Molecular Characterization Methods Research Articles

Deciphering Fire Blight: From Erwinia amylovora Ecology to Genomics and Sustainable Control

Fire blight is a highly destructive plant disease that affects the pome fruit value chain, with high economic impacts. Its etiological agent is the Gram-negative bacterium Erwinia amylovora. The origin of fire blight goes back to the late 1700s in North America, and the disease since then has spread to New Zealand, Europe, North Africa, the Middle East, and Asia. Due to its worldwide dissemination, advances have been made to identify and characterize E. amylovora strains from different regions and understand their evolutionary adaptation. Additionally, many efforts have been made in recent decades to stop the occurrence and impacts of fire blight, but in many countries, only preventive measures have been applied, as the application of antibiotics and copper-based compounds has become more restricted. Thus, new sustainable methods to control the pathogen are constantly required. This article presents a comprehensive review of the pathogen, from the phenotypic and molecular characterization methods applied to advances in comparative genomics and the development of new compounds for sustainable control of E. amylovora.

Online LC-Orbitrap MS method for the rapid molecular characterization of dissolved organic matter.

High-resolution mass spectrometry (HRMS) combined with electrospray ionization (ESI) has been the most useful technique for molecular characterization of dissolved organic matter (DOM) derived from diverse sources. However, the comprehensive detection of DOM composition was hindered by ionization suppression observed in ESI sources. HRMS coupled with liquid chromatography (LC) is a potential tool for comprehensive molecular characterization of DOM. The Suwannee River fulvic acids (SRFAs) and two DOM samples from seawater and refinery wastewater extracted by solid phase extraction (SPE) were analyzed by LC-Orbitrap MS coupled with ESI. The mobile phases, solvent composition, and gradient in the LC-Orbitrap MS analysis were optimized. The number of detected molecular formulae of SRFAs by online LC-Orbitrap MS was significantly increased by approximately 40% compared to direct injection. These additional detected compounds are mainly protein and lignin-like compounds, with a low O/C ratio and high H/C ratio. For the SRFAs, the relative standard deviations (%) of reproducibility are 5.51, 2.33, 7.97, and 1.80 for average O/C, H/C, double bond equivalent, and modified aromaticity index, respectively. This study proposed a simple and rapid method based on LC-Orbitrap MS for an in-depth analysis of the molecular composition of DOM, achieving a remarkable analysis time of only 5 min per sample. The rapid method provides a dependable and efficient approach for the molecular characterization of DOM, thereby advancing our comprehension and investigation of DOM across diverse ecosystems.

Near-infrared microscopy reveals diabetic nephropathy in ob/ob mice

Diabetic nephropathy (DN) is a major cause of global kidney failure. While histological kidney biopsy is the gold standard for diagnosis, it primarily reveals tissue morphology. In contrast, near-infrared (NIR) microscopy offers a label-free method for detailed molecular characterization of kidney tissue. Hematoxylin and eosin-stained kidney tissue samples from 17 ob/ob mice with DN and 14 healthy mice were examined using Fourier transform-NIR microscopy. Four different spectra were obtained from both the mesangium and tubulus. NIR spectral analysis unveiled distinct differences in wavenumbers between DN-affected and healthy kidneys, notably in the carbohydrate and protein-associated region (5500-4200 cm−1). In the mesangium, DN tissue samples exhibited higher median values at 4235 cm−1, 4659 cm−1, 4844 cm−1, 4906 cm−1, and 5222 cm−1 compared to controls (P < 0.05, P < 0.01, P < 0.05, P < 0.05 and P < 0.001, respectively). In tubular spectra, higher median values were found at 4258 cm−1, 4659 cm−1, 5222 cm−1, and 5346 cm−1 in the DN group (P < 0.01, P < 0.05, P < 0.05 and P < 0.01, respectively). These spectral differences strongly correlated with metabolic, histologic, and urinary parameters, providing valuable DN progression insights. The classification model achieved a visible clustering between the control and DN group for both the mesangial and tubular measurements. NIR microscopy demonstrated significant spectral differences between DN and healthy kidney tissues in mice, hinting at its potential for providing chemical insights, aiding in more accurate diagnoses, and offering a foundation for further clinical exploration and potential therapeutic advancements in DN.

Inflammation perturbs hematopoiesis by remodeling specific compartments of the bone marrow niche.

Hematopoietic stem and progenitor cells (HSPC) are regulated by interactions with stromal cells in the bone marrow (BM) cavity, which can be segregated into two spatially defined central marrow (CM) and endosteal (Endo) compartments. However, the importance of this spatial compartmentalization for BM responses to inflammation and neoplasia remains largely unknown. Here, we extensively validate a combination of scRNA-seq profiling and matching flow cytometry isolation that reproducibly identifies 7 key CM and Endo populations across mouse strains and accurately surveys both niche locations. We demonstrate that different perturbations exert specific effects on different compartments, with type I interferon responses causing CM mesenchymal stromal cells to adopt an inflammatory phenotype associated with overproduction of chemokines modulating local monocyte dynamics in the surrounding microenvironment. Our results provide a comprehensive method for molecular and functional stromal characterization and highlight the importance of altered stomal cell activity in regulating hematopoietic responses to inflammatory challenges.

In Silico Prediction of ERRα Agonists Based on Combined Features and Stacking Ensemble Method.

Estrogen-related receptor α (ERRα) is considered a very promising target for treating metabolic diseases such as type 2 diabetes. Development of a prediction model to quickly identify potential ERRα agonists can significantly reduce the time spent on virtual screening. In this study, 298 ERRα agonists and numerous nonagonists were collected from various sources to build a new dataset of ERRα agonists. Then a total of 90 models were built using a combination of different algorithms, molecular characterization methods, and data sampling techniques. The consensus model with optimal performance was also validated on the test set (AUC=0.876, BA=0.816) and external validation set (AUC=0.867, BA=0.777) based on five selected baseline models. Furthermore, the model's applicability domain and privileged substructures were examined, and the feature importance was analyzed using the SHAP method to help interpret the model. Based on the above, it's hoped that our publicly accessible data, models, codes, and analytical techniques will prove valuable in quick screening and rational designing more novel and potent ERRα agonists.

Diagnosis and Molecular Characterization of Dermatophytosis: An Observational Study

Dermatophytes are the keratinophilic fungi which infect humans and is the most recurring type of disease. The high level of transmissibility creates an epidemiological risk and emphasises the significance of these illnesses. However, a growing number of reports describing dermatophytes can cause deep infections in diabetic and immunocompromised patients, by invading deep layers like the dermis and hypodermis. Despite the prevalence and significance of dermatophytes in clinical mycology, it is not always possible to accurately diagnose this specific infection due to its overlapping structures among species of dermatophytes. Since it is difficult to identify species that exhibit weak characteristics in the morphological highlights, identification of the dermatophyte is often relied on its morphological analysis, which is a laborious process and demands skill. The massive shift in genetic variation, the source of infection, and epidemiological research can be discovered using molecular approaches. Therefore, the development of an accurate laboratory test for dermatophyte species identification is essential for the prevention and efficient management of dermatophytoses. One such methodology allows use of PCR technology which has many methods for molecular level characterization which is rapid, efficient, and capable of producing DNA polymorphisms specific to various dermatophyte species based on distinctive band patterns seen by agarose gel electrophoresis. The RAPD-PCR approach will be used in this study protocol to molecularly characterize the dermatophytes for precise speciation of the sample. In addition to improving knowledge of fungal biology and pathology with a focus on adaptive mechanisms to combat difficult conditions from host counteractions, there is a need to improve awareness of the importance of these diseases through accurate epidemiological data. The advantages of molecular approaches for characterizing objects over traditional methods are their sensitivity and specificity.

Structural elucidation of complex polyesters polyols from bio-lubricant using off-line liquid chromatography x supercritical fluid chromatography coupled with Orbitrap mass spectrometry

Synthetic complex esters and polyol esters are incorporated as partially bio-based and biodegradable alternatives to petroleum base oils in lubricant formulations, to provide specific properties or performance and to help reducing their carbon footprint in certain cases. A sample can contain over 400 molecules of high chemical similarity including numerous isomers. To resolve such complexity, a separation technique with large peak capacity coupled to high-resolution mass spectrometry (HRMS) is essential. In this study, comprehensive off-line LCxSFC hyphenated with an Orbitrap analyzer was used for the structural elucidation of a synthetic bio-lubricant composed of a polyol reacted with fatty acids of varying length or with repetitive units of polyesters of ricinoleic acid. Retention in the LC first dimension was mostly due to the degree of oligomerization of ricinoleic acid within the polyester and to the chain length of the fatty acid. The SFC second dimension highlighted the esterification degree of the polyalcohol and the number and positions of fatty acids double bonds. The combination of both dimensions permitted the separation of isomers. The coupling of SFC with Orbitrap analyzer allowed an accurate assignment of molecular formulas. Finally, the fragmentation in the ionization source confirmed the attributed structures. By introducing a clear distribution of the chemical structures in the retention space, LCxSFC-HRMS provided a powerful analytical method for the comprehensive molecular characterization of the complex polyester polyols sample.

Rare SMARCA4-deficient thoracic tumor: Insights into molecular characterization and optimal therapeutics methods

IntroductionsThe 2021 WHO Classification of Thoracic Tumors recognized SMARCA4-deficient undifferentiated thoracic tumors (SMARCA4-dUT) as a distinct entity that shows a striking overlap in demographic and molecular profiles with SMARCA4-deficient non-small lung cancer (SMARCA4-dNSCLC). The implications of SMARCA4 deficiency based on immunohistochemistry remain unclear. We aimed to investigate molecular characteristics of SMARCA4-deficient thoracic tumors (SDTT) and explore optimal therapeutics. MethodsFrom June.15, 2018, to Nov.15, 2023, a large cohort including patients diagnosed with SMARCA4-deficient (N = 196) and SMARCA4-intact (N = 438) thoracic tumors confirmed by immunohistochemistry at SYSUCC were screened. Clinicopathologic and molecular characteristics were identified and compared. External SRRSH cohort (N = 34) was combined into a pooled cohort to compare clinical outcome of first-line therapy efficacy. ResultsSDTT is male predominance with smoking history, high tumor burden, and adrenal metastases. The relationship between SMARCA4 mutation and protein expression is not completely parallel. The majority of SMARCA4-deficient patients harbor truncating (Class-I) SMARCA4 mutations, whereas class-II alterations and wild-type also exist. Compared with SMARCA4-intact thoracic tumors, patients with SDTT displayed a higher tumor mutation burden (TMB) and associated with a shorter median OS (16.8 months vs. Not reached; P < 0.001). Notably, SMARCA4 protein deficiency, rather than genetic mutations, played a decisive role in these differences. SDTT is generally resistant to chemotherapy, while sensitive to chemoimmunotherapy (median PFS: 7.5 vs. 3.5 months, P < 0.001). In particular, patients with SMARCA4 deficient thoracic tumors treated with paclitaxel-based chemoimmunotherapy achieved a longer median PFS than those with pemetrexed-based chemoimmunotherapy (10.0 vs. 7.3 months, P = 0.028). ConclusionsSMARCA4 protein deficiency, rather than genetic mutations, played a decisive role in its characteristics of higher TMB and poor prognosis. Chemoimmunotherapy serves as the optimal option in the current treatment regimen. Paclitaxel-based chemoimmunotherapy performed better than those with pemetrexed-based chemoimmunotherapy.

Study of molecular characterization for diagnosis of chronic and recurrent dermatophytosis

Dermatophytes are the keratinophilic fungi which infect humans and is the most recurring type of disease. The high level of transmissibility creates an epidemiological risk and emphasises the significance of these illnesses. However, a growing number of reports describing dermatophytes can cause deep infections in diabetic and immunocompromised patients, by invading deep layers like the dermis and hypodermis. Despite the prevalence and significance of dermatophytes in clinical mycology, it is not always possible to accurately diagnose this specific infection due to its overlapping structures among species of dermatophytes. Since it is difficult to identify species that exhibit weak characteristics in the morphological highlights, identification of the dermatophyte is often relied on its morphological analysis, which is a laborious process and demands skill. The massive shift in genetic variation, the source of infection, and epidemiological research can be discovered using molecular approaches. Therefore, the development of an accurate laboratory test for dermatophyte species identification is essential for the prevention and efficient management of dermatophytoses. One such methodology allows use of PCR technology which has many methods for molecular level characterization which is rapid, efficient, and capable of producing DNA polymorphisms specific to various dermatophyte species based on distinctive band patterns seen by agarose gel electrophoresis. The RAPD-PCR approach will be used in this study protocol to molecularly characterize the dermatophytes for precise speciation of the sample. In addition to improving knowledge of fungal biology and pathology with a focus on adaptive mechanisms to combat difficult conditions from host counteractions, there is a need to improve awareness of the importance of these diseases through accurate epidemiological data. The advantages of molecular approaches for characterizing objects over traditional methods are their sensitivity and specificity.

The Greater Celandine: Identification and Characterization of an Antimicrobial Peptide from Chelidonium majus.

Chelidonium majus, known as Greater Celandine, is a latex-bearing plant that has been leveraged for its anticancer and antimicrobial properties. Herein, C. majus aerial tissue is mined for the presence of antimicrobial peptides. A highly abundant cysteine-rich peptide with a length of 25 amino acids, deemed CM-AMP1, is characterized through multiple mass spectrometric approaches. Electron-activated dissociation is leveraged to differentiate between isoleucine and leucine residues and complement conventional collision-induced dissociation to gain full sequence coverage of the full-length peptide. CM-AMP1 shares little sequence similarity with any proteins in publicly available databases, highlighting the novelty of its cysteine landscape and core motif. The presence of three disulfide bonds in the native peptide confers proteolytic stability, and antimicrobial activity is greatly decreased upon the alkylation of the cysteine residues. Synthetic variants of CM-AMP1 are used to confirm the activity of the full-length sequence and the core motif. To assess the biological impact, E. coli was grown in a sublethal concentration of CM-AMP1 and quantitative proteomics was used to identify proteins produced by the bacteria under stress, ultimately suggesting a membrane lytic antimicrobial mechanism of action. This study integrates multiple analytical methods for molecular and biological characterization of a unique antimicrobial peptide identified from C. majus.

Methods for molecular characterization of dissolved organic matter in the alpine water environment: an overview

The alpine area has become a sensitive indicator and amplifier of global climate change and human activities because of its unique geographical and climatic conditions. Being an essential part of biochemical cycling, dissolved organic matter (DOM) could effectively help understand the process, structure, and function of alpine aquatic ecosystems. Due to the low content and sampling difficulties, the analysis of DOM in alpine water demands high sensitivity with low sample volume, which has not been comprehensively reviewed. This review summarizes the DOM sampling, pretreatment, and analysis methods involving the characterization of concentration, spectroscopy, and molecular structure. Overall, conventional parameters are the basis of advanced characterization methods. Spectroscopic tests can reveal the optical properties of DOM in response to lights from ultraviolet to infrared wavelengths, to distinguish the chemical composition. Molecular structure characterizations can provide microscopic information such as functional groups, element ratios, and molecular weights. The combination of multiple methods can depict DOM composition from multiple perspectives. In sum, optimized sampling and pretreatment, high-sensitivity molecular characterization, and method integration are crucial for effectively analyzing DOM components in alpine waters. These perspectives help to standardize the DOM characterization process and to understand the correlation between DOM composition and its properties, as well as the migration and transformation of DOM.

Exploring Molecular Methods for Candida species Identification and Characterization

Candida species (CS) are significant fungal pathogens responsible for a wide range of infections in humans, making their precise and timely identification crucial for effective treatment and disease management. Traditional identification methods, such as phenotypic assays and culture-based techniques, often suffer from limitations, including prolonged processing times, limited accuracy, and inadequate differentiation between species. As a result, molecular techniques have gained prominence for their ability to rapidly and accurately identify and characterize various Candida species. This review discusses the shortcomings of conventional methods and highlights the array of molecular strategies that have been developed, such as polymerase chain reaction (PCR) assays, DNA sequencing, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). These techniques leverage the unique genetic and proteomic profiles of Candida species to provide highly sensitive and specific identification, even distinguishing between closely related strains. Additionally, the review explores the use of molecular approaches in clinical diagnostics, epidemiological studies, and antifungal resistance monitoring, where they enable the rapid detection of Candida species from clinical samples, allowing for swift diagnosis and timely antifungal intervention. Furthermore, these techniques are instrumental in identifying clonal outbreaks and tracking transmission dynamics in healthcare settings. The review emphasizes the advantages of molecular methods—such as their precision, speed, and capacity to uncover cryptic species—while also calling for continued research to improve their efficacy and broader application in Candida species identification.

Diagnostic Guide for Sclerotial Blight and Circular Spot of Sweetpotato

Agroathelia rolfsii (anamorph: Sclerotium rolfsii) is a soilborne fungal pathogen that can cause disease on over 500 documented host species, including economically important field and vegetable crops. The pathogen commonly infects the stem or crown of most hosts, but it is also capable of damaging fruit and root structures that are near the soil line, resulting in wilting, stunting, and plant death. Two diseases caused by this pathogen are sclerotial blight and circular spot, both of which are detrimental for sweetpotato production. A. rolfsii is a necrotrophic pathogen and can be cultured from susceptible hosts and on artificial media. The purpose of this diagnostic guide is to provide characteristic traits for identifying A. rolfsii in sweetpotato as well as outline methods for pathogen isolation, morphological and molecular characterization, culture maintenance and long-term storage, and pathogenicity testing.

Pd nanoparticles lubricant additive catalyze the construction of carbon-based tribofilm to reduce graphitization-induced wear of DLC films under boundary lubrication

To explore approaches of reducing graphitization-induced wear on the friction surface of DLC films under boundary lubrication to increase their service life, Pd nanoparticles (PdNPs) prepared by chemical reduction method were used as lubricant additive of DLC films and GCr15 ball under boundary lubrication. Tribotests, reactive force field molecular dynamics (ReaxFF MD) and surface characterization methods, such as SEM, XPS, Raman and FIB-TEM, were used to study the effects of PdNPs as lubricant additive on the graphitization-induced wear of DLC films and the effect mechanisms. The results show that, PdNPs lubricant additive can reduce the friction coefficient (COF) of the tribosystems, and more importantly, they can obviously increase the lifespan of DLC films. Characterization results indicate that PdNPs can promote the tribochemical reaction on the friction surface and catalyze the formation of carbon-based tribofilm on the friction surface, which significantly reduces the graphitization-induced wear of DLC films. ReaxFF MD studies show that PdNPs effectively catalyze the massive dehydrogenation and chain-breaking of base oil hydrocarbon molecules, providing the basis for the formation of carbon-based tribofilm. These results indicate that PdNPs are promising as effective additive to prolong the service life of DLC films under boundary lubrication.

Isolation and Characterization of Plant Growth Promoting Rhizobacteria form Raphanus sativus (Radish)

Rhizobacteria are present in rhizosphere region of plant root system, which enhance the plant growth by various way like biological nitrogen fixation, siderophore production, phosphate solubilization and phytohormone production. The soil samples were collected from rhizosphere region of Raphanus sativus, after enrichment rhizobacteria was isolated by serial dilution method, diluted sample were spread on respective solid agar media plates. Isolated rhizobacteria was identify by biochemical and molecular characterization methods. The isolated PGPRs was Bacillus subtilis which was showed phosphate solubilization activity and they were enhanced 30% more green gram seed germination. The method of current research is screening, isolation and biochemical characterization of rhizobacteria form rhizosphere region of Raphanus sativus. Phosphate solubilizing bacteria, solubilized the unavailable phosphate and provide to plant. The main purpose of this research paper is to widen the understanding of the role of phosphate solubilizing bacteria in crop production as biofertilizers.

A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions

Hydrated electrons (eaq−) exhibit rapid degradation of diverse persistent organic contaminants (OCs) and hold great promise as a formidable reducing agent in water treatment. However, the diverse structures of compounds exert different influences on the second-order rate constant of hydrated electron reactions (keaq−), while the same OCs demonstrate notable discrepancies in keaq− values across different pH levels. This study aims to develop machine learning (ML) models that can effectively simulate the intricate reaction kinetics between eaq− and OCs. Furthermore, the introduction of the pH variable enables a comprehensive investigation into the impact of ambient conditions on this process, thereby improving the practicality of the model. A dataset encompassing 701 keaq− values derived from 351 peer-reviewed publications was compiled. To comprehensively investigate compound properties, this study introduced molecular descriptor (MD), molecular fingerprint (MF), and the integration of both (MD + MF) as model variables. Furthermore, 60 sets of predictive models were established utilizing two variable screening methodologies (MLR and RF) and ten prominent algorithms. Through statistical parameter analysis, it was determined that descriptors combined with MD and MF, the RF screening method, and the symbolism algorithm exhibited the best predictive efficacy. Importantly, the combination of descriptor models exhibited significantly superior performance compared to individual MF and MD models. Notably, the optimal model, denoted as RF - (MF + MD) - LGB, exhibited highly satisfactory predictive results (R2tra = 0.967, Q2tra = 0.840, R2ext = 0.761). The mechanistic explanation study based on Shapley Additive Explanations (SHAP) values further elucidated the crucial influences of polarity, pH, molecular weight, electronegativity, carbon–carbon double bonds, and molecular topology on the degradation of OCs by eaq−. The proposed modeling approach, particularly the integration of MF and MD, alongside the introduction of pH, may furnish innovative ideas for advanced reduction or oxidation processes (ARPs/AOPs) and machine learning applications in other domains.

Prediction of IDO1 Inhibitors by a Fingerprint-Based Stacking Ensemble Model Named IDO1Stack.

Indoleamine 2,3-dioxygenase 1 (IDO1) is viewed as an extremely promising target for cancer immunotherapy. Here, we proposed a two-layer stacking ensemble model, IDO1Stack, that can efficiently predict IDO1 inhibitors. First, we constructed a series of classification models based on five machine learning algorithms and eight molecular characterization methods. Then, a stacking ensemble model was built using the top five models as the base classifier and logistic regression as the meta-classifier. The areas under the receiver operating characteristic curve (AUC) of IDO1Stack on the test set and external validation set were 0.952 and 0.918, respectively. Furthermore, we computed the applicability domain and privileged substructures of the model and interpreted the model using SHapley Additive exPlanations (SHAP). It is expected that IDO1Stack can well study the interaction between target and ligand, providing practitioners with a reliable tool for rapid screening and discovery of IDO1 inhibitors.

Soil Bacterial Communities from Three Agricultural Production Systems in Rural Landscapes of Palmira, Colombia.

Soils play important roles in the proper functioning of agroecosystems. Using molecular characterization methods such as metabarcoding, soils from eight farms (57 samples) belonging to three production system types-agroecological (two farms with twenty-two sampling points), organic (three farms with twenty-one sampling points), and conventional (three farms with fourteen sampling points)-were compared from the rural villages of El Arenillo and El Mesón in Palmira, Colombia. Amplification and sequencing of the hypervariable V4 region of the 16S rRNA gene was performed using next-generation sequencing (Illumina MiSeq) to estimate the bacterial composition and the alpha and beta diversity present. Across all soil samples, we found 2 domains (Archaea and Bacteria), 56 phylum, 190 classes, 386 orders, 632 families, and 1101 genera to be present. The most abundant phyla in the three systems were Proteobacteria, (agroecological 28%, organic 30%, and conventional 27%), Acidobacteria (agroecological 22%, organic 21%, and conventional 24%), and Verrucomicrobia (agroecological 10%, organic 6%, and conventional 13%). We found 41 nitrogen-fixing and phosphate-dissolving genera which promote growth and pathogens. Alpha and beta diversity indices were very similar across the three agricultural production systems, as reflected by shared amplicon sequence variants (ASVs) among them, likely due to the proximity of the sampling sites and recent management changes.

Virtual screening of drugs targeting PD-L1 protein

Monoclonal antibody inhibitors targeting PD-1/PD-L1 immune checkpoints are gradually entering the market and have achieved certain positive effects in the treatments of various types of tumors. However, with the expansion of application, the limitations of antibody drugs and problems such as excessive homogenization of research gradually appear, making small-molecule inhibitors the new focus of researchers. This study aims to use ligand-based and structure-based binding activity prediction methods to achieve virtual screening of small-molecule inhibitors targeting PD-L1, thereby helping to accelerate the development of small molecule drugs. A dataset of PD-L1 small-molecule inhibitory activity from relevant research literature and patents is collected and activity judgment classification models with intensity prediction regression models are constructed based on different molecular featurization methods and machine learning algorithms. The two types of models filter 68 candidate compounds with high PD-L1 inhibitory activity from a large drug-like small molecule screening pool (ZINC15). Ten of these compounds not only have good drug similarities and pharmacokinetics, but also exhibit comparable binding affinities and similar mechanisms of action with previous reported hotspot compounds in molecular docking. This phenomenon is further verified in subsequent molecular dynamics simulation and the estimation of binding free energy. In this study, a virtual screening workflow integrating ligand-based method and structure-based method is developed, and potential PD-L1 small-molecule inhibitors are effectively screened from large compound databases, which is expected to help accelerate the application and expansion of tumor immunotherapy.

Combining multidimensional chromatography-mass spectrometry and feature-based molecular networking methods for the systematic characterization of compounds in the supercritical fluid extract of Tripterygium wilfordii Hook F.

Tripterygium wilfordii Hook F from the family Celastraceae is a traditional Chinese medicine (TCM) whose principal chemical constituents are terpenoids, including sesquiterpene alkaloids and diterpenoids, which have unique and diverse structures and remarkable biological activities. In order to advance pharmacological research and guide the preparation of monomer compounds derived from T. wilfordii, a systematic approach to efficiently discover new compounds or their derivatives is needed. Herein, compound separation and identification were performed by offline reversed-phase × supercritical fluid chromatography coupled mass spectrometry (RP × SFC-Q-TOF-MS/MS) and Global Natural Product Social (GNPS) molecular networking. The 2D chromatography system exhibited a high degree of orthogonality and significant peak capacity, and SFC has an advantage during the separation of sesquiterpene alkaloid isomers. Feature-based molecular networking offers the great advantage of quickly detecting and clustering unknown compounds, which greatly assists in intuitively judging the type of compound, and this networking technique has the potential to dramatically accelerate the identification and characterization of compounds from natural sources. A total of 324 compounds were identified and quantitated, including 284 alkaloids, 22 diterpenoids and 18 triterpenoids, which means that there are numerous potential new compounds with novel structures to be further explored. Overall, feature-based molecular networking provides an effective method for discovering and characterizing novel compounds and guides the separation and preparation of targeted natural products.