Capturing antibacterial natural products with in silico techniques

ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques

BACKGROUND OBJECTIVES

Discovery of new antibiotics is the need of the hour to treat infectious diseases. An ever-increasing repertoire of multidrug-resistant pathogens poses an imminent threat to human lives across the globe. However, the low success rate of the existing approaches and technologies for antibiotic discovery remains a major bottleneck. In silico methods like machine learning (ML) deem more promising to meet the above challenges compared with the conventional experimental approaches. The goal of this study was to create ML models that may be used to successfully predict new antimicrobial compounds.

METHODS

In this article, we employed eight different ML algorithms namely, extreme gradient boosting, random forest, gradient boosting classifier, deep neural network, support vector machine, multilayer perceptron, decision tree, and logistic regression. These models were trained using a dataset comprising 312 antibiotic drugs and a negative set of 936 non-antibiotic drugs in a five-fold cross validation approach.

RESULTS

The top four ML classifiers (extreme gradient boosting, random forest, gradient boosting classifier and deep neural network) were able to achieve an accuracy of 80 per cent and above during the evaluation of testing and blind datasets.

INTERPRETATION CONCLUSIONS

We aggregated the top performing four models through a soft-voting technique to develop an ensemble-based ML method and incorporated it into a freely accessible online prediction server named ABDpred ( http://clinicalmedicinessd.com.in/abdpred/ ).

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

In Vitro Activity of Essential Oils Distilled from Colombian Plants against Candidaauris and Other Candida Species with Different Antifungal Susceptibility Profiles

Multi-drug resistant species such as Candida auris are a global health threat. This scenario has highlighted the need to search for antifungal alternatives. Essential oils (EOs), or some of their major compounds, could be a source of new antifungal molecules. The aim of this study was to evaluate the in vitro activity of EOs and some terpenes against C. auris and other Candida spp. The eleven EOs evaluated were obtained by hydro-distillation from different Colombian plants and the terpenes were purchased. EO chemical compositions were obtained by gas chromatography/mass spectrometry (GC/MS). Antifungal activity was evaluated following the CLSI standard M27, 4th Edition. Cytotoxicity was tested on the HaCaT cell line and fungal growth kinetics were tested by time–kill assays. Candida spp. showed different susceptibility to antifungals and the activity of EOs and terpenes was strain-dependent. The Lippia origanoides (thymol + p-cymene) chemotype EO, thymol, carvacrol, and limonene were the most active, mainly against drug-resistant strains. The most active EOs and terpenes were also slightly cytotoxic on the HaCaT cells. The findings of this study suggest that some EOs and commercial terpenes can be a source for the development of new anti-Candida products and aid the identification of new antifungal targets or action mechanisms.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

Antimicrobial Activity of Dihydroisocoumarin Isolated from Wadi Lajab Sediment-Derived Fungus Penicillium chrysogenum: In Vitro and In Silico Study

Antibiotic resistance is considered a major health concern globally. It is a fact that the clinical need for new antibiotics was not achieved until now. One of the most commonly prescribed classes of antibiotics is β-Lactam antibiotics. However, most bacteria have developed resistance against β-Lactams by producing enzymes β-Lactamase or penicillinase. The discovery of new β-Lactamase inhibitors as new antibiotics or antibiotic adjuvants is essential to avoid future catastrophic pandemics. In this study, five dihydroisocoumarin: 6-methoxy mellein (1); 5,6-dihydroxymellein (2); 6-hydroxymellein (3); 4-chloro-6-hydroxymellein (4) and 4-chloro-5,6-di-hydroxymellein (5) were isolated from Wadi Lajab sediment-derived fungus Penicillium chrysogenum, located 15 km northwest of Jazan, KSA. The elucidation of the chemical structures of the isolated compounds was performed by analysis of their NMR, MS. Compounds 1–5 were tested for antibacterial activities against Gram-positive and Gram-negative bacteria. All of the compounds exhibited selective antibacterial activity against Gram-positive bacteria Staphylococcus aureus and Bacillus licheniformis except compound 3. The chloro-dihydroisocoumarin derivative, compound 4, showed potential antimicrobial activities against all of the tested strains with the MIC value between 0.8–5.3 μg/mL followed by compound 5, which exhibited a moderate inhibitory effect. Molecular docking data showed good affinity with the isolated compounds to β-Lactamase enzymes of bacteria; NDM-1, CTX-M, OXA-48. This work provides an effective strategy for compounds to inhibit bacterial growth or overcome bacterial resistance.

Machine Learning in Antibacterial Drug Design

Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Forty-One Plant Extracts Screened for Dual Antidiabetic and Antioxidant Functions: Evaluating the Types of Correlation between -Amylase Inhibition and Free Radical Scavenging

Dysregulation of glucose homeostasis followed by chronic hyperglycemia is a hallmark of diabetes mellitus (DM), a disease spreading as a worldwide pandemic for which there is no satisfactory dietary treatment or cure. The development of glucose-controlling drugs that can prevent complications of DM, such as hyperglycemia and oxidative stress, which contribute to the impairment of the key physiological processes in the body, is of grave importance. In pursuit of this goal, this study screened 41 plant extracts for their antidiabetic and antioxidant activities by employing assays to test for α-amylase inhibition and free radical scavenging activity (FRSA) and by measuring glucose uptake in L6-GLUT4myc cells. While extracts of Rhus coriaria, Punica granatum, Olea europaea, Pelargonium spp., Stevia rebaudiana, and Petroselinum crispum demonstrated significant α-amylase inhibition, the extracts of Rhus coriaria and Pelargonium spp. also demonstrated increased FRSA, and the extract of Rhus coriaria stimulated glucose uptake. These natural extracts, which are believed to have fewer side effects because they are prepared from edible plants, interfere with the process in the small intestine that breaks down dietary carbohydrates into monosaccharide and disaccharide derivatives, and thereby suppress increases in diet-induced blood glucose; hence, they may have clinical value for type 2 diabetes management. The Pelargonium spp. and Rhus coriaria extracts demonstrated the highest antidiabetic and antioxidant activities. Both plants may offer valuable medical benefits, especially because they can be taken as dietary supplements by patients with diabetes and can serve as sources of new, natural-based antidiabetic drug candidates. The enhancement of cellular glucose uptake stimulated by Rhus coriaria extract could lead to the development of clinical applications that regulate blood glucose levels from within the circulatory system. Isolating bioactive substances from these plant extracts and testing them in diabetic mice will significantly advance the development of natural drugs that have both antidiabetic and free radical-scavenging properties, likely with lesser side effects.

Lauryl Gallate Activity and Streptococcus mutans: Its Effects on Biofilm Formation, Acidogenicity and Gene Expression

Streptococcus mutans bacterium is implicated in the pathogenesis of dental caries due to the production of biofilm and organic acids from dietary sucrose. Despite the availability of various means of prophylaxis, caries still has a high worldwide prevalence. Therefore, it is important to find new pharmaceuticals to inhibit S. mutans biofilm formation and acidogenicity. The aim of the current study was to evaluate the activity of lauryl gallate (dodecyl gallate) against S. mutans acidogenicity, the expression of biofilm-associated genes, and biofilm development on solid surfaces (polystyrene, glass). The biofilm quantities produced by S. mutans bacteria were assessed using colorimetric and optical profilometry techniques. Acidogenicity was evaluated by measuring the pH of the biofilm growth medium with microelectrode. Assessment of the expression of gene coding for glucan-binding protein B (gbpB), glucosyltranferases B, -C, -D (gtfB, -C, -D), and the F-ATPase β subunit of F₁ protein (atpD) was carried out using a quantitative reverse transcription-polymerase chain reaction (RT-qPCR). The results demonstrate the capacity of lauryl gallate to significantly inhibit S. mutans acidogenicity and biofilm development on solid surfaces, in a dose-dependent manner, compared to untreated bacteria (p < 0.05). The highest activity of lauryl gallate occurred at a concentration of 98.98 µM, at which it suppressed biofilm formation by 100% and lowered pH levels by 98%. The effect of lauryl gallate treatment on gene expression changes, as demonstrated by our RT-qPCR data, was limited to the gtfD gene only, was a significant (48%) decrease in gene expression, obtained for the biofilm-producing bacteria, while a 300% increase in fold change for the same gene occurred in the planktonic cells. It is important to note that in previous studies we showed a broader effect of related derivatives. However, a similar magnitude of difference in effects between biofilm and planktonic cells for the atpD gene was obtained after treatment with octyl gallate and reverse magnitude for the same gene after treatment with ethyl gallate. Therefore, to ascertain the possible direct or indirect effects of lauryl gallate, as well as octyl gallate and ethyl gallate, more research is needed to examine the effects on the amount of enzymes and on the enzymatic activity of the products of the affected genes that are involved in the production and maintenance of biofilm by S. mutans.

Correlation between Antibacterial Activity and Free-Radical Scavenging: In-Vitro Evaluation of Polar/Non-Polar Extracts from 25 Plants

Objectives: The current study aimed to measure the antioxidant and antibacterial activities of 25 wild Palestinian edible plants, which were subjected to extraction by polar and non-polar solvents. Correlations between free radical scavenging activity and antibacterial activity of the extracts were assessed for both polar and non-polar fractions. Materials: Twenty-five wild edible plant species that are frequently consumed by people in Palestine (mainly in a rural area) were examined. Among them, 10 plant species were among those with the highest mean cultural importance values, according to an ethnobotanical survey that was conducted in the West Bank, Palestine, a few years ago. Method: The protocol of the DPPH assay for testing free-radical scavenging was utilized for determining EC50 values, while microdilution tests were conducted to determine the 50% inhibitory concentration (IC50) of the extracts for the microorganism Staphylococcus mutans. Results and Discussion: Eight extracts (non-polar fractions) were found to possess an antibacterial IC50 of less than 20 ppm, such as Foeniculum vulgare, Salvia palaestinafruticose, Micromeria fruticose, Trigonella foenum-graecum, Cichorium pumilum jacq, Salvia hierosolymitana boiss, Ruta chalepensis, and Chrysanthemum coronarium. The polar fractions possess higher antioxidant activity, while non-polar fraction possess higher antibacterial activity. Looking at all the results together can deceive and lead to the conclusion that there is no correlation between antibacterial activity against S. mutans and free radical scavenging (R2 equals 0.0538). However, in-depth analysis revealed that non-polar plant extracts with an EC50 of free radical scavenging ≤100 ppm have a four-fold order of enrichment toward more activity against S. mutans. These findings are of high importance for screening projects. A four-fold order of enrichment could save plenty of time and many in screening projects. The antibacterial active extracts marked by low-medium free radical scavenging might act through a mechanism of action other than that of highly active, free radical scavenging extracts. Conclusion: The screening of antioxidant and antimicrobial activity performed on 25 selected wild plant extracts revealed a satisfactory free radical scavenging and antimicrobial potential that could be of value in the management of oxidative stress. Further studies are recommended to explore novel and highly active natural antibacterial products.

Correlation between cytotoxicity in cancer cells and free radical-scavenging activity: In vitro evaluation of 57 medicinal and edible plant extracts

Cancer is a complex interaction among multiple signaling pathways involving a variety of target molecules. Cancer causes morbidity and mortality in millions of people worldwide, and due to its prevalence, the discovery of novel anticancer drugs is urgently required. Nature is considered an important source of the discovery of anticancer treatments, and many of the cytotoxic medicines in clinics today are derived from plants and other natural sources. Reactive oxygen species (ROS) induce a variety of human cancers, and antioxidants or scavengers are used to counteract them. The current study reports on the screening of extracts from 57 plants that are used in the galilee district as a food and/or for traditional medicine. Investigating the free radical scavenging capacity and these plants, and their cytotoxicity, may prove helpful to high-throughput screening projects that use antioxidants and cytotoxic natural products. The current study assessed the correlation between free radical scavenging and cytotoxicity. Correlational analysis is important for increasing the efficiency of the screening process. In the present study, free radical scavenging was assessed using a DPPH assay, while cytotoxicity was measured using a XTT assay. A total of 9 extracts were indicated to exhibit EC₅₀ values <250 µg/ml, and 4 others exhibited a high antioxidant content, with EC₅₀ values, for free radical scavenging, of <0.5 µg/ml. An in-depth analysis of the results revealed that the extracts of plants that exhibit an EC₅₀ of free radical scavenging ≤10 µg/ml show a degree of enrichment toward increased cytotoxicity. It is recommended that future studies test the validity of the conclusions of the current study on other cancer cell-lines, and isolate and identify the bioactive agents that are found in the most cytotoxic extracts of plants.

Suppressive Effects of Octyl Gallate on Streptococcus mutans Biofilm Formation, Acidogenicity, and Gene Expression

The accumulation of biofilm by Streptococcus mutans bacteria on hard tooth tissues leads to dental caries, which remains one of the most prevalent oral diseases. Hence, the development of new antibiofilm agents is of critical importance. The current study reports the results from testing the effectiveness of octyl gallate (C8-OG) against: (1) S. mutans biofilm formation on solid surfaces (polystyrene, glass), (2) acidogenicity, (3) and the expression of biofilm-related genes. The amount of biofilm formed by S. mutans bacteria was evaluated using the colorimetric method and optical profilometry. The pH of the biofilm growth medium was measured with microelectrode. A quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was used to assess the expression of genes encoding glucan binding protein B (gbpB), glucosyltransferases B, -C, -D (gtfB, -C, -D), and the F-ATPase β subunit of the F₁ protein (atpD). The results show that C8-OG significantly diminished biofilm formation by exposed S. mutans on solid surfaces and suppressed acidogenicity in a dose-dependent manner, compared to unexposed bacteria (p < 0.05). The C8-OG concentration of 100.24 µM inhibited S. mutans biofilm development on solid surfaces by 100% and prevented a decrease in pH levels by 99%. In addition, the RT-qPCR data demonstrate that the biofilm-producing bacteria treated with C8-OG underwent a significant reduction in gene expression in the case of the four genes under study (gbpB, gtfC, gtfD, and atpD), and there was a slight decrease in expression of the gtfB gene. However, C8-OG treatments did not produce significant expression change compared to the control for the planktonic cells, although there was a significant increase for the atpD gene. Therefore, C8-OG might be a potent antibiofilm and/or anticaries agent for oral formulations that aim to reduce the prevalence of dental caries.

Identification of Novel Antibacterials Using Machine Learning Techniques

Many pharmaceutical companies are avoiding the development of novel antibacterials due to a range of rational reasons and the high risk of failure. However, there is an urgent need for novel antibiotics especially against resistant bacterial strains. Available in silico models suffer from many drawbacks and, therefore, are not applicable for scoring novel molecules with high structural diversity by their antibacterial potency. Considering this, the overall aim of this study was to develop an efficient in silico model able to find compounds that have plenty of chances to exhibit antibacterial activity. Based on a proprietary screening campaign, we have accumulated a representative dataset of more than 140,000 molecules with antibacterial activity against Escherichia coli assessed in the same assay and under the same conditions. This intriguing set has no analogue in the scientific literature. We applied six in silico techniques to mine these data. For external validation, we used 5,000 compounds with low similarity towards training samples. The antibacterial activity of the selected molecules against E. coli was assessed using a comprehensive biological study. Kohonen-based nonlinear mapping was used for the first time and provided the best predictive power (av. 75.5%). Several compounds showed an outstanding antibacterial potency and were identified as translation machinery inhibitors in vitro and in vivo. For the best compounds, MIC and CC50 values were determined to allow us to estimate a selectivity index (SI). Many active compounds have a robust IP position.

Phytochemical Composition and Biological Activities of Wild Scolymus maculatus L

Background: The wild population of spotted golden thistle, Scolymus maculatus, which belongs to the Compositae family, is believed to be one of the multi-curative wild plants mentioned in Flora Palaestina. This study aims to disclose the phytochemical composition, antioxidant potential, and antimicrobial activity of wild S. maculatus collected from the farms of Kabul, a village in northwest Galilee, for the first time. Methods: The phytochemical components of crude S. maculatus extracts from methanol, ethyl acetate, and n-hexane solvents were separated and identified using gas chromatography-mass spectrometry (GC-MS) in the electron impact (EI) mode. The free radical scavenging of the plant extracts was measured by DPPH assay. The microdilution test was used to determine the minimum inhibitory concentrations (MICs) of different S. maculatus extracts and to evaluate their antimicrobial activities. Results: Thirty-two phytochemicals were found in S. maculatus extracts including stigmasterol, γ-sitosterol, lupeol, lupeol acetate, and β-amyrin. Phytochemicals, such as 2-linoleoylglycerol, γ-sitosterol, β-amyrin, lupeol, (3α)-12-oleanen-3-yl acetate, and lupenyl acetate, were found to dominate the methanol extract. Most of these compounds were also observed in ethyl acetate and n-hexane extracts, but at different levels, in addition to some other minor compounds. The various extracts were investigated for their antioxidant and antimicrobial activity. The ethanolic and the methanolic extracts were shown to exhibit the highest free radical scavenging by DPPH assay with a half-maximally effective concentration (EC₅₀) of 0.37 and 0.65 mg/mL respectively, while the other three extracts (aqueous, ethyl acetate and n-hexane) were less active and their EC₅₀ (effective concentration at which DPPH radical was scavenged by 50%) were above 1.0 mg/mL. Moreover, MICs were determined to be effective against Staphylococcus aureus, Salmonella typhimurium, and Candida albicans microorganisms. Ethyl acetate and the ethanolic extracts are active against the three types of microorganisms at a minimum inhibitory concentration (MIC) of 0.5 mg/mL, while aqueous and the n-hexane extracts are inactive against Salmonella typhimurium. Conclusions: The results show that S. maculatus extracts are a rich source of compounds that can play an important role in human health, and in a broader context, in the treatment of various diseases, such antimicrobial and antioxidant-related ailments.