Structure-based identification of natural compound inhibitor against M. tuberculosis thioredoxin reductase: insight from molecular docking and dynamics simulation

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

The Thioredoxin System in Edwardsiella piscicida Contributes to Oxidative Stress Tolerance, Motility, and Virulence

Edwardsiella piscicida is an important fish pathogen that causes substantial economic losses. In order to understand its pathogenic mechanism, additional new virulence factors need to be identified. The bacterial thioredoxin system is a major disulfide reductase system, but its function is largely unknown in E. piscicida. In this study, we investigated the roles of the thioredoxin system in E. piscicida (named TrxB_Ep, TrxA_Ep, and TrxC_Ep, respectively) by constructing a correspondingly markerless in-frame mutant strain: ΔtrxB, ΔtrxA, and ΔtrxC, respectively. We found that (i) TrxB_Ep is confirmed as an intracellular protein, which is different from the prediction made by the Protter illustration; (ii) compared to the wild-type strain, ΔtrxB exhibits resistance against H₂O₂ stress but high sensitivity to thiol-specific diamide stress, while ΔtrxA and ΔtrxC are moderately sensitive to both H₂O₂ and diamide conditions; (iii) the deletions of trxB_Ep, trxA_Ep, and trxC_Ep damage E. piscicida's flagella formation and motility, and trxB_Ep plays a decisive role; (iv) deletions of trxB_Ep, trxA_Ep, and trxC_Ep substantially abate bacterial resistance against host serum, especially trxB_Ep deletion; (v) trxA_Ep and trxC_Ep, but not trxB_Ep, are involved in bacterial survival and replication in phagocytes; (vi) the thioredoxin system participates in bacterial dissemination in host immune tissues. These findings indicate that the thioredoxin system of E. piscicida plays an important role in stress resistance and virulence, which provides insight into the pathogenic mechanism of E. piscicida.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

CB1 as a novel target for Ginkgo biloba's terpene trilactone for controlling chemotherapy-induced peripheral neuropathy (CIPN)

The application of antineoplastic chemotherapeutic agents causes a common side effect known as chemotherapy-induced peripheral neuropathy (CIPN) that leads to reducing the quality of patient's life. This research involves the performance of molecular docking and molecular dynamic (MD) simulation studies to explore the impact of terpenoids of Ginkgo biloba on the targets (CB-1, TLR4, FAAH-1, COX-1, COX-2) that can significantly affect the controlling of CIPN's symptoms. According to the in-vitro and in-vivo investigations, terpenoids, particularly ginkgolides B, A, and bilobalide, can cause significant effects on neuropathic pain. The molecular docking results disclosed the tendency of our ligands to interact with mainly CB1 and FAAH-1, as well as partly with TLR4, throughout their interactions with targets. Terpene trilactone can exhibit a lower rate of binding energy than CB1's inhibitor (7dy), while being precisely located in the CB1's active site and capable of inducing stable interactions by forming hydrogen bonds. The analyses of MD simulation proved that ginkgolide B was a more suitable activator and inhibitor for CB1 and TLR4, respectively, when compared to bilobalide and ginkgolide A. Moreover, bilobalide is capable of inhibiting FAAH-1 more effectively than the two other ligands. According to the analyses of ADME, every three ligands followed the Lipinski's rule of five. Considering these facts, the exertion of three ligands is recommended for their anti-inflammatory, neuroprotective, and anti-nociception influences caused by primarily activating CB1 and inhibiting FAAH-1 and TLR4; in this regard, these compounds can stand as potential candidates for the control and treatment of CIPN's symptoms.

Analysis of Pleurotin binding to human thioredoxin reductase using docking and molecular dynamics simulation

Thioredoxin reductase (TrxR) has been considered a potential target for cancer chemotherapy. It acts by controlling the redox homeostasis of human cells and, therefore, interfering in its function may trigger apoptosis, which is a crucial tumor suppression mechanism. Despite the great effort in the search for TrxR inhibitors, none was approved for human therapy. In the present study a virtual screening for natural organic compounds is discussed for a set of 72 compounds with known IC-50 for TrxR inhibition. The results suggest the Pleurotin, a naphthoquinone obtained from Hohenbuehelia grisea fungus, as a potential TrxR inhibitor, which acts by binding to the active site of the enzyme, between the N- and C-terminal domains. The presence of the ligand blocks the approximation of the C-terminal arm to the N-terminal, which is an essential step of the enzyme function. Besides, the two equivalent binding sites of TrxR were explored, by docking two ligands simultaneously. The results indicate that both sites have an allosteric correlation and, the presence of the ligand in one site may interfere, or even prevent, the binding of the second ligand at the other site. All these findings are quantitatively discussed based on the analysis of long molecular dynamics trajectories, which provides a full description of the ligand-receptor binding modes, average binding energies and conformational changes.Communicated by Ramaswamy H. Sarma.

From genomes to molecular dynamics - A bottom up approach in extrication of SARS CoV-2 main protease inhibitors

The recent pandemic Coronavirus disease-19 outbreak had traumatized global countries since its origin in late December 2019. Though the virus originated in China, it has spread rapidly across the world due its firmly established community transmission. To successfully tackle the spread and further infection, there needs a clear multidimensional understanding of the molecular mechanisms. Henceforth, 942 viral genome sequences were analysed to predict the core genomes crucial in virus life cycle. Additionally, 35 small interfering RNA transcripts were predicted that can target specifically the viral core proteins and reduce pathogenesis. The crystal structure of Covid-19 main protease-6LU7 was chosen as an attractive target due to the factors that there were fewer mutations and whose structure had significant identity to the annotated protein sequence of the core genome. Drug repurposing of both recruiting and non recruiting drugs was carried out through molecular docking procedures to recognize bitolterol as a good inhibitor of Covid-19 protease. The study was extended further to screen antiviral phytocompounds through quantitative structure activity relationship and molecular docking to identify davidigenin, from licorice as the best novel lead with good interactions and binding energy. The docking of the best compounds in all three categories was validated with molecular dynamics simulations which implied stable binding of the drug and lead molecule. Though the studies need clinical evaluations, the results are suggestive of curbing the pandemic.