Chemical-chemical Interaction Research Articles

Overexpression of l,d-Transpeptidase A Induces Dispensability of Rod Complex in Escherichia coli.

Antimicrobial resistance (AMR) is a significant global threat, and the presence of resistance-determinant genes is one of the major driving forces behind it. The bacterial rod complex is an essential set of proteins that is crucial for cell survival due to its role in cell wall biogenesis and shape maintenance. Therefore, these proteins offer excellent potential as drug targets; however, compensatory mutations in nontarget genes render this complex nonessential. The MreB protein of this complex is an actin homologue that rotates along the longitudinal axis of the cell to provide rod shape to the bacteria. In this study, using chemical-chemical interaction profiling and FtsZ suppression assay, we identified the MreB targeting activity of IITR07865, a previously discovered small molecule in our lab. Escherichia coli suppressors against IITR07865 revealed mutations in two cell division-associated genes, min C and pal, that have not been previously implicated in rod complex essentiality. IITR07865 resistant mutants were found to inactivate and render the rod complex nonessential, making the rod complex inhibitors ineffective. Further, through transcriptome analysis, we reveal the primary cause of resistance in suppressor strains to be the overexpression of an l, d-transpeptidase A enzyme, which is involved in peptidoglycan and Braun's lipoprotein cross-linking. Our results demonstrate a novel mechanism of resistance development in rod-shaped Gram-negative bacterial pathogen E. coli involved in UTIs where mecillinam, a clinically used antibiotic that targets rod complex, is a drug of choice.

Evaluating the MDCK cell permeability of greenly synthesize bimetallic Ag/Zn Nanoparticles using leaf extract of Vallaris solanacea as a potential antipesticide-resistant agent.

Bimetallic nanoparticles, particularly Ag/Zn bimetallic nanoparticles, have gained increasing attention due to their unique properties, making them suitable for a variety of applications such as catalysis, water treatment, and environmental remediation. This study aimed to elucidate the use of bimetallic nanoparticles of Ag/Zn as an alternative to resistant pesticides for pest control. Furthermore, this research demonstrates that BNPs can target specific pollutants and degrade them through various mechanisms. BNP docking with the Nilaparvata lugens cytochrome P450 (CYP6ER1) protein exhibited the lowest binding energy of-7.5 kcal/mol. The cell permeability analysis of BNP in plant cells reveals that the BNP has 0 % permeability towards any cell at-10 kcal/mol energy, which is the lowest free energy translocation pathway. The harmful leftover residues of the pesticides have a higher chance of degradability in case of interaction with BNP validated by chemical-chemical interaction analysis. Additionally, MDCK permeability coefficient of small molecules based on the regression model was calculated for BNP which authenticated the efficiency of BNP. Moreover, Swiss ADMET simulated absorption using a boiled egg model with no blood-brain barrier and gastrointestinal crossing for the expected BNP molecule has been observed. Significantly, the findings indicate that employing bimetallic nanoparticles like Ag/Zn is a crucial strategy for bioremediation because they proficiently decompose pesticides while posing no risk to humans. Our results will facilitate the design of novel BNPs materials for environmental remediation and pest control ensuring human health safety that are predicated on bimetallic nanoparticles.

Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring.

Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, the machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target. Previous works try to solve the cold start problem by learning the drug or target representation using unsupervised learning. While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction. To incorporate the interaction information into the drug and protein interaction, we proposed using transfer learning from chemical–chemical interaction (CCI) and protein–protein interaction (PPI) task to drug-target interaction task. The representation learned by CCI and PPI tasks can be transferred smoothly to the DTA task due to the similar nature of the tasks. The result on the DTA datasets shows that our proposed method has advantages compared to other pre-training methods in the DTA task.

Anatomical Therapeutic Chemical Classification (ATC) With Multi-Label Learners and Deep Features

Automatic anatomical therapeutic chemical (ATC) classification predicts an unknown compound's therapeutic and chemical characteristics. Predicting the organs/systems an unidentified compound will act on has the potential of expediting drug development and research. That a given compound can affect multiple organs/systems makes automatic ATC classification a complex problem. In this paper, the authors experimentally develop a multi-label ensemble for ATC prediction. The proposed approach extracts a 1D feature vector based on a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds, as defined by the ATC coding system. This 1D vector is reshaped into 2D matrices and fed into seven pre-trained convolutional neural networks (CNN). A bidirectional long short-term memory network (BiLSTM) is trained on the 1D vector. Features extracted from both deep learners are then trained on multi-label classifiers, with results fused. The best system proposed here is shown to outperform other methods reported in the literature.

Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy.

Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one of the essential problems in drug research. Designing efficient computational methods is an alternative way. Some studies paired the drug and side effect as a sample, thereby modeling the problem as a binary classification problem. However, the selection of negative samples is a key problem in this case. In this study, a novel negative sample selection strategy was designed for accessing high-quality negative samples. Such strategy applied the random walk with restart (RWR) algorithm on a chemical-chemical interaction network to select pairs of drugs and side effects, such that drugs were less likely to have corresponding side effects, as negative samples. Through several tests with a fixed feature extraction scheme and different machine-learning algorithms, models with selected negative samples produced high performance. The best model even yielded nearly perfect performance. These models had much higher performance than those without such strategy or with another selection strategy. Furthermore, it is not necessary to consider the balance of positive and negative samples under such a strategy.

ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method

Anatomical Therapeutic Chemical (ATC) classification system proposed by the World Health Organization is a widely accepted drug classification scheme in both academic and industrial realm. It is a multilabeling system which categorizes drugs into multiple classes according to their therapeutic, pharmacological, and chemical attributes. In this study, we adopted a data-driven network-based label space partition (NLSP) method for prediction of ATC classes of a given compound within the multilabel learning framework. The proposed method ATC-NLSP is trained on the similarity-based features such as chemical–chemical interaction and structural and fingerprint similarities of a compound to other compounds belonging to the different ATC categories. The NLSP method trains predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes the ensemble labels for a compound as final prediction. Experimental evaluation based on the jackknife test on the benchmark dataset demonstrated that our method has boosted the absolute true rate, which is the most stringent evaluation metrics in this study, from 0.6330 to 0.7497, in comparison to the state-of-the-art approaches. Moreover, the community structures of the label relation graph were detected through the label propagation method. The advantage of multilabel learning over the single-label models was shown by label-wise analysis. Our study indicated that the proposed method ATC-NLSP, which adopts ideas from network research community and captures the correlation of labels in a data driven manner, is the top-performing model in the ATC prediction task. We believed that the power of NLSP remains to be unleashed for the multilabel learning tasks in drug discovery. The source codes are freely available at https://github.com/dqwei-lab/ATC.

Convolutional Neural Networks for ATC Classification.

Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult. In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem. Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the "absolute true" rate and the "absolute false" rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.

Ethanol via Regulation of NF-κB/p53 Signaling Pathway Increases Manganese-Induced Inflammation and Apoptosis in Hypothalamus of Rats.

The diet is a major route of manganese (Mn) exposure for humans. Interestingly, several epidemiological data demonstrated an increase in the incidence of alcohol consumption globally. Chemical-chemical interaction subsequent to chemical mixtures exposure may result in a synergism or antagonism effects. The present study investigated the influence of co-exposure to ethanol (EtOH) and Mn on inflammation and apoptosis in the hypothalamus of rats. The study consisted of five groups of rats that were exposed to drinking water alone, EtOH alone at 5g/kg, Mn alone at 30mg/kg or co-expose with EtOH at 1.25 and 5g/kg body weight by oral gavage for 35 consecutive days. The results indicated that the significant (p < 0.05) increases in pro-inflammatory cytokines, namely tumor necrosis factor alpha (TNF-α) and interleukin 1 beta (IL-1β) as well as cyclooxygenase-2 (COX-2) and nuclear factor kappa B (NF-κB) activation in the hypothalamus following individual exposure to Mn and EtOH to rats were intensified in the co-exposure group. Moreover, immunohistochemistry analysis showed marked decrease in B cell lymphoma-2 (Bcl-2) protein expression as well as the increases in the apoptotic proteins, namely Bax and caspase-3 along with p53 in the hypothalamus of rats treated with Mn or EtOH alone were intensified in the co-exposure group. Taken together, these findings highlight that EtOH exacerbated the induction of inflammatory and apoptotic biomarkers via regulation of NF-κB/p53 signaling pathways in the hypothalamus of rats. These alterations may have profound disrupting effects on the hypothalamus functions such as impairment of it metabolic and autonomic nervous system functions.

End-to-End Representation Learning for Chemical-Chemical Interaction Prediction.

Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a learning-based approach. In other drug analyses, deep learning has been actively used in recent years. However, in most cases, deep learning has been used only for classification even though it has feature extraction capabilities. Thus, in this paper, we propose an end-to-end representation learning method for CCI, named DeepCCI, which includes feature extraction and a learning-based approach. Our proposed architecture is based on the Siamese network. Hidden representations are extracted from a simplified molecular input line entry system (SMILES), which is a string notation representing the chemical structure using weight-shared convolutional neural networks. Subsequently, L1 element-wise distances between the two extracted hidden representations are measured. The performance of DeepCCI is compared with those of 12 fingerprint-method combinations. The proposed DeepCCI shows the best performance in most of the evaluation metrics used. In addition, DeepCCI was experimentally validated to guarantee the commutative property. The automatically extracted features can alleviate the efforts required for manual feature engineering and improve prediction performance.

ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures

The assessment of bioactivity and toxicity for mixtures remains a challenging work. Although several computational models have been developed to accelerate the evaluation of chemical-chemical interaction, a specific biological endpoint should be defined before applying the models that usually relies on clinical and experimental data. The development of computational methods is desirable for identifying potential biological endpoints of mixture interactions. To facilitate the identification of potential effects of mixture interactions, a novel online system named ChemDIS-Mixture is proposed to analyze the shared target proteins, and common enriched functions, pathways, and diseases affected by multiple chemicals. Venn diagram tools have been implemented for easy analysis and visualization of interaction targets and effects. Case studies have been provided to demonstrate the capability of ChemDIS-Mixture for identifying potential effects of mixture interactions in clinical studies. ChemDIS-Mixture provides useful functions for the identification of potential effects of coexposure to multiple chemicals. ChemDIS-Mixture is freely accessible at http://cwtung.kmu.edu.tw/chemdis/mixture.

A computational method for the identification of candidate drugs for non-small cell lung cancer.

Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.

Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound.

Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological and chemical properties, thereby substantially expediting the pace of drug development. The problem is challenging because some drugs and compounds belong to two or more ATC classes, making machine learning extremely difficult. In this article a multi-label classifier system is proposed that incorporates information about a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes. The proposed system reshapes a 1D feature vector to obtain a 2D matrix representation of the compound. This matrix is then described by a histogram of gradients that is fed into a Multi-Label Learning with Label-Specific Features classifier. Rigorous cross-validations demonstrate the superior prediction quality of this method compared with other state-of-the-art approaches developed for this problem, a superiority that is reflected particularly in the absolute true rate, the most important and harshest metric for assessing multi-label systems. The MATLAB code for replicating the experiments presented in this article is available at https://www.dropbox.com/s/7v1mey48tl9bfgz/ToolPaperATC.rar?dl=0 . loris.nanni@unipd.it. Supplementary data are available at Bioinformatics online.

The Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung Cancer

Lung cancer causes over one million deaths every year worldwide. However, prevention and treatment methods for this serious disease are limited. The identification of new chemicals related to lung cancer may aid in disease prevention and the design of more effective treatments. This study employed a weighted network, constructed using chemical-chemical interaction information, to identify new chemicals related to two types of lung cancer: non-small lung cancer and small-cell lung cancer. Then, a randomization test as well as chemical-chemical interaction and chemical structure information were utilized to make further selections. A final analysis of these new chemicals in the context of the current literature indicates that several chemicals are strongly linked to lung cancer.

Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions

Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.

Chemical Probes of Escherichia coli Uncovered through Chemical-Chemical Interaction Profiling with Compounds of Known Biological Activity

While cell-based screens have considerable power in identifying new chemical probes of biological systems and leads for new drugs, a major challenge to the utility of such compounds is in connecting phenotype with a cellular target. Here, we present a systematic study to elucidate the mechanism of action of uncharacterized inhibitors of the growth of Escherichia coli through careful analyses of interactions with compounds of known biological activity. We studied growth inhibition with a collection of 200 antibacterial compounds when systematically combined with a panel of 14 known antibiotics of diverse mechanism and chemical class. Our work revealed a high frequency of synergistic chemical-chemical interactions where the interaction profiles were unique to the various compound pairs. Thus, the work revealed that chemical-chemical interaction data provides a fingerprint of biological activity and testable hypotheses regarding the mechanism of action of the novel bioactive molecules. In the study reported here, we determined the mode of action of an inhibitor of folate biosynthesis and a DNA gyrase inhibitor. Moreover, we identified eight membrane-active compounds, found to be promiscuously synergistic with known bioactives.

Use of tissue slices in chemical mixture toxicology and interspecies investigations

Precision-cut tissue slices have proven to be a useful in vitro system for biotransformation and toxicity studies. Since tissue slices can be readily prepared from a variety of tissues and species, they can easily be used for interspecies investigations and comparisons. Furthermore, slices can be readily prepared from human tissue, thus comparisons (extrapolation) can be made between laboratory animals and humans. Slices can also be used to examine the toxic interactions of chemicals in vitro. It is important to use the correct experimental design to demonstrate toxic interactions and to assure that the tissue slices are properly exposed to the chemicals. Overall, tissue slices offer a valid in vitro system for performing species comparisons and chemical-chemical interaction studies.

The peroxidase-dependent activation of butylated hydroxyanisole and butylated hydroxytoluene (BHT) to reactive intermediates: Formation of BHT-quinone methide via a chemical-chemical interaction

The food antioxidants butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT) are shown to be metabolized to covalent binding intermediates and various other metabolites by prostaglandin H synthase and horseradish peroxidase. BHA was extensively metabolized by horseradish peroxidase (80% conversion of parent BHA into metabolites) resulting in the formation of three dimeric products. Only two of these dimers were observed in prostaglandin H synthase-catalyzed reactions. In contrast to BHA, BHT proved to be a relatively poor substrate for prostaglandin synthase and horseradish peroxidase, resulting in the formation of a small amount of polar and aqueous metabolites (23% conversion of parent BHT into metabolites). With arachidonic acid as the substrate, prostaglandin H synthase catalyzed the covalent binding of [14C]BHA and [14C]BHT to microsomal protein which was significantly inhibited by indomethacin and glutathione. The covalent binding of BHA and its metabolism to dimeric products were also inhibited by BHT. In contrast, the addition of BHA enhanced the covalent binding of BHT by 400%. Moreover, in the presence of BHA, the formation of the polar and aqueous metabolites of BHT was increased and two additional metabolites, BHT-quinone methide and stilbenequinone, were detected. The increased peroxidase-dependent oxidation of BHT in the presence of BHA is proposed to occur via the direct chemical interaction of BHA phenoxyl radical with BHT or BHT phenoxyl radical. These results suggest a potential role for phenoxyl radicals in the activation of xenobiotic chemicals to toxic metabolites.