QSAR models for describing the toxicological effects of ILs against Staphylococcus aureus based on norm indexes

In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity

The minimal inhibitory concentrations (pMIC) are a valuable measure of the biological activity of polypeptides. Numerical data on the pMIC are necessary to systematize knowledge on polypeptides' biochemical behaviour. The model of negative decimal logarithm of pMIC of polypeptides in the form of a mathematical function of a sequence of amino acids is suggested. The suggested model is based on the so-called correlation weights of amino acids together with the correlation weights of fragments of local symmetry (FLS). Three kinds of the FLS are considered: (i) three-symbol fragments '…xyx…', (ii) four-symbol fragments '…xyyx…', and (iii) five-symbol fragments '…xyzyx…'. The models built using the Monte Carlo technique improved by applying the index of ideality of correlation (IIC) and correlation intensity index (CII).

Two QSAR models for predicting the toxicity of chemicals towards Tetrahymena pyriformis based on topological-norm descriptors and spatial-norm descriptors

Quantitative structure-activity relationship (QSAR) is important for safe, rapid and effective risk assessment of chemicals. In this study, two QSAR models were established with 1230 chemicals to predict toxicity towards Tetrahymena pyriformis using multiple linear regression (MLR) method. The topological(T)-QSAR model was developed by using topological-norm descriptors generated from the topological structure, and the spatial(S)-QSAR model were built with spatial-norm descriptors obtained from the three-dimensional structure of molecules and topological-norm descriptors. The r²_training and r²_test are 0.8304 and 0.8338 for the T-QSAR model, and 0.8485 and 0.8585 for the S-QSAR model, which means that T-QSAR model and S-QSAR model can be used to predict toxicity quickly and accurately. In addition, we also conducted validation on the developed models. Satisfying validation results and statistical parameters demonstrated that QSAR models based on the topological-norm descriptors and spatial-norm descriptors proposed in this paper could be further utilized to estimate the toxicity of chemicals towards Tetrahymena pyriformis.

Review of the toxic effects of ionic liquids

Interest in ionic liquids (ILs), called green or designer solvents, has been increasing because of their excellent properties such as thermal stability and low vapor pressure; thus, they can replace harmful organic chemicals and help several industrial fields e.g., energy-storage materials production and biomaterial pretreatment. However, the claim that ILs are green solvents should be carefully considered from an environmental perspective. ILs, given their minimal vapor pressure, may not directly cause atmospheric pollution. However, they have the potential to cause adverse effects if leaked into the environment, for instance if they are spilled due to human mistakes or technical errors. To estimate the risks of ILs, numerous ILs have had their toxicity assessed toward several micro- and macro-organisms over the past few decades. Since the toxic effects of ILs depend on the method of estimating toxicity, it is necessary to briefly summarize and comprehensively discuss the biological effects of ILs according to their structure and toxicity testing levels. This can help simplify our understanding of the toxicity of ILs. Therefore, in this review, we discuss the key findings of toxicological information of ILs, collect some toxicity data of ILs to different species, and explain the influence of IL structure on their toxic properties. In the discussion, we estimated two different sensitivity values of toxicity testing levels depending on the experiment condition, which are theoretical magnitudes of the inherent sensitivity of toxicity testing levels in various conditions and their changes in biological response according to the change in IL structure. Finally, some perspectives, future research directions, and limitations to toxicological research of ILs, presented so far, are discussed.

Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures

Ionic liquids (ILs) have been regarded as "designer solvents" because of their satisfactory physicochemical properties. The 5% onset decomposition temperature (T _d,_5%onset) is one of the most conservative but reliable indicators for characterizing the possible fire hazard of engineered ILs. This study is devoted to develop a quantitative structure-property relationship model for predicting the T _d,_5%onset of binary imidazolium IL mixtures. Both in silico design and data analysis descriptors and norm index were employed to encode the structural characteristics of binary IL mixtures. The subset of optimal descriptors was screened by combining the genetic algorithm with the multiple linear regression method. The resulting optimal prediction model was a four-variable multiple linear equation, with the average absolute error (AAE) for the external test set being 12.673 K. The results of rigorous model validations also demonstrated satisfactory model robustness and predictivity. The present study would provide a new reliable approach for predicting the thermal stability of binary IL mixtures.

Norm index in QSTR work for predicting toxicity of ionic liquids on Vibrio fischeri

Ionic liquids have been becoming new 'green solvent' because of the low saturation vapor pressure, less volatilization and more recycling utilization. Since most ILs are soluble in water, it should be indispensable to evaluate the ecotoxicology effect of ILs on aquatic environment before using them widely. Based on the concept of norm index, a set of norm descriptors were proposed for anions, cations and ILs. The whole IL structure optimization method has been used to build a predictive norm index-based quantitative structure-toxicity relationship model for the toxicity of ILs on Vibrio fischeri. Statistical results indicated that norm descriptors were reliable and robust in expressing the relationship between structural information and toxicity of ILs. Meanwhile, a series of ILs without experimental values were predicted based on this stable QSTR model. The results indicated that for imidazole-based ILs, an increase in the length of substituent in the branch could enhance the toxicity of ILs on Vibrio fischeri, and the branch contains hydroxyl group, double bond or triple bonds might reduce the toxicity of ILs. Results obtained in this present work would be valuable for the molecular design and the toxicity evaluation toward aquatic organism of ILs.

Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics

The n-octanol/water partition coefficient (logK_ow) is widely used in the environmental, agricultural and pharmaceutical fields for the risk evaluation and application of organic chemicals. In this work, grounded on atomic distribution matrices, a norm index-based QSPR model was built for organic chemicals with 18 kinds of diverse structures. The statistical results (R² = 0.9037, RMSE = 0.4515) showed that the QSPR model for describing the logK_ow of organics was fitted well. Various validation results showed that the model had good robustness, good predictability and wide applicability. These satisfactory results indicated that the model was applicable for the logK_ow description of organic chemicals and that norm descriptors were reliable and general for the description of organic structures. The model was relatively better at describing logK_ow for aromatics, alcohols, nitriles, esters, amides, halogenated compounds, acids and amine compounds. The intensity of spatial branching and the space charge distribution intensity descriptors could have a greater impact on the logK_ow value of a compound.

Norm index for predicting the rate constants of organic contaminants oxygenated with sulfate radical

The degradation of organic contaminants in aquatic systems has raised immense attention worldwide, and the second-order rate constant ([Formula: see text]) of water pollutants oxidized by sulfate radical anion is an important index for assessing the degradation efficiency of organics. Herein, a new norm mathematical formula is defined. Based on this, four new descriptors are proposed and a QSPR model is developed for predicting [Formula: see text] using 30 families of emerging organic pollutants in water. The statistical results fully prove that this model has good fitting effect and stability with R² of 0.8862, Q²_LOO of 0.8466, and Q²_5-fold of 0.8329, respectively. The validation results including cross validation, applicability domain analysis, and model comparison show that this model has good robustness, predictive performance, and reliability. These decent results indicate that the new norm mathematical formula is effective in calculating descriptors and the norm indexes have a great application for evaluating the transformation fate of organic pollutants by sulfate radical in aquatic systems.

Application of general toxic effects of ionic liquids to predict toxicities of ionic liquids to Spodoptera frugiperda 9, Eisenia fetida, Caenorhabditis elegans, and Danio rerio

Modeling for the toxicity of ionic liquids (ILs) is necessary to fill data gaps for untested chemicals and to understand the relevant mechanisms at the molecular level. In order for many researchers to easily predict toxicity and/or develop some prediction model, simple method(s) based on a single parameter should be proposed. Therefore, previously our group developed a comprehensive toxicity prediction model with unified linear free-energy relationship descriptors to address the single parameter for predicting the toxicities, as follows (Cho et al., 2016b). Log 1/toxicity in the unit of mM= (2.254 E_c - 2.545 S_c + 0.646 A_c - 1.471 B_c + 1.650 V_c + 2.917 J⁺ - 0.201 E_a + 0.418 V_a + 0.131 J^-) - 0.709. It is considered that the model can calculate the general toxicological effect of ILs in parenthesis, as it was developed on the basis of numerous toxic effects i.e., 58 toxicity testing methods and about 1600 data points. In order to check the hypothesis, the values calculated by the model were correlated with four different datasets from insect cell line (Spodoptera frugiperda 9), earthworm (Eisenia fetida), nematode (Caenorhabditis elegans), and fish (Danio rerio). The results clearly showed that the calculated values are in good agreement with each dataset. In the case of S. frugiperda 9 cells, the calculated parameters were correlated with log1/LC₅₀ values, measured after 24 h and 48 h incubation, in R² of 0.67 and 0.88, respectively. The R² values for the earthworm, nematode, and fish were 0.88, 0.96, and 0.94-0.95, respectively. This study confirmed that the comprehensive model can be simply and accurately used to predict toxicity of ILs.

Norm index-based QSTR model to predict the eco-toxicity of ionic liquids towards Leukemia rat cell line

The evaluation of eco-toxicity of ionic liquids (ILs) in the aquatic environment is essential for their safe utilization and QSTR approach plays an important role in obtaining the eco-toxicity data of ILs with diverse structures. Usually, the descriptors used to build QSTR model were made up of anion and cation descriptors, and their interactions were often neglected to some extent. In this work, based on the optimization of the ILs structure, a new set of descriptors were proposed to describe the interaction between anions and cations, and some new atomic distribution matrices were constructed to calculate norm descriptors of ILs, anion and cation. A norm index-based QSTR model was built to predict the eco-toxicity of ILs toward Leukemia rat cell line (IPC-81). This model has satisfactory statistical results with the R² of 0.954 and RMSE of 0.241, respectively. Furthermore, leave-one-out cross-validation and applicability domain results showed good stability and predictability of this model. This approach showed that the interaction between cations and anions could be reflected by optimizing the whole structure of ILs which might play an important role for describing the eco-toxicity of ILs. Therefore, it is further suggested that the norm descriptors would be applicable to predict the eco-toxicity of ILs towards IPC-81.

Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method

This study describes in silico designing of aptamers against the influenza virus using Monte Carlo method. Aptamers are short, single-stranded oligonucleotides and these bind to an ample range of biologically important proteins which are related to many disease conditions. The affinities and specificities of aptamers are comparable to antibodies. In the medicinal chemistry, quantitative structure-activity relationship (QSAR) is an important skill which is used for drug design and development. To study the inhibitory activity of aptamers, we have developed QSAR models based on Monte Carlo method. The nucleobase sequence descriptors B_k, BB_k and BBB_k are used to generate the QSAR models. A number of statistical benchmarks together with index of ideality of correlation (IIC) is considered to validate the build QSAR models. Data set of 98 aptamers is divided into four random splits. The statistical criteria R² = 0.8711 and CCC = 0.9207 of the validation set of split 3 are best, so the build QSAR model of split 3 is the paramount model. The aptamer fragment responsible for the promotors of endpoint increase and decrease are also determined. These fragments are applied to design new nine aptamers from the lead aptamer APT01.Communicated by Ramaswamy H. Sarma.

Multiple toxicity endpoint-structure relationships for substituted phenols and anilines

Quantitative structure-toxicity relationship (QSTR) models with the same mathematical structure were proposed for predicting the multiple toxicity endpoints of substituted phenols and anilines towards Chlorella vulgaris (C. vulgaris) based on the norm indexes. Four aquatic toxicity endpoints including growth inhibition concentrations of IC₅₀, IC₂₀, LOEC and NOEC towards C. vulgaris were involved in the modeling work. The results indicated that the developed models could produce satisfactory predictive results for the four different toxicity endpoints with high squared correlation coefficients (R²). Leave-one-out cross validation, Y-randomized validation and application domain analysis demonstrated the accuracy, robustness and reliability of these models. Accordingly, the results obtained in this work suggested that it might be possible to develop QSTR models with the same mathematical structure for predicting multiple toxicity endpoints successfully via norm index descriptors.

QSAR model for predicting the toxicity of organic compounds to fathead minnow

In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R²) and squared relation coefficient of the cross validation (Q²) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.