expressed lncRNAs in LF. The joint analysis of m6A-seq and RNA-seq found that there were three lncRNAs with significant changes in m6A methylation and RNA expression levels: lncRNA H19, lncRNA Gm16023 and lncRNA Gm17586. Subsequently, the verification results showed that the m6A methylation levels of lncRNA H19 and lncRNA Gm17586 were significantly increased, while that of lncRNA Gm16023 was significantly decreased, and the RNA expression of three lncRNAs was significantly decreased. Through the establishment of a lncRNA-miRNA-mRNA regulatory network, the possible regulatory relationships of lncRNA H19, lncRNA Gm16023 and lncRNA Gm17586 in LF were revealed. CONCLUSION: This study revealed the unique m6A methylation pattern of lncRNAs in LF mice, suggesting that the m6A methylation modification of lncRNAs is related to the occurrence and development of LF.
Subject(s)
RNA, Long Noncoding , Animals , Mice , RNA, Long Noncoding/genetics , Liver Cirrhosis/genetics , High-Throughput Nucleotide Sequencing , BiomarkersABSTRACT
TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high-performance in the simulation of unitary coupled-cluster circuits, using compact representations of quantum states and excitation operators. Additionally, TenCirChem supports noisy circuit simulation and provides algorithms for variational quantum dynamics. TenCirChem's capabilities are demonstrated through various examples, such as the calculation of the potential energy curve of H2O with a 6-31G(d) basis set using a 34-qubit quantum circuit, the examination of the impact of quantum gate errors on the variational energy of the H2 molecule, and the exploration of the Marcus inverted region for charge transfer rate based on variational quantum dynamics. Furthermore, TenCirChem is capable of running real quantum hardware experiments, making it a versatile tool for both simulation and experimentation in the field of quantum computational chemistry.