On finding natural antibiotics based on TCM formulae.

CONTEXT: Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates. OBJECTIVE: This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design. METHOD: A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task. RESULTS: The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.

Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds.

The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types of medicinal plants to treat various diseases. Research on herbal medicine usually focuses on insight into the composition of plants used as ingredients. However, in the present study, we extended to the level of metabolites that exist in the medicinal plants. This study aimed to develop a predictive model of the Unani therapeutic usage based on its constituent metabolites using deep learning and data-intensive science approaches. Furthermore, the best prediction model was then utilized to extract important metabolites for each therapeutic usage of Unani. In this study, it was observed that the deep neural network approach provided a much better prediction model than other algorithms including random forest and support vector machine. Moreover, according to the best prediction model using the deep neural network, we identified 118 important metabolites for nine therapeutic usages of Unani.

Prediction of Potential Natural Antibiotics Plants Based on Jamu Formula Using Random Forest Classifier.

Jamu is the traditional Indonesian herbal medicine system that is considered to have many benefits such as serving as a cure for diseases or maintaining sound health. A Jamu medicine is generally made from a mixture of several herbs. Natural antibiotics can provide a way to handle the problem of antibiotic resistance. This research aims to discover the potential of herbal plants as natural antibiotic candidates based on a machine learning approach. Our input data consists of a list of herbal formulas with plants as their constituents. The target class corresponds to bacterial diseases that can be cured by herbal formulas. The best model has been observed by implementing the Random Forest (RF) algorithm. For 10-fold cross-validations, the maximum accuracy, recall, and precision are 91.10%, 91.10%, and 90.54% with standard deviations 1.05, 1.05, and 1.48, respectively, which imply that the model obtained is good and robust. This study has shown that 14 plants can be potentially used as natural antibiotic candidates. Furthermore, according to scientific journals, 10 of the 14 selected plants have direct or indirect antibacterial activity.

Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach.

BACKGROUND: We performed in silico prediction of the interactions between compounds of Jamu herbs and human proteins by utilizing data-intensive science and machine learning methods. Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. METHODS: Initially, data related to compounds, target proteins, and interactions between them were collected from open access databases. Compounds are represented by molecular fingerprints, whereas amino acid sequences are represented by numerical protein descriptors. Then, prediction models that predict the interactions between compounds and target proteins were constructed using support vector machine and random forest. RESULTS: A random forest model constructed based on MACCS fingerprint and amino acid composition obtained the highest accuracy. We used the best model to predict target proteins for 94 important Jamu compounds and assessed the results by supporting evidence from published literature and other sources. There are 27 compounds that can be validated by professional doctors, and those compounds belong to seven efficacy groups. CONCLUSION: By comparing the efficacy of predicted compounds and the relations of the targeted proteins with diseases, we found that some compounds might be considered as drug candidates.

Oviposition inhibitor in umbelliferous medicinal plants for the common yellow swallowtail (Papilio machaon).

Umbelliferous medicinal plants, such as Angelica acutiloba Kitagawa and Angelica dahurica Bentham et Hooker filius ex Franchet et Savatier, account for a large percentage of crude drug consumption in Japan. The most serious problem in the cultivation of umbelliferous medicinal plants is the feeding damage caused by the common yellow swallowtail (Papilio machaon hippocrates C. & R. Felder, 1864). When we compared the numbers of eggs laid by P. machaon on six umbelliferous medicinal plants, the eggs on A. acutiloba, A. dahurica, and Glehnia littoralis Fr. Schmidt ex Miquel were the most numerous, those on Saposhnikovia divaricata Schischkin and Cnidium officinale Makino were rare, and Bupleurum falcatum Linné was not oviposited at all. To identify oviposition inhibitors for P. machaon in B. falcatum, S. divaricata, and C. officinale, the volatile chemical constituents of these umbelliferous medicinal plants were compared with GC-MS. We carried out multivariate analysis of gas chromatographic data and concluded that germacrene D, α-humulene, and trans-caryophyllene play important roles in protecting plants from oviposition by P. machaon. Their oviposition repellent activity was confirmed by the fact that the number of eggs laid on the leaves around a repellent device containing a mixture of germacrene D, α-humulene, and trans-caryophyllene was reduced by 40% compared to a control.

Analysis of chemical properties of edible and medicinal ginger by metabolomics approach.

In traditional herbal medicine, comprehensive understanding of bioactive constituent is important in order to analyze its true medicinal function. We investigated the chemical properties of medicinal and edible ginger cultivars using a liquid-chromatography mass spectrometry (LC-MS) approach. Our PCA results indicate the importance of acetylated derivatives of gingerol, not gingerol or shogaol, as the medicinal indicator. A newly developed ginger cultivar, Z. officinale cv. Ogawa Umare or "Ogawa Umare" (OG), contains more active ingredients, showing properties as a new resource for the production of herbal medicines derived from ginger in terms of its chemical constituents and rhizome yield.

Metabolomic characterization of a low phytic acid and high anti-oxidative cultivar of turmeric.

Turmeric, the rhizome of Curcuma longa, has a long history of use as a spice and also as a traditional medicine in many Asian countries. To reveal unique morphological features of a newly registered Curcuma cultivar, C. longa cv. Okinawa Ougon (Ougon), non-targeted LC-MS and GC-MS analyses were conducted. The analysis revealed its distinctive chemical properties: lower amount of phytic acid and inorganic metals such as Fe, Mn, and Al, as well as higher concentrations of reduced derivatives of curcuminoids, such as dihydrobisdemethoxycurcumin, tetrahydrobisdemethoxycurcumin, dihydrodemethoxycurcumin, and tetrahydrodemethoxycurcumin. In addition, germacrane-type sesquiterpenes were almost absent although α-humulene and ß-caryophyllene, generated by the same biosynthetic route, were present. Presumably the alternation of the metal ion content, serving as a cofactor of sesquiterpene synthase, modulates the resulting variation of the sesquiterpenes. In summary, the cultivar Ougon is considered a promising candidate for functional food additives.

Supervised clustering based on DPClusO: prediction of plant-disease relations using Jamu formulas of KNApSAcK database.

Indonesia has the largest medicinal plant species in the world and these plants are used as Jamu medicines. Jamu medicines are popular traditional medicines from Indonesia and we need to systemize the formulation of Jamu and develop basic scientific principles of Jamu to meet the requirement of Indonesian Healthcare System. We propose a new approach to predict the relation between plant and disease using network analysis and supervised clustering. At the preliminary step, we assigned 3138 Jamu formulas to 116 diseases of International Classification of Diseases (ver. 10) which belong to 18 classes of disease from National Center for Biotechnology Information. The correlation measures between Jamu pairs were determined based on their ingredient similarity. Networks are constructed and analyzed by selecting highly correlated Jamu pairs. Clusters were then generated by using the network clustering algorithm DPClusO. By using matching score of a cluster, the dominant disease and high frequency plant associated to the cluster are determined. The plant to disease relations predicted by our method were evaluated in the context of previously published results and were found to produce around 90% successful predictions.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology.

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.

Transcriptome analysis of parallel-evolved Escherichia coli strains under ethanol stress.

BACKGROUND: Understanding ethanol tolerance in microorganisms is important for the improvement of bioethanol production. Hence, we performed parallel-evolution experiments using Escherichia coli cells under ethanol stress to determine the phenotypic changes necessary for ethanol tolerance. RESULTS: After cultivation of 1,000 generations under 5% ethanol stress, we obtained 6 ethanol-tolerant strains that showed an approximately 2-fold increase in their specific growth rate in comparison with their ancestor. Expression analysis using microarrays revealed that common expression changes occurred during the adaptive evolution to the ethanol stress environment. Biosynthetic pathways of amino acids, including tryptophan, histidine, and branched-chain amino acids, were commonly up-regulated in the tolerant strains, suggesting that activating these pathways is involved in the development of ethanol tolerance. In support of this hypothesis, supplementation of isoleucine, tryptophan, and histidine to the culture medium increased the specific growth rate under ethanol stress. Furthermore, genes related to iron ion metabolism were commonly up-regulated in the tolerant strains, which suggests the change in intracellular redox state during adaptive evolution. CONCLUSIONS: The common phenotypic changes in the ethanol-tolerant strains we identified could provide a fundamental basis for designing ethanol-tolerant strains for industrial purposes.