Thermostability-assisted limited proteolysis-coupled mass spectrometry for capturing drug target proteins and sites.

BACKGROUND: Identifying drug-binding targets and their corresponding sites is crucial for drug discovery and mechanism studies. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a sophisticated method used for the detection of compound and protein interactions. However, in some cases, LiP-MS cannot identify the target proteins due to the small structure changes or the lack of enrichment of low-abundant protein. To overcome this drawback, we developed a thermostability-assisted limited proteolysis-coupled mass spectrometry (TALiP-MS) approach for efficient drug target discovery. RESULTS: We proved that the novel strategy, TALiP-MS, could efficiently identify target proteins of various ligands, including cyclosporin A (a calcineurin inhibitor), geldanamycin (an HSP90 inhibitor), and staurosporine (a kinase inhibitor), with accurately recognizing drug-binding domains. The TALiP protocol increased the number of target peptides detected in LiP-MS experiments by 2- to 8-fold. Meanwhile, the TALiP-MS approach can not only identify both ligand-binding stability and destabilization proteins but also shows high complementarity with the thermal proteome profiling (TPP) and machine learning-based limited proteolysis (LiP-Quant) methods. The developed TALiP-MS approach was applied to identify the target proteins of celastrol (CEL), a natural product known for its strong antioxidant and anti-cancer angiogenesis effect. Among them, four proteins, MTHFD1, UBA1, ACLY, and SND1 were further validated for their strong affinity to CEL by using cellular thermal shift assay. Additionally, the destabilized proteins induced by CEL such as TAGLN2 and CFL1 were also validated. SIGNIFICANCE: Collectively, these findings underscore the efficacy of the TALiP-MS method for identifying drug targets, elucidating binding sites, and even detecting drug-induced conformational changes in target proteins in complex proteomes.

Discovery of effective combination from Renshen-Fuzi herbal pair against heart failure by spectrum-effect relationship analysis and zebrafish models.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional herbal pair Ginseng Radix et Rhizoma (roots and rhizomes of Panax ginseng C.A. Mey, Renshen in Chinese) and Aconiti Lateralis Radix Praeparata (lateral roots of Aconitum carmichaelii Debeaux, Fuzi in Chinese), composition of two traditional Chinese medicinal herbs, has been widely used in traditional Chinese medicine formula, in which Shenfu decoction has been used clinically in China for the treatment of heart failure at present. AIM OF THE STUDY: Although the ginsenosides and aconite alkaloids have been proven as the essential bioactive components in Renshen-Fuzi herbal pair, the exact composition of effective components to combat heart failure are still unclear. Therefore, spectrum-effect relationship analysis was performed to reveal its effective combination for anti-heart failure effect. MATERIALS AND METHODS: Firstly, the chemical constituents of Renshen-Fuzi herbal pair were identified using ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS). The 39 major compounds in Renshen-Fuzi with five different compatibility ratios were simultaneously quantified using ultra high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UHPLC-QQQ MS/MS). Subsequently, zebrafish models induced by verapamil hydrochloride were constructed and four heart failure-related indexes were selected for pharmacodynamic evaluation of Renshen-Fuzi. To analyze the spectrum-effect relationships, partial least squares regression (PLSR) models were established among the contents of 39 compounds in Renshen-Fuzi with each pharmacodynamic index. According to the contribution of each compound to the whole efficacy, 12 compounds were finally screened out as the effective combination. RESULTS: A total of 157 chemical compounds of Renshen-Fuzi herbal pair were identified, in which 39 components were simultaneously determined. The pharmacological effects indicated that Renshen-Fuzi with 1:2 ratio exhibited the best effect based on zebrafish model, which could improve cardiac output and blood flow velocity and inhibit pericardial enlargement and venous blood stasis significantly. A combination of 9 ginsenosides and 3 aconite alkaloids based on a component-efficacy modeling by PLSR was screened, and exerted approximately equivalent pharmacological effects compared with Renshen-Fuzi herbal pair. CONCLUSIONS: Our findings elucidated the effective combination of Renshen-Fuzi herbal pair that has been used in clinic for the treatment of heart failure, which could also promote the pharmacological research and quality control of their formula such as Shenfu decoction.

A molecular networking-assisted automatic database screening strategy for comprehensive annotation of small molecules in complex matrices.

Liquid chromatography-tandem with high-resolution mass spectrometry (LCHRMS) has proven challenging for annotating multiple small molecules within complex matrices due to the complexities of chemical structure and raw LCHRMS data, as well as limitations in previous literatures and reference spectra related to those molecules. In this study, we developed a molecular networking assisted automatic database screening (MN/auto-DBS) strategy to examine the combined effect of MS1 exact mass screening and MS2 similarity analysis. We compiled all previously reported compounds from the relevant literatures. With the development of a Python software, the in-house database (DB) was created by automatically calculating the m/z and data from experimental MS1 hits were rapid screened with DB. We then performed a feature-based molecular network analysis on the auto-MS2 data for supplementary identification of unreported compounds, including clustered FBMN and annotated GNPS compounds. Finally, the results from both strategies were merged and manually curated for correct structural assignment. To demonstrate the applicability of MN/auto-DBS, we selected the Huangqi-Danshen herb pair (HD), commonly used in prescriptions or patent medicines to treat diabetic nephropathy and cerebrovascular disease. A total of 223 compounds were annotated, including 65 molecules not previously reported in HD, such as aromatic polyketides, coumarins, and diarylheptanoids. Using MN/auto-DBS, we can profile and mine a wide range of complex matrices for potentially new compounds.

Functional annotation map of natural compounds in traditional Chinese medicines library: TCMs with myocardial protection as a case.

The chemical complexity of traditional Chinese medicines (TCMs) makes the active and functional annotation of natural compounds challenging. Herein, we developed the TCMs-Compounds Functional Annotation platform (TCMs-CFA) for large-scale predicting active compounds with potential mechanisms from TCM complex system, without isolating and activity testing every single compound one by one. The platform was established based on the integration of TCMs knowledge base, chemome profiling, and high-content imaging. It mainly included: (1) selection of herbal drugs of target based on TCMs knowledge base; (2) chemome profiling of TCMs extract library by LC‒MS; (3) cytological profiling of TCMs extract library by high-content cell-based imaging; (4) active compounds discovery by combining each mass signal and multi-parametric cell phenotypes; (5) construction of functional annotation map for predicting the potential mechanisms of lead compounds. In this stud TCMs with myocardial protection were applied as a case study, and validated for the feasibility and utility of the platform. Seven frequently used herbal drugs (Ginseng, etc.) were screened from 100,000 TCMs formulas for myocardial protection and subsequently prepared as a library of 700 extracts. By using TCMs-CFA platform, 81 lead compounds, including 10 novel bioactive ones, were quickly identified by correlating 8089 mass signals with 170,100 cytological parameters from an extract library. The TCMs-CFA platform described a new evidence-led tool for the rapid discovery process by data mining strategies, which is valuable for novel lead compounds from TCMs. All computations are done through Python and are publicly available on GitHub.

Time-Series-Dependent Global Data Filtering Strategy for Mining and Profiling of Xenobiotic Metabolites in a Dynamic Complex Matrix: Application to Biotransformation of Flavonoids in the Extract of Ginkgo biloba by Gut Microbiota.

Efficient characterization of xenobiotic metabolites and their dynamics in a changing complex matrix remains difficult. Herein, we proposed a time-series-dependent global data filtering strategy for the rapid and comprehensive characterization of xenobiotic metabolites and their dynamic variation based on metabolome data. A set of data preprocessing methods was used to screen potential xenobiotic metabolites, considering the differences between the treated and control groups and the fluctuations over time. To further identify metabolites of the target, an in-house accurate mass database was constructed by potential metabolic pathways and applied. Taking the extract of Ginkgo biloba (EGB) co-incubated with gut microbiota as an example, 107 compounds were identified as flavonoid-derived metabolites (including 67 original from EGB and 40 new) from 7468 ions. Their temporal metabolic profiles and regularities were also investigated. This study provided a systematic and feasible method to elucidate and profile xenobiotic metabolism.

Comprehensive chemical profiling of volatile constituents of Angong Niuhuang Pill in vitro and in vivo based on gas chromatography coupled with mass spectrometry.

BACKGROUND: Angong Niuhuang Pill (ANP), a renowned precious traditional Chinese medicine prescription, is extensively utilized for the clinical treatment of stroke, meningitis and encephalorrhagia in China. As a classic resuscitation-inducing aromatic prescription, ANP has been investigated for its pharmacological effects in recent years, while the volatile composition in ANP still lacks comprehensive elucidation. METHOD: To better explore the volatile constituents in ANP, a qualitative analysis method was developed based on gas chromatography coupled with mass spectrometry. Furthermore, a validated quantitative method was established to determine 21 main compounds in 8 batches of commercially available ANP samples by gas chromatography-tandem mass spectrometry. The quantitative data were successively subjected to Pearson correlation coefficient analysis. Additionally, the absorbed volatile constituents in rat plasma after single oral administration of ANP have also been characterized. RESULTS: A total of 93 volatile constituents including 29 sesquiterpenoids, 28 monoterpenoids, 13 fatty acids and their esters, 7 alkanes, 6 ketones, 3 phenols, 3 aldehydes, 2 benzoate esters, and 2 other types, were preliminarily characterized, which primarily originated from Borneolum, Moschus, Curcumae Radix, and Gardeniae Fructus. D-Borneol, isoborneol and muscone were the top three abundant ingredients (> 600 µg/g) in 8 batches of ANP samples. Subsequently, the average Pearson correlation coefficient of the contents of 21 analytes was 0.993, inferring the high batch-to-batch similarity among 8 batches. After oral administration of ANP, D-borneol, isoborneol, muscone and camphor were the main volatile constituents absorbed in the rat plasma. CONCLUSION: This research may be helpful for the comprehensive quality control study of ANP, and provide for guarantee the clinical efficacy of ANP.

Temporal heterogeneity in photosystem II photochemistry in Artemisia ordosica under a fluctuating desert environment.

Acclimation strategies in xerophytic plants to stressed environmental conditions vary with temporal scales. Our understanding of environmentally-induced variation in photosystem II (PSII) processes as a function of temporal scales is limited, as most studies have thus far been based on short-term, laboratory-controlled experiments. In a study of PSII processes, we acquired near-continuous, field-based measurements of PSII-energy partitioning in a dominant desert-shrub species, namely Artemisia ordosica, over a six-year period from 2012-2017. Continuous-wavelet transformation (CWT) and wavelet coherence analyses (WTC) were employed to examine the role of environmental variables in controlling the variation in the three main PSII-energy allocation pathways, i.e., photochemical efficiency and regulated and non-regulated thermal dissipation, i.e., Φ PSII, Φ NPQ, and Φ NO, respectively, across a time-frequency domain from hours to years. Convergent cross mapping (CCM) was subsequently used to isolate cause-and-effect interactions in PSII-energy partitioning response. The CWT method revealed that the three PSII-energy allocation pathways all had distinct daily periodicities, oscillating abruptly at intermediate timescales from days to weeks. On a diurnal scale, WTC revealed that all three pathways were influenced by photosynthetically active radiation (PAR), air temperature (T a), and vapor pressure deficit (VPD). By comparing associated time lags for the three forms of energy partitioning at diurnal scales, revealed that the sensitivity of response was more acutely influenced by PAR, declining thereafter with the other environmental variables, such that the order of influence was greatest for T a, followed by VPD, and then soil water content (SWC). PSII-energy partitioning on a seasonal scale, in contrast, displayed greater variability among the different environmental variables, e.g., Φ PSII and Φ NO being more predisposed to changes in T a, and Φ NPQ to changes in VPD. CCM confirmed the causal relationship between pairings of PSII-energy allocation pathways, according to shrub phenology. A. ordosica is shown to have an innate ability to (i) repair damaged PSII-photochemical apparatus (maximum quantum yield of PSII photochemistry, with F v/F m > 0.78), and (ii) acclimatize to excessive PAR, dry-air conditions, and prolonged drought. A. ordosica is relatively sensitive to extreme temperature and exhibits photoinhibition.

Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction.

Thermophilicity is a very important property of proteins, as it sometimes determines denaturation and cell death. Thus, methods for predicting thermophilic proteins and non-thermophilic proteins are of interest and can contribute to the design and engineering of proteins. In this article, we describe the use of feature dimension reduction technology and LIBSVM to identify thermophilic proteins. The highest accuracy obtained by cross-validation was 96.02% with 119 parameters. When using only 16 features, we obtained an accuracy of 93.33%. We discuss the importance of the different characteristics in identification and report a comparison of the performance of support vector machine to that of other methods.

Quantitative study on redistribution of nitrogen and phosphorus by wetland plants under different water quality conditions.

The application of wetland plants to purify surface rivers has gradually become an important means to control water pollution. However, there are many species of wetland plants which differ greatly in living conditions, water purification effects and pollutant migration paths. Therefore, it is necessary to select suitable wetland plants and quantitatively analyze the effects of different wetland plants on pollutant transport paths for the protection of water quality. In this research, the Typha orientalis C. Presl (T), Lemna minor L.(L) and Ceratophyllum demersum L.(C) were selected as typical wetland plants to conduct single-factor and multi-factor experiments under different water quality conditions. The results showed that wetland plants had significantly decreased nitrogen (N) and phosphorus (P) concentration in two sewage bodies. The NH4+-N and NO3--N removal efficiencies ranged from 71 to 96% and from 46% to 76%, and the PO43--P removal efficiencies ranged from 79% to 94% . The concentration of nutrient in the sewage decreased rapidly in the early stage and then tended to reach a stable state. The total nitrogen (TN) removal efficiencies under two kinds of sewage ranked as follows: T+L+C>T>C>L. Under high concentration sewage, the TN removal efficiencies by wetland plants mostly attributed to the change in the microbial status of the water body, which ranged from 82% to 95%. Under low concentration sewage, the combination of wetland plants could optimize the purification effect of plant consumption and microbial decomposition, and the TN removal efficiencies ranged from 75% to 95%. The total phosphorus (TP) removal efficiencies of T and T+L+C were better in two concentration sewages. The research demonstrated that P in sewage was mainly accumulated in soil matrix and it was important to select the emergent plants with well-develoed roots and vigorous growth to purify sewage.