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Natural products (NPs) have long been associated with human production and play a key role in the survival of species. Significant variations in NP content may severely affect the "return on investment" of NP-based industries and render ecological systems vulnerable. Thus, it is crucial to construct a platform that relates variations in NP content to their corresponding mechanisms. In this study, a publicly accessible online platform, NPcVar (http://npcvar.idrblab.net/), was developed, which systematically described the variations of NP contents and their corresponding mechanisms. The platform comprises 2201 NPs and 694 biological resources, including plants, bacteria, and fungi, curated using 126 diverse factors with 26,425 records. Each record contains information about the species, NP, and factors involved, as well as NP content data, parts of the plant that produce NPs, the location of the experiment, and reference information. All factors were manually curated and categorized into 42 classes which belong to four mechanisms (molecular regulation, species factor, environmental condition, and combined factor). Additionally, the cross-links of species and NP to well-established databases and the visualization of NP content under various experimental conditions were provided. In conclusion, NPcVar is a valuable resource for understanding the relationship between species, factors, and NP contents and is anticipated to serve as a promising tool for improving the yield of high-value NPs and facilitating the development of new therapeutics.
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Productos Biológicos , Humanos , HongosRESUMEN
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.
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Aprendizaje Automático , Proteínas , Proteínas/química , Mapas de Interacción de ProteínasRESUMEN
The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.
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Aprendizaje Automático , Proteínas , Almacenamiento y Recuperación de la InformaciónRESUMEN
Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.
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Coronavirus , Interacciones Huésped-Patógeno , ARN Viral , Humanos , Coronavirus/genética , Coronavirus/metabolismo , Infecciones por Coronavirus/metabolismo , Interacciones Huésped-Patógeno/genética , ARN Viral/genética , ARN Viral/metabolismo , Replicación Viral , Bases de Datos GenéticasRESUMEN
In this study, matrix solid phase dispersion (MSPD) microextraction combined with supercritical fluid chromatography-ion mobility quadrupole time-of-flight mass spectrometry (SFC/IM-QTOF-MS) was used to analyze the multipolar compounds in plant tea. The parameters of stationary phase, mobile phase, make-up solution, temperature, and back pressure were optimized. The target analytes were gradient eluted in 8 min by supercritical CO2 on a Zorbax RX-SIL column. Collisional Cross Section (CCS) values for single and multiple fields were measured. A series of validation studies were carried out under the optimal conditions, and the linear relationship and reproducibility were good. The limits of detection were 1.4 (Scoparone (1))~70 (Naringenin (4)) ng/mL, and the limits of quantification were 4.7 (Scoparone (1))~241 (Naringenin (4)) ng/mL. The recoveries of most compounds ranged from 60.7% to 127%. As a consequence, the proposed method was used for the separation and quantitative analysis of active ingredients in caulis dendrobii.
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The oxidation products and metabolic pathways of five Citrus flavonoids were studied by online electrochemical/quadrupole time-of-flight mass spectrometry (EC/Q-TOF/MS). The simulated oxidation metabolism of target compounds in phase I and phase â ¡ was carried out at boron-doped diamond (BDD) working electrode. The results obtained by EC-MS were compared with the conventional metabolism of rats and humans reported in previous literatures. In addition, the method of incubating the target compounds with rat liver microsomes in vitro was established, the target compounds and their metabolites were analyzed by high performance liquid chromatography coupled mass spectrometry. The structures of the metabolites were determined by accurate mass measurements and previous in vivo metabolite results. The results showed that the electrochemical oxidation metabolites were consistent with the results of in vitro incubation of liver microsomes, and also with the results reported in other literatures. As a consequence, EC/Q-TOF/MS is a promising and effective tool for studying metabolic transformation of different complex food components.
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Citrus , Flavonoides , Animales , Cromatografía Líquida de Alta Presión , Espectrometría de Masas , Microsomas Hepáticos , RatasRESUMEN
A boron nitride nanosheet (BNNS)-assisted matrix solid-phase dispersion method was established to microextract alkaloids from medicinal plants. The target compounds were identified by high-performance liquid chromatography coupled with ultraviolet detection and ion mobility quadrupole time-of-flight mass spectrometry. During the experimental process, several important parameters, including the type of dispersant, the amount of dispersant, the grinding time, and the type of elution solvent, were optimized. Finally, the BNNSs were chosen as the best dispersant, and their microcosmic morphologies were identified by scanning electron microscopy and transmission electron microscopy. Because of the special property of BNNSs, the cost of this experiment was greatly reduced, especially in elution volume, sample amount (50 mg), and extraction time (2 min). Under the best conditions, 50 mg of sample powder was dispersed with 50 mg of BNNSs, the grinding time was 120 s, the mixed powder was eluted with 200 µL of methanol, and good linearity (r2 > 0.9993) and satisfactory recoveries (80-100%) were obtained. The inter- and intraday precisions were acceptable, with RSDs lower than 2.01 and 4.84%, respectively. The limits of detection ranged from 2.54 to 15.00 ng/mL, and the limits of quantitation were 8.47 to 50.00 ng/mL. The proposed method was successfully applied for the determination of liensinine, isoliensinine, and neferine in lotus plumule.
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Alcaloides , Lotus , Compuestos de Boro , Cromatografía Líquida de Alta Presión/métodos , Espectrometría de Masas/métodos , Polvos , Extracción en Fase Sólida/métodosRESUMEN
In this study, a supercritical fluid chromatography (SFC) method based on ion pair reagents was used to separate alkaloids. The chromatographic parameters, including the stationary phase, additive type, additive concentration, outlet pressure, temperature and flow rate, were optimized. Baseline separation was completed in 20 min on an Agilent Pursuit 5 PFP column (4.6 × 150 mm) using carbon dioxide as the mobile phase and 7.5 mM sodium 1-pentanesulfonate as an additive with gradient elution at 140 bar, 60 °C, and a flow rate of 1.5 mL/min. The retention rate and resolution of the analytes were satisfactory. The limits of detection were 27.04-298.03 ng/mL, and the limits of quantification were 90.15-993.42 ng/mL. The recoveries of low and high concentrations were 77.46-111.86% and 83.84-111.00%, respectively. This ion pair additive greatly improved the separation efficiency of alkaloids. Consequently, this SFC method was successfully applied to the separation of alkaloids from Rhizoma corydalis.