Food & Function
·
2020
·Vol. 11
·期 4
·3043-3052
·
IF 4.171
Wenli Yan,Guimei Lin,Rong Zhang,Zhen Liang,Wenjuan Wu
Two novel effective antioxidative tripeptides GWY and QWY were designed based on 3D-QSAR models. Their activities were confirmed by an improved TEAC assay. The experimental results showed that GWY and QWY possessed good antioxidant activity, equaling 3.32 mM TE and 2.97 mM TE respectively. This indicated that 3D-QSAR models possessed significant predictive capacity for drug design. In addition, molecular docking and molecular dynamics simulation were applied to reveal the potential molecular mechanism of antioxidant peptides. The result showed that GWY and QWY could enhance the stability of Keap1 by interacting with the key residues Arg415, Arg483, Arg380 and Ser555 in the active sites. Interestingly, the key residues were exactly the binding site of Nrf2 in the active pocket of Keap1. Thus, GWY and QWY could compete with Nrf2 for binding to Keap1. This demonstrated that the new tripeptides might have the ability to activate the signaling pathway Keap1–Nrf2–ARE and improve the antioxidant defense system of the body as well.
研究领域:
生物化学
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药物设计
·
分子生物学
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产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
·
2021
·
IF 7.514
Jia-Yi Yin,Ya-Ning Han,Meng-Qi Liu,Zan-Hao Piao,Xu Zhang,Yu-Ting Xue,Ying-Hua Zhang
Human health can be damaged by free radicals, and antioxidant peptides are excellent radical scavengers. Antioxidant tripeptides data set based on 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulofnic acid) (ABTS) assay was created, 9 types of descriptors were integrated and 4 quantitative structure–activity relationship (QSAR) models were constructed in this study. Several structural factors influencing the activity of antioxidant tripeptides and the dominant amino acids at each position of tripeptides were revealed by the optimal model. Ten food-derived tripeptides with higher activity were selected for synthesis and activity determination. Molecular docking results demonstrated that these tripeptides were stably bound to the Keap1 receptor, further elucidating the antioxidant mechanism. It was known from the simulation of gastrointestinal digestion experiments that the model results possessed a guiding effect on the selection of proteins with high antioxidant activity . The performance of the model was proved to be robust after validation.
研究领域:
生物信息学
·
营养学
·
分子生物学
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF HAZARDOUS MATERIALS
·
2024
·
IF 12.2
Beibei Li,Ruijuan Qu,Ting Wang,Ruixue Guo,Jie Tian,Shuyi Li,Mostafa R. Abukhadra,Rehab Khaled Mahmoud,Zunyao Wang
In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants ( k obs ). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy ( E gap) and k obs . MLR analysis further underscored E gap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees , feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed E gap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors , establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model's stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry .
研究领域:
环境化学
·
计算化学
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产品关联
DOI
PubMed
高影响因子
Frontiers in Environmental Science
·
2023
·
IF 4.6
Zhang Meilan,Qin Shengnan,Tan Yujia,Shen Zhemin
Introduction: The inorganic coagulant AlCl3 is used in the traditional coagulation method for the decolorization of industrial dye wastewater. We studied its effectiveness in 41 kinds of dye with different structures, including azo, anthraquinone, arylmethane, and indigo dyes.Discussion: The optimal conditions for the removal of dye in the AlCl3 coagulation system were alkaline > neutral > acidic conditions. Under alkaline conditions, the hydrolysis colloid of AlCl3 is positively charged and easily combined with negatively charged anionic dyes by electrostatic adsorption. Therefore, the relationships between the dye removal behavior and molecular parameters under alkaline conditions were analyzed.Methods: Quantitative structure–activity relationship (QSAR) models were built for the color removal rates (Rexp) of 41 dyes and 46 molecular parameters computed by the density functional theory (DFT). Internal validation, external validation, statistical tests, Y-randomization, and applicability domain tests indicated that the optimal models are stable, accurate, reliable, and predictive.Results: The optimal QSAR model showed that surface area (approx.) (SAA) and molecular weight (MW) are two key molecular parameters. Moreover, electrostatic forces and hydrogen bonding are the predominant adsorption forces in this coagulation process.
研究领域:
环境科学
·
化学
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DOI
高影响因子
Food & Function
·
2022
·Vol. 13
·期 14
·7529-7539
·
IF 6.317
Hongxia Xiu,Yajie Liu,Huihui Yang,Haibin Ren,Bowen Luo,Zhipeng Wang,Hong Shao,Fengzhong Wang,Jingjian Zhang,Yutang Wang
Umami substances can increase the overall taste of food and bring pleasure to people. However, it is still challenging to identify the umami molecules through virtual screening due to the crystal structure of the umami receptor being undefined. Herein, based on the hypothesis that the molecules with bitter and sweet taste characteristics may be umami molecules, this study proposed an in silico method to identify novel umami-tasting molecules in batch from SWEET-DB and BitterDB databases via the QSAR models, PCA, molecular docking and electronic tongue analysis. In total, 169 potential umami molecules were identified through QSAR modeling, PCA, and molecular docking. Of the 169 molecules, 18 were randomly selected, and all were identified as umami molecules via electronic tongue analysis. Among the 18 chosen molecules, 10 molecules could be traced back to their concentration range in food, and finally, 8 molecules were predicted to be nontoxic. This work provides a simple and efficient strategy to identify novel umami molecules, holding an excellent promise for demonstrating the crystal structure of umami receptors and taste-sensing mechanisms. Furthermore, this study opens the possibility for the practical application of new umami molecules in food.
研究领域:
食品科学
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计算生物学
·
生物信息学
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产品关联
DOI
PubMed
高影响因子
PROCESS BIOCHEMISTRY
·
2023
·
IF 4.4
Qi Liu,Shan Shao,Jingyu Bao,Syed Jalil Shah,Shumin Yue,Xinqi Luan,Qing Liu,Linguang Xing,Zhongfeng Shi,Zhenxia Zhao,Zhongxing Zhao
The 3D-QSAR model is one of the most effective techniques for predicting the peptide bioactivity based on their structure properties. The accuracy of model prediction is highly dependent on the bio-activity of the peptides in its dataset. However, the same peptide used for constructing the database was reported to have obviously different activities in different published articles, which may significantly decrease the prediction accuracy. Based on this, we chose ACE inhibitory (ACE-I) dipeptides that can be absorbed directly into the human small intestine as research object. Ten dipeptides were randomly selected from the literatures for molecular docking with ACE and synthesized to test their activity. Ultimately, a linear equation was developed between bio-activities and docking scores, which was used to revalidate the reliable ACE-I activities of all reported dipeptides for constructing a credible dataset of 3D-QSAR model. The prediction accuracy was significantly enhanced and the model we established had a high Q 2 = 0.840 and R 2 = 0.998. Furthermore, dipeptide CW was predicted and tested in vitro to achieve the highest ACE-I activity (IC 50 = 0.16 μM), and the inhibition mechanism was also investigated. This study introduced a new method for predicting bio-activity with high precision using the 3D-QSAR model.
研究领域:
生物信息学
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药物化学
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生物化学
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产品关联
DOI
高影响因子
PHARMACEUTICAL RESEARCH
·
2003
·Vol. 20
·期 9
·1401-1408
·
IF 2.354
Wanchana Suchada,Yamashita Fumiyoshi,Hashida Mitsuru
Purpose . To develop a quantitative structure/activity relationship (QSAR) model for predicting drug-CYP 3A4 interactions.
Method . The inhibitory effect of 53 structurally diverse drugs on the metabolism of 7-benzyloxy-4-trifluoromethyl coumarin (BFC) by recombinant CYP 3A4 was evaluated using a rapid microtiter plate assay. For each drug, a total of 220 two-dimensional topological indices were calculated using Molconn-Z software. Using a genetic algorithm-based partial least squares (GA-PLS) method, the desired descriptors were automatically selected to maximize the predictability of the IC 50 values.
Results . The IC 50 values of the drugs tested ranged from 9 nM to 2 mM. Based on the GA-PLS method, five principal components derived from 20 Molconn-Z descriptors were found to be effective for QSAR modeling. Interestingly, these descriptors suggested that the molecular size would be an important factor in determining drug-CYP 3A4 interactions. In the leave-one-out prediction, the r
pred and the standard error of prediction ( s ) were 0.754 and 0.787, respectively. Even in an external validation, the predictions were in good agreement with experimental values ( r
pred = 0.744, s = 0.769, n = 9).
Conclusions . The proposed model, in which two-dimensional topological descriptors were used as molecular descriptors, was able to predict drug-CYP 3A4 interactions with reasonable accuracy.
研究领域:
药理学
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计算生物学
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药物化学
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DOI
PubMed
高影响因子
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
·
2015
·Vol. 23
·期 4
·3609-3620
·
IF 2.828
Su Pingru,Zhu Huicen,Shen Zhemin
Manganese dioxide formed in oxidation process by potassium permanganate exhibits promising adsorptive capacity which can be utilized to remove organic pollutants in wastewater. However, the structure variances of organic molecules lead to wide difference of adsorption efficiency. Therefore, it is of great significance to find a general relationship between removal rate of organic compounds and their quantum parameters. This study focused on building up quantitative structure activity relationship (QSAR) models based on experimental removal rate ( r
exp ) of 25 organic compounds and 17 quantum parameters of each organic compounds computed by Gaussian 09 and Material Studio 6.1. The recommended model is r
pre = −0.502 − 7.742 f (+) x + 0.107 E
HOMO + 0.959 q (H + ) + 1.388 BO
x . Both internal and external validations of the recommended model are satisfied, suggesting optimum stability and predictive ability. The definition of applicability domain and the Y-randomization test indicate all the prediction is reliable and no possibility of chance correlation. The recommended model contains four variables, which are closely related to adsorption mechanism. f (+) x reveals the degree of affinity for nucleophilic attack. E
HOMO represents the difficulty of electron loss. q (H + ) reflect the distribution of partial charge between carbon and hydrogen atom. BO
x shows the stability of a molecule.
研究领域:
环境科学
·
化学
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DOI
高影响因子
CHEMOSPHERE
·
2023
·
IF 8.8
Jin J. Li,Ya X. Yue,Jie F. Jiang,Sheng J. Shi,Hui X. Wu,Yuan H. Zhao,Fei F. Che
Antibiotics have received much attention owing to their ecotoxicity toward nontarget aquatic creatures. However, the mode of action (MOA) of toxicity against nontarget organisms is unclear in some aquatic organisms. In this study, the comparison of toxicities through interspecies correlations, excess toxicity calculated from toxicity ratio, and quantitative structure-activity relationship (QSAR) was carried out to investigate the MOAs for 14 antibiotics among Daphnia magna , Vibrio fischeri, and Pseudokirchneriella subcapitata . The results showed that interspecies toxicity correlations were very poor between any two of the three species for the 14 antibiotics. The toxicity ratio revealed that most antibiotics exhibited excess toxicity to algae and Daphnia magna but not to V. fischeri , demonstrating that some antibiotics share the same MOA, but some antibiotics share different MOAs among the three different levels of species. P. subcapitata was the most sensitive species, and V. fischeri was the least sensitive species. This is because of the differences in the biouptake and interactions of antibiotics with the target receptors between the three different trophic levels of the species. Molecular docking simulations suggested that the toxicity of antibiotics depends highly on their interactions with target receptors through hydrogen bonds , electrostatic or polar interactions, π bond interactions, and van der Waals forces . QSAR models demonstrated that hydrogen bonding and electrophilicity/nucleophilicity play key roles in the interaction of antibiotics with different receptors in the three species. The toxic mechanisms of antibiotics are attributed to the interactions between electrophilic antibiotics and biological nucleophiles , and hydrogen-bond interactions. These results are valuable for understanding the toxic mechanisms and MOA of the three different levels of species.
研究领域:
生态毒理学
·
环境科学
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DOI
PubMed
高影响因子
SCIENCE OF THE TOTAL ENVIRONMENT
·
2023
·
IF 9.8
Min Sun,Xian-Zhang Wang,Ren-Ying Xiong,Xiangying Chen,Lin-Feng Zhai,Shaobin Wang
Utilization of sewage sludge for the fabrication of environmental functional materials is highly desirable to achieve pollution mitigation and resource recovery. In the present work, we introduced a novel MgAl-layered double oxide (LDO)@biochar composite adsorbent in-situ fabricated from Al-rich sewage sludge, and its excellent application in nanoplastics adsorption. Initially, fifteen model contaminants with varied conjugate structures, hydrogen bonding and ionic properties were selected for an investigation of adsorption behavior and adsorption selectivity on LDO@biochar. Structural variation of LDO@biochar suggested reconstruction of the layered double hydroxide (LDH) during the adsorption process due to the “memory effect”. Under the synergy of LDH and biochar, the contaminants were adsorbed via multiple adsorbent-adsorbate interactions, including anion exchange, electrostatic interaction, hydrogen bonding and π-π conjugation. Then, a quantitative structure-activity relationship (QSAR) model was constructed by integrating the number of hydrogen bond acceptors, polarity surface area, number of aromatic rings, and Fukui index f(−) x together to reflect the affinity of each contaminant to the adsorbent. Guided by the QSAR model, the negatively charged polystyrene nanoplastics with continuously conjugated aromatic rings were predicted to be effectively adsorbed on LDO@biochar. Experimental tests confirmed a great capacity of LDO@biochar towards the polystyrene nanoplastics, given the equilibrium adsorption capacity as high as 360 mg g −1 at 30–50 °C. This work not only opened up a new avenue for sustainable utilization of sewage sludge towards high-performance environmental functional materials, but also demonstrated the potential of the QSAR analysis as a rapid and accurate approach for guiding the application of an adsorbent to new emerging containments.
研究领域:
环境科学
·
材料科学
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产品关联
DOI
PubMed
高影响因子
CHEMOSPHERE
·
2023
·
IF 8.8
Jia Wang,Xujia Zhang,Lingyun Fan,Limin Su,Yuanhui Zhao
Insecticides are widely used in crop protection against insects and frequently detected in aquatic environment. Photolysis kinetics are directly related with exposure assessment and risk assessment. However, the photolysis mechanism of neonicotinoid insecticides with different structures has not been studied and compared systematically in the literature. In this paper, the photolysis rate constants in water were determined for eleven insecticides under irradiation of simulated sunlight. At the same time, the photolysis mechanism and effect of dissolved organic matter (DOM) on their photolysis were studied. The results showed that photolysis rates of eleven insecticides vary in a large range. The photolysis rates of nitro-substituted neonicotinoids and butenolide insecticide are much faster than that of cyanoimino-substituted neonicotinoids and sulfoximine insecticide. The ROS scavenging activity assays reveal that direct photolysis dominates the degradation of seven insecticides and, on the other hand, self-sensitized photolysis dominates four insecticides. The shading-effect from DOM can reduce the direct photolysis rates, on the other hand, ROSs generated by triplet-state DOM ( 3 DOM*) can also accelerate photolysis of insecticides. According to the photolytic products identified from HPLC-MS, these eleven insecticides have different photolysis pathways. Six insecticides are degraded from the removal of nitro group from their parent compounds and four insecticides are degraded through ·OH reaction or singlet oxygen ( 1 O 2 ) reaction. QSAR (quantitative structure-activity relationship) analysis showed that photolysis rate was directly related to the energy gap between the highest occupied molecular orbital to the lowest unfilled molecular orbital ( E gap = E LUMO − E HOMO ) and dipole moment (δ). These two descriptors reflect the chemical stability and reactivity of insecticides. The pathways developed from identified products and the molecular descriptors of QSAR models can well verify the photolysis mechanisms of eleven insecticides.
研究领域:
环境科学
·
化学
✓ 已验证
DOI
PubMed
高影响因子
ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY
·
2025
·
IF 2.8
Qi Qi,Wang Zhuang
In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by generating intracellular reactive oxygen species (ROS), serving as a key mechanism in their cytotoxicity studies. However, existing in silico methods still face significant challenges in predicting the oxidative stress effects induced by ENMs. Herein, we utilized laboratory-derived toxicity data and machine learning methods to develop quantitative nanostructure-activity relationship (nano-QSAR) classification and regression models, aiming to predict the oxidative stress effects of five carbon nanomaterials (fullerene, graphene, graphene oxide, single-walled carbon nanotubes, and multi-walled carbon nanotubes) and their binary mixtures on Scenedesmus obliquus cells. We constructed five nano-QSAR classification models by combining zeta potential (ζP) with the C4.5 decision tree, support vector machine, artificial neural network, naive Bayes, and K-nearest neighbor algorithms. Moreover, we constructed three classification models by integrating the features including ζP, hydrodynamic diameter (DH), and specific surface area (SSA) with the logistic regression, random forest, and Adaboost algorithms. The Accuracy, Recall, Precision and harmonic mean of Precision and Recall (F1-score) values of these models were all higher than 0.600, indicating an excellent performance in distinguishing whether CNMs have the potential to generate ROS. In addition, using the ζP, DH, and SSA descriptors, we combined decision tree regression, random forest regression, gradient boosting, and the Adaboost algorithm, and successfully constructed four nano-QSAR regression models with applicable application domains (all training and testing data points lie within 95% confidence intervals), goodness-of-fit (Rtrain2 ≥ 0.850), and robustness (cross-validation R2 ≥ 0.650) as well as predictive power (Rtest2 ≥ 0.610). The method developed would establish a fundamental basis for more precise evaluations of ecological risks posed by these materials from a mechanistic standpoint.
研究领域:
环境毒理学
·
计算生物学
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纳米科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）