Imidazolium ionic liquids as effective antiseptics and disinfectants against drug resistant S. aureus: In silico and in vitro studies

Hodyna, Diana; Kovalishyn, Vasyl; Semenyuta, Ivan; Blagodatnyi, Volodymyr; Rogalsky, Sergiy; Metelytsia, Larysa

doi:10.1016/j.compbiolchem.2018.01.012

Cited by 25 publications

(17 citation statements)

References 59 publications

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“…Ionic liquids are known to kill cells, which is believed to be due to osmotic shocking of cell membranes 46 . Live-dead stain of the anammox biofilms indicated both live and dead cells, even in the non-treated component (Figure 3A).…”

Section: Resultsmentioning

confidence: 99%

Isolation of a putative S-layer protein from anammox biofilm extracellular matrix using ionic liquid extraction

Wong

Natarajan

Boleij

et al. 2019

Preprint

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25Anaerobic ammonium oxidation (anammox) performing bacteria self-assemble into compact 26 biofilms by expressing extracellular polymeric substances (EPS). Anammox EPS are poorly 27 characterized, largely due to their low solubility in typical aqueous solvents. Pronase digestion 28 cationic exchange resin (CER) 23 , physical methods such as centrifugation, heating and 74 sonication or chemical methods by using organic solvents (detergents and ethanol), have been 75 made. However, to date no exopolymer has been isolated from the matrix of anammox biofilms, 76 which is a minimum requirement for subsequent biophysical and functional analysis. 77 While some metabolic proteins are conserved between anammox species (e.g. the c-type heme 78 proteins) 24 , it is unknown whether any extracellular proteins are similarly conserved. It has been 79 reported that surface layer (S-layer) proteins may be common to anammox biofilms as a 80 structural component of the cell. For example, KUSTD1514 forms a shell on the outside of the 81 cell envelope of Ca. Kuenenia stuttgartsiensis 25 , while a homologous S-layer protein is a major 82 EPS constituent of Ca. Brocadia-enriched granular biofilm and hypothesized to also contribute 83 to biofilm matrix structure (i.e. following shedding) 26 . S-layer proteins could be important to 84 biofilm formation more broadly 27 . Describing a function for S-layer proteins in biofilm 85formation is confounded by the fact that they are embedded in EPS and the S-layer protein has 86 not been isolated from the anammox biofilm matrix. 87To address the challenge of processing anammox EPS, we used ionic liquid 1-ethyl-3-methyl 88 imidazolium acetate (EMIM-Ac) to dissolve a laboratory anammox granular biofilm. 89Imidazolium-based ionic liquids are green solvents that have also been applied to process 90 similarly recalcitrant biopolymers (e.g. cellulose and chitosan) 28 , as protein stabilizers and co-91 solvents, and enzyme catalysts. We extracted and purified the putative S-layer protein from a 92 laboratory anammox granular biofilm. We found a putative S-layer protein to be the dominant 93 polymer in our biofilm extract. While six monosaccharides were co-enriched with the EPS, they 94 contributed <0.5% (w/w) of total granular biomass and were all common protein o-95 glycosylating sugars. It is thus likely that they were enriched as glycoprotein appendages rather 96 than free exopolysaccharides. We provide further support for an important role for S-layer 97 proteins in anammox biofilms, and present a method, involving EMIM-Ac, that allows for S-98 5 layer proteins to be isolated from complex matrices such as biofilm EPS. This will inform on 99 the role of S-layer proteins in anammox biofilm formation. 100 Materials and methods 101 102 Figure 1: Schematic diagram of anammox extracellular polymeric substances (EPS) extraction and 103 purification with ionic liquid (IL) 104 105 Anammox granular sludge enrichment 106 Anammox granular sludges (GR) were cultivated in a 4 L reactor seeded with activated s...

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Section: Resultsmentioning

confidence: 99%

Isolation of a putative S-layer protein from anammox biofilm extracellular matrix using ionic liquid extraction

Wong

Natarajan

Boleij

et al. 2019

Preprint

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“…The OCHEM platform provides several software packages to calculate many different descriptor sets 34,35 . In this study, eight descriptor sets were used to build QSPR models, including OEstate and ALogPS, CDK, Chemaxon, Dragon v6, ISIDA Fragment, Inductive Descriptor, Mordred, and PyDescriptor.…”

Section: Molecular Descriptors Calculation and Pruningmentioning

confidence: 99%

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

Setiawan¹,

Mudasir²,

Wijaya³

2020

Preprint

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A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R 2 = 0.95, q 2 = 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R 2 = 0.87, q 2 = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.

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“…Based on an arbitrary number of decision trees used as an ensemble with a majority vote to decide on the most probable class assigned to each data point, random forests (RF) are a popular classification algorithm often used with very good performance in QSAR models (35)(36)(37). Each decision tree is constructed using bootstrap sets of the training set and subsets of descriptors that are selected in a random manner (38).…”

Section: Machine Learning Algorithms and Model Buildingmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

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Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

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Imidazolium ionic liquids as effective antiseptics and disinfectants against drug resistant S. aureus: In silico and in vitro studies

Cited by 25 publications

References 59 publications

Isolation of a putative S-layer protein from anammox biofilm extracellular matrix using ionic liquid extraction

Isolation of a putative S-layer protein from anammox biofilm extracellular matrix using ionic liquid extraction

QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

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