Functional Characterization and Structural Basis of an Efficient Di-C-glycosyltransferase from Glycyrrhiza glabra.

A highly efficient di-C-glycosyltransferase GgCGT was discovered from the medicinal plant Glycyrrhiza glabra. GgCGT catalyzes a two-step di-C-glycosylation of flopropione-containing substrates with conversion rates of >98%. To elucidate the catalytic mechanisms of GgCGT, we solved its crystal structures in complex with UDP-Glc, UDP-Gal, UDP/phloretin, and UDP/nothofagin, respectively. Structural analysis revealed that the sugar donor selectivity was controlled by the hydrogen-bond interactions of sugar hydroxyl groups with D390 and other key residues. The di-C-glycosylation capability of GgCGT was attributed to a spacious substrate-binding tunnel, and the G389K mutation could switch di- to mono-C-glycosylation. GgCGT is the first di-C-glycosyltransferase with a crystal structure, and the first C-glycosyltransferase with a complex structure containing a sugar acceptor. This work could benefit the development of efficient biocatalysts to synthesize C-glycosides with medicinal potential.

ETCM: an encyclopaedia of traditional Chinese medicine.

Traditional Chinese medicine (TCM) is not only an effective solution for primary health care, but also a great resource for drug innovation and discovery. To meet the increasing needs for TCM-related data resources, we developed ETCM, an Encyclopedia of Traditional Chinese Medicine. ETCM includes comprehensive and standardized information for the commonly used herbs and formulas of TCM, as well as their ingredients. The herb basic property and quality control standard, formula composition, ingredient drug-likeness, as well as many other information provided by ETCM can serve as a convenient resource for users to obtain thorough information about a herb or a formula. To facilitate functional and mechanistic studies of TCM, ETCM provides predicted target genes of TCM ingredients, herbs, and formulas, according to the chemical fingerprint similarity between TCM ingredients and known drugs. A systematic analysis function is also developed in ETCM, which allows users to explore the relationships or build networks among TCM herbs, formulas,ingredients, gene targets, and related pathways or diseases. ETCM is freely accessible at http://www.nrc.ac.cn:9090/ETCM/. We expect ETCM to develop into a major data warehouse for TCM and to promote TCM related researches and drug development in the future.

[Analysis of traditional Chinese medicine prescriptions based on support vector machine and analytic hierarchy process].

Combined use of drugs is a hot spot in the research of new drugs nowadays, and traditional Chinese medicine (TCM) is a classic practice in the combined use of drugs. In this paper, the compatibility of TCM prescriptions and the related properties of composed herbs were calculated and studied to verify and discuss the feasibility of the results in guiding compatibility. Research Group on New Drug Design, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences had established a structured database of TCM prescriptions by using traditional Chinese medicine inheritance support system (TCMISS V2.0), including 4 012 prescription compatibilities, 2 072 drug components, 381 kinds of TCM diseases, 316 kinds of TCM syndromes and 26 kinds of drug properties. On the basis of the created database above, Support Vector Machine (SVM) was used to analyze the prescription compatibility data and establish a model for predicting feasibility of drug compatibilities. Analytic Hierarchy Process (AHP) and cluster analysis were used to study the influence of drug properties in the rationality of prescription compatibility. The computational results showed that the accuracy in efficacy prediction of two data sets, i.e. prescription-disease and prescription-syndrome, was up to 90% in the linear SVM model. The macro₋averaging and micro₋averaging of the two models were around 0.92, 0.46, respectively. After AHP mapping, most of the incompatible combinations showed significant difference with other drug combinations during the clustering process in the vertical icicle, indicating that the proper machine learning algorithm can be used to lay the foundation for further exploring the combination rules in TCM and establishing more detailed drug-disease and syndrome predicting models, and provide theoretical guidance for the study of the combined use of drugs to a certain degree.

[Exploiture and application of an internet-based Computation Platform for Integrative Pharmacology of Traditional Chinese Medicine].

Recently, integrative pharmacology(IP) has become a pivotal paradigm for the modernization of traditional Chinese medicines(TCM) and combinatorial drugs discovery, which is an interdisciplinary science for establishing the in vitro and in vivo correlation between absorption, distribution, metabolism, and excretion/pharmacokinetic(ADME/PK) profiles of TCM and the molecular networks of disease by the integration of the knowledge of multi-disciplinary and multi-stages. In the present study, an internet-based Computation Platform for IP of TCM(TCM-IP, www.tcmip.cn) is established to promote the development of the emerging discipline. Among them, a big data of TCM is an important resource for TCM-IP including Chinese Medicine Formula Database, Chinese Medical Herbs Database, Chemical Database of Chinese Medicine, Target Database for Disease and Symptoms, et al. Meanwhile, some data mining and bioinformatics approaches are critical technology for TCM-IP including the identification of the TCM constituents, ADME prediction, target prediction for the TCM constituents, network construction and analysis, et al. Furthermore, network beautification and individuation design are employed to meet the consumer's requirement. We firmly believe that TCM-IP is a very useful tool for the identification of active constituents of TCM and their involving potential molecular mechanism for therapeutics, which would wildly applied in quality evaluation, clinical repositioning, scientific discovery based on original thinking, prescription compatibility and new drug of TCM, et al.

Molecular interaction studies of acetylcholinesterase with potential acetylcholinesterase inhibitors from the root of Rhodiola crenulata using molecular docking and isothermal titration calorimetry methods.

(-)-Epicatechin gallate ((-)-ECG), 1,2,3,4,6-O-pentagalloylglucose (PGG), rhodionin, herbacetin and rhodiosin isolated from the root of Rhodiola crenulata exhibited potent, dose-dependent inhibitory effects on acetylcholinesterase (AChE) with IC50 ranged from 57.50±5.83 to 2.43±0.34µg/mL. With the aim of explaining the differences in activity of these active ingredients and clarifying how they inhibit AChE, the AChE-inhibitor interactions were further explored using molecular docking and isothermal titration calorimetry (ITC) methods in the present study. Molecular docking studies revealed that all compounds except PGG showed binding energy values ranging from -10.30 to -8.00kcal/mol while the binding energy of galantamine, a known AChE inhibitor, was -9.53kcal/mol; they inhibited the AChE by binding into the ligand pocket with the similar binding pattern to that of galantamine by interacting with Glu199 of AChE. Inhibition constant of these active ingredients had a positive correlation with binding energy. The interaction between AChE and PGG was further evaluated with the ITC method and the results indicated that the PGG-AChE interaction was relevant to AChE concentration. The results revealed a possible mechanism for the AChE inhibition activity of these bioactive ingredients, which may provide some help in lead compounds optimization in the future.