key: cord-1023232-5z1k3yxv authors: Wang, Zixuan; Zhang, Jumei; Zhan, Jiuyu; Gao, Hongwei title: Screening out anti-inflammatory or anti-viral targets in Xuanfei Baidu Tang through a new technique of reverse finding target date: 2021-08-19 journal: Bioorg Chem DOI: 10.1016/j.bioorg.2021.105274 sha: c0dd0f762774eecbff010f57b6a421a9638bf27f doc_id: 1023232 cord_uid: 5z1k3yxv Traditional Chinese herbal compound prescription in Xuanfei Baidu Tang (XBT) has obvious effects in the treatment of COVID-19. However, its effective compounds and targets for the treatment of COVID-19 remain unclear. Computer-Aided Drug Design is used to virtually screen out the anti-inflammatory or anti-viral compounds in XBT, and predict the potential targets by Discovery Studio 2020. Then, we searched for COVID-19 targets using Genecards databases and Protein Data Bank (PDB) databases and compared them to identify targets that were common to both. Finally, the target we screened out is: TP53 (Tumor Protein P53). This article also shows that XBT in the treatment of COVID-19 works in a multi-link and overall synergistic manner. Our results will help to design the new drugs for COVID-19. we searched for COVID-19 targets using Genecards databases and Protein Data Bank (PDB) databases and compared them to identify targets that were common to both. Finally, the target we screened out is: TP53 (Tumor Protein P53). This article also shows that XBT in the treatment of COVID-19 works in a multi-link and overall synergistic manner. Our results will help to design the new drugs for COVID-19. KEYWORDS: COVID-19; the traditional Chinese herbal medicine; anti-viral; antiinflammatory; reverse finding target. At the end of 2019, the novel coronavirus disease (COVID-19) appeared and caused global concern. It is a highly infectious disease characterized by respiratory symptoms [1] . COVID-19 pandemic has caused an unprecedented and uncontrollable health crisis. Since the outbreak of this disease, it has been characterized by strong infectivity, long treatment time after infection, and high mortality of patients with severe illness [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] . The main physiological and pathological feature of severe COVID-19 is "cytokine storm", also known as inflammatory storm [12] . It is an immune response produced by a positive feedback loop between cytokines and immune cells, and it is also the state which the body's immune system has evolved from "self-protection" to "overprotection" [13, 14] . Therefore, the outbreak of inflammation is the core pathological factor leading to aggravation and even death of patients in lung injury induced by COVID-19 [15] [16] [17] . Over expression and release of pro-inflammatory cytokines will lead to tissue damage [18] [19] [20] [21] [22] [23] [24] . In response to the inflammatory mechanism caused by the novel coronavirus (SARS-CoV-2) infection, a variety of methods have emerged to treat COVID-19, such as oxygen therapy, plasma therapy, drug (lopi-navir, favipiravir, ribavirin, PegIFN-α2a, traditional Chinese herbal compound prescription) therapy [25, 26] . Various traditional Chinese herbal compound prescriptions have been proved to be highly effective in the treatment of COVID-19 and have been widely used in the market and clinic, such as Sijunzi Decoction, Yupingfeng Powder, Buzhong Yiqi Decoction, Xuanfei Baidu Tang (XBT) and so on. In this article, we studies XBT in terms of antiinflammatory or anti-viral effects. XBT is composed of 13 traditional Chinese herbal medicines, namely Ephedra, Bitter almond, Coix Seed, Atractylodes, Patchouli, Artemisia annua, Polygonum cuspidatum, Verbena, Reed root, Semen Lepidii, Exocarpium, licorice and Gypsum. XBT is a traditional Chinese herbal compound prescription for the treatment of anti-epidemic, which is designed for the pathological characteristics of wet toxin [27] . It has the effects of inhibiting viral infections, reducing inflammatory factors, and promoting the absorption of lung inflammation. Because of its outstanding efficacy, it is widely used as a recommended prescription in clinical practice [28] . Computer-Aided Drug Design (CADD) is to design and optimize lead compounds through calculating and estimating the relationship between ligand and receptor based on computer chemistry [29] . With the development of CADD, our understanding of disease pathogenesis, drug signal transduction pathways, target interactions, and other aspects is becoming more and more mature [30] [31] [32] . The modules of drug analysis, synthesis design, and drug molecular optimization in Discovery Studio 2020 (DS2020) make the later experimental operation more convenient. This not only reduces the cost of money and time, but also improves the safety of medicines. CADD builds a bridge between traditional Chinese medicine and modern pharmacology, and plays an important role in scientific development. XBT is widely used in the treatment of clinical diseases [27, 33, 34] , but its effective compounds and targets for the treatment of COVID-19 need to be further clarified. In this study, we aim to use DS2020 to screen out the anti-inflammatory or anti-viral compounds and targets in XBT, which will make contribution to treat COVID-19. The Traditional Chinese Medicines Database is currently the world's largest noncommercial Chinese medicine database. This online database contains more than 20,000 purified compounds of 453 Chinese medicine ingredients [35, 36] . Using the database to obtain plant information, the components of traditional Chinese herbal medicines in XBT were screened separately. The "Plant Source" field in query mode was activated, entering the following informations (Ephedra, Bitter almond, Coix Seed, Atractylodes, Patchouli, Artemisia annua, Polygonum cuspidatum, Verbena, Reed root, Semen Lepidii, Exocarpium, licorice, and Gypsum) one by one. Then execute the query and output the ID of all compounds related to each traditional Chinese herbal medicines, as well as their name, source, structure, efficacy, etc. After exporting the data of all compounds, we only keep the compounds that have the functions of "antiinflammatory" or "anti-virus". In view of the uncertain numbers of ligands and isomers, we used Prepare Ligands to modify the compound system, and then optimized the structure of small molecule ligands through the Minimize Ligands function. The optimized small molecules need to be evaluated by the prediction of ADMET properties. In the early stage of drug development, compounds can be predicted and selected based on the properties of the drug's ADMET. The properties of ADMET we mentioned refer to the absorption, distribution, metabolism, excretion, and toxicity of drug molecules in the body [37, 38] . This operation can lower the expense of excessive structural modification and recombination in the later stage, and can improve the success rate of medical research and development to a certain extent. The ADMET properties that can be calculated in DS2020 include: aqueous solubility, blood brain barrier penetration (BBB), Cytochrome P450 2D6 inhibition [39] , hepatotoxicity, Human intestinal absorption (HIA) and plasma protein binding [40, 41] . After the job is completed, open the ADMET plot window. Because the point within the 99% confidence interval means that the predictive properties of this molecule are reliable, it is necessary to delete the BBB model and the HIA model points outside the 99% confidence interval. For a more comprehensive analysis of drug toxicity, toxicological properties prediction process (TOPKAT) is required after the prediction of ADMET properties. TOPKAT is based on the 2D structure of the molecule to calculate and verify the toxicity and environmental effects of the compound [42] . Toxicity prediction has the characteristics of low cost and high efficiency in drug development and drug risk reduction. Enter the compounds retained after the ADMET screening, and the factors we need to consider in this screening are: mutagenicity, aerobic biodegradability, oral LD50 in rats, and rodent carcinogenicity [43, 44] . After the results are obtained, we need to verify whether all compounds are true in the ranges of "With Optimum Prediction Space (OPS)" and "Within OPS Limits". Among them, "With OPS" evaluates whether the prediction results are in the best prediction space of the model, and "Within OPS Limits" evaluates whether the prediction result is within the best prediction space limit of the model. If "With OPS" and "Within OPS Limits" are both true for one compound, this compound is credible and will been kept. According to the Lipinski Rule of Five and Veber Rule [45, 46] After the evaluation, the small molecule compounds that do not meet the criteria are deleted, and the small molecules with good druggability are retained. After the prediction and screening of reverse finding target, the higher matching value of the pharmacophore and the compound, the more reliable it is. Then the targets corresponding to the pharmacophores are more likely to be the target of the compounds. The Fit Value of the entered compounds and pharmacophores can be got through the calculated results pages of PharmacophoreFits link and the "LigandProfiler" heat map. First, we use reverse finding target technology to get the targets that have antiinflammatory or antiviral effects in XBT. Then we use Genecards databases (https://www.genecards.org) and Protein Data Bank (PDB) databases to search all targets related to COVID-19 with the key word of "SARS-CoV-2". By comparing the results of these two parts, we can find anti-inflammatory or antiviral targets related to COVID-19 in XBT. Finally, we use Cytoscape 3.8.0 to establish the interaction network diagram of "XBT-Compounds-Targets-Efficacy". After the construction is completed, it can clearly show the active components of each the traditional Chinese herbal medicine, as well as the common targets related to anti-inflammation or antivirus in these components. In the Traditional Chinese Medicines Database, if you query with "Ephedra" as the key word, the output results show that there are 53 compounds related to ephedra, including 2 compounds with anti-inflammatory effects and 0 compounds with antiviral effects. By analogy, it can be seen from After the small molecule optimization process, the ADMET property prediction is carried out. Most drugs need to be discontinued and search for other candidate compounds during the development process. The main reason for this result is that the prediction results of ADMET properties are unreliable. From Fig. 1 , we can see that if a compound is reliable, the points shown in the prediction must be within the blue ellipse (99% confidence interval of the BBB model) and the green ellipse (99% confidence interval of the HIA model). Therefore, as shown in the Fig. 1, only 1 compound remains in Ephedra. In the same way, finally, 1 compound is retained in Bitter almond, 3 compounds in Atractylodes, 13 compounds in Artemisia annua, 3 compounds in Polygonum cuspidatum, and 30 compounds in Licorice. The compounds in Patchouli all meet the requirements of ADMET, while the compounds in Exocarpium do not meet the requirements of ADMET. Therefore, Exocarpium is excluded from the next calculation. The calculated result is exported to PDF format. Open the calculated result (Table 3，4) In order to show the matching degree of the pharmacophore with all the compounds participating in the test more concisely, we used the "Ligand Profiler" heat map (Fig. 2) to represent it. In Fig. 2 , the horizontal axis represents the pharmacophores, and the vertical axis represents the compounds screened from traditional Chinese herbal medicines. The color changes from red to blue, indicating that the Fit Value is gradually decreasing. In order to explain the meaning of Fig. 2 more clearly, we take Ephedra as an example. After a series of screening, we extracted (4S, 5R) Ephedroxane from Ephedra. Because the pharmacophore with higher matching degree is red and yellow, the fitted value corresponding to 1coy is the highest. Therefore, the target corresponding to 1coy is very likely to be the target of (4S, 5R) Ephedroxane. It can be seen from Fig. 2，3 that 29 compounds in XBT through reverse finding target were found. The reverse finding target technology was used to obtain pharmacophores whose corresponding targets were been predicted. The targets of XBT are rep (ATP-dependent DNA helicase Rep), TTR (Transthyretin), AKT1 (AKT Serine/Threonine Kinase 1), FYN (FYN Proto-Oncogene/Src Family Tyrosine Kinase) and TP53 (Tumor Protein P53). As the potential targets: TP53, FYN, FYN are against inflammation, rep is against virus, and AKTI is against inflammation and virus ( Table 5) . We compared these targets (rep, TTR, AKT1, FYN and TP53) with the targets searched by Genecards databases and PDB databases, and found the common target TP53 for COVID-19 (Fig. 4, 5) . Fig. 4 , there are 29 compounds from 6 traditional Chinese herbal medicines (Ephedra, Atractylodes, Patchouli, Artemisia annua, Polygonum cuspidatum, Licorice) that have anti-inflammatory or antiviral effects in XBT. As shown in Based on the anti-inflammatory or anti-viral effects, we used DS2020 to optimize, screen out small molecule and predict ADMET, toxicological properties for various traditional Chinese herbal medicines in XBT. The pharmacophores are collected by reverse finding target, and then the targets are predicted. From Table 5 , we found that the anti-inflammatory or antiviral targets corresponding to the compounds of XBT were mainly: rep, TTR, AKT1, FYN, TP53. Among them, TP53 is highly related to COVID-19. In addition, among these targets, each target is connected to two or more compounds, indicating that the same target can also be controlled by multiple compounds at the same time. The calculated results are expected to be used in the design of new drugs for the treatment of COVID-19. All authors declare that No conflict of interest exists. 2gz7-01，2gz7-01-s，2gz7-02，2gz7-03，2gz7-03-s，2gz7-04，2gz7-04-s，2gz7-05，2gz7-05-s，2gz7-06， 2gz7-07， 2gz7-07-s， 2gz7-08， 2gz7-08-s， 2gz7-09 2gz7(rep) antiviral 2dq7-01, 2dq7-02, 2dq7-02-s, 2dq7-03, 2dq7-04 ， 2dq7-05，2dq7-05-s，2dq7-06, 2dq7-06-s，2dq7-07， 2dq7-07-s，2dq7-08，2dq7-09，2dq7-10 2dq7(FYN) antiinflammatory 2x0v-01 2x0v(TP53) antiinflammatory 2x0u-01 2x0u(TP53) antiinflammatory 3cqu-01 ， 3cqu-01-s ， 3cqu-02 ， 3cqu-02-s ， 3cqu-03， 3cqu-03-s， 3cqu-04， 3cqu-04-s， 3cqu-05， 3cqu-05-s，3cqu-06，3cqu-06-s，3cqu-07，3cqu-07-s，3cqu-08， 3cqu-08-s， 3cqu-09， 3cqu-09-s， 3cqu-10， 3cqu-10s 3cqu(AKT1) antiviral ， antiinflammatory 3cqw-01， 3cqw-02，3cqw-03，3cqw-04，3cqw-05， 3cqw-06， 3cqw-06-s， 3cqw-07， 3cqw-08， 3cqw-09， 3cqw-10，3cqw-10-s 3cqw(AKT1) antiviral ， antiinflammatory 29 A systematic review involving 11,187 participants evaluating the impact of COVID-19 on anxiety and depression in pregnant women Introduction to COVID-19, history, impact, symptoms and prevention COVID19: A Systematic Approach to Early Identification and Healthcare Worker Protection Effects of COVID-19 Home Confinement on Eating Behaviour and Physical Activity: Results of the ECLB-COVID19 International Online Survey Molecular mechanisms of the novel coronavirus SARS-CoV-2 and potential anti-COVID19 pharmacological targets since the outbreak of the pandemic Effects of COVID-19 Home Confinement on Eating Behaviour and Physical Activity: Results of the ECLB-COVID19 International Online Survey Challenges and solutions for addressing critical shortage of supply chain for personal and protective equipment (PPE) arising from Coronavirus disease (COVID19) pandemic -Case study from the Republic of Ireland Novel SARS-CoV-2 outbreak and COVID19 disease a systemic review on the global pandemic, genes and diseases Impact of Non-pharmaceutical Interventions (NPIs) to Reduce 13 COVID-19 Mortality and Healthcare Demand The Role of Telemedicine in the Maintenance of IR Outpatient Evaluation and Management Volume During the COVID-19 Global Pandemic COVID-19, chronic inflammatory rheumatic disease and anti-rheumatic treatments SARS-CoV-2, which induces COVID-19, causes kawasaki-like disease in children: role of pro-inflammatory and anti-inflammatory cytokines The allergist/immunologist, the Janus gatekeeper of inflammation, COVID-19 and beyond Relation between Cardiac Injury and Elevated Levels with Severe COVID-19 of Inflammatory Biomarkers in Patients Clinical significance of measuring serum cytokine levels as inflammatory biomarkers in adult and pediatric COVID-19 cases: A review Characteristics of inflammatory factors and lymphocyte subsets in patients with severe COVID-19 Cytokine prediction of mortality in COVID19 patients Stroke and COVID19: Not only a large-vessel disease Mesenchymal Stem Cells: A new front emerge in COVID19 treatment: Mesenchymal Stem Cells therapy for SARS-CoV2 viral infection Reduced Inflammatory Responses to SARS-CoV-2 Infection in Children Presenting to Hospital with COVID-19 in China The pro-inflammatory cytokines in COVID-19 pathogenesis: What goes wrong? 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