A QSAR-based mechanistic study on the combined toxicity of antibiotics and quorum sensing inhibitors against Escherichia coli

Wang, Dali; Shi, Junyi; Xiong, Yanna; Hu, Jingyun; Zhao, Lin; Qiu, Yanling; Cheng, Jiangbo

doi:10.1016/j.jhazmat.2017.07.059

Cited by 34 publications

(23 citation statements)

References 35 publications

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“…The prediction of protein corona functional compositions is critical for the design of ideal NPs for clinical applications, but methods to quantitatively predict protein coronas have been unavailable to date (1,5). The high-dimensional relationships (involving at least eight qualitative factors and 13 quantitative factors) between the protein corona and NP properties present challenges to traditional models and experimental methods (18,19). By collecting knowledge from previous efforts, the present work assembled evidence to investigate protein corona formation behaviors, overcoming the limitations and uncertainty in distinct studies.…”

Section: Resultsmentioning

confidence: 99%

“…The general linear regression model cannot handle multivariable problems (15) and will be compared with machine learning in the present work. Furthermore, because of the high heterogeneity between the multidimensional properties of NPs and the protein corona functional components (17), many traditional models (e.g., quantitative structural activity relationship) poorly predict the functional fingerprints of the protein corona (18,19).…”

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confidence: 99%

“…With the robust capability to build models to explain observations through experience, machine learning [e.g., random forest (RF) (20) and neural network] has recently been applied to recognize meaningful complex patterns to control robots (21), predict reproductive responses (19), and predict synthetic reactions (22). RF is a robust machine learning algorithm integrating a decision tree with good learning capability (20).…”

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confidence: 99%

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Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

Ban

Yuan

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

167

112

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Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by R2 (less than 0.40). Here, the performance with R2 over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R2 (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

Ban

Yuan

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

167

112

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“…[21][22][23][24] Quantitative structure-activity relationship (QSAR) modeling can be used to determinec orrelations between modifications in geometric ande lectronic structure andc orresponding changes in ar esponse variable, such as partitionc oefficient or reactivity. [25] QSAR models are applied in ad iverse range of fields,f rom predicting biological toxicity based on structurald escriptors, [26] to correlating reactionb arriers with molecular orbitalp arameters. [27] With advances in computationalt echniques, quantum chemicalm ethods are increasingly being used to calculate descriptorstobuild the models.…”

Section: Introductionmentioning

confidence: 99%

Screening for Improved Nerve Agent Simulants and Insights into Organophosphate Hydrolysis Reactions from DFT and QSAR Modeling

Mendonca

Snurr

2019

Chemistry A European J

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The effective detoxification of chemical warfare agents, specifically nerve agents, is a pressing issue in the modern world. Due to the high toxicity of these molecules, simulants are often used in experiments as substitutes for the agents. However, there is little reason to believe that the current simulants used in the literature are optimal predictors of nerve agent reactivity. Density functional theory calculations were performed on the alkaline hydrolysis of over 100 organophosphate molecules to identify improved simulants for the G‐series nerve agents soman and sarin, based on low toxicity and similarity to nerve agent hydrolysis energetics and degradation mechanism. This screening highlighted 5 molecules that have nearly identical reaction barriers to the actual agents, while being far less toxic. Quantitative structure–activity relationship (QSAR) models were also derived to determine the most significant molecular descriptors for describing the hydrolysis free energy barriers of these reactions. The optimal QSAR model was subjected to a thorough statistical analysis and validation procedure to confirm its predictive capacity, showing excellent quantitative and ranking accuracy. It was further shown that the model trained on G‐series agents can reliably predict energetics for other organophosphate classes as well, including VX. Through these computational insights, experimentalists may be aided in accurately and safely studying these reactions with less toxic simulants.

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“…After loss of nicotine tolerance, the bacterial population possibly reduces their virulence in order to survive as a trade-off. Though we can not apply of QQ under nicotine stress due to its addiction, it gives us an explanation that the combination of QQ with antibiotics is higher efficient than only one treatment (Wang et al, 2018 ). Therefore, this study not only improves our understanding regarding the role of QS in environmental stress tolerance, but also provides a foundation for the development of QQ-based strategies to control or reduce the pathogenicity of bacteria (Figure 8 ).…”

Section: Discussionmentioning

confidence: 99%

Regulation of Nicotine Tolerance by Quorum Sensing and High Efficiency of Quorum Quenching Under Nicotine Stress in Pseudomonas aeruginosa PAO1

Tang

Zhang

et al. 2018

Front. Cell. Infect. Microbiol.

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Quorum sensing (QS) regulates the behavior of bacterial populations and promotes their adaptation and survival under stress. As QS is responsible for the virulence of vast majority of bacteria, quorum quenching (QQ), the interruption of QS, has become an attractive therapeutic strategy. However, the role of QS in stress tolerance and the efficiency of QQ under stress in bacteria are seldom explored. In this study, we demonstrated that QS-regulated catalase (CAT) expression and biofilm formation help Pseudomonas aeruginosa PAO1 resist nicotine stress. CAT activity and biofilm formation in wild type (WT) and ΔrhlR strains are significantly higher than those in the ΔlasR strain. Supplementation of ΔlasI strain with 3OC12-HSL showed similar CAT activity and biofilm formation as those of the WT strain. LasIR circuit rather than RhlIR circuit is vital to nicotine tolerance. Acylase I significantly decreased the production of virulence factors, namely elastase, pyocyanin, and pyoverdine under nicotine stress compared to the levels observed in the absence of nicotine stress. Thus, QQ is more efficient under stress. To our knowledge, this is the first study to report that QS contributes to nicotine tolerance in P. aeruginosa. This work facilitates a better application of QQ for the treatment of bacterial infections, especially under stress.

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A QSAR-based mechanistic study on the combined toxicity of antibiotics and quorum sensing inhibitors against Escherichia coli

Cited by 34 publications

References 35 publications

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

Screening for Improved Nerve Agent Simulants and Insights into Organophosphate Hydrolysis Reactions from DFT and QSAR Modeling

Regulation of Nicotine Tolerance by Quorum Sensing and High Efficiency of Quorum Quenching Under Nicotine Stress in Pseudomonas aeruginosa PAO1

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