Histone lysine-specific demethylase 1 (LSD1) is overexpressed in various solid and hematological tumors, suggesting its potential as a therapeutic target, but there are currently no LSD1 inhibitors available on the market. In this study we employed a computer-guided approach to identify novel LSD1/EGFR dual inhibitors as a potential therapeutic agent for non-small cell lung cancer. Through a multi-stage virtual screening approach, we found L-1 and L-6, two compounds with unique scaffolds that effectively inhibit LSD1 with IC50 values of 6.24 and 9.26 μM, respectively. Using molecular similarity-based screening, 48 analogs of L-1 and L-6 were retrieved from ChemDiv library, 18 analogs were selected for biological activity analysis. Eight compounds showed weaker inhibitory activity against LSD1, with IC50 values of 19.79 – 35.70 μM. Moreover, L-1, L-6, and two analogs of L-6 (D-14 and D-16) were found to inhibit triple-mutant EGFR (L858R/T790M/C797S) with potencies ranging from 5.01 to 86.70 μM, and to inhibit double-mutant EGFR (T790M/L858R) with potencies ranging from 2.06 to 64.36 μM. In BaF3 cells that stably express EGFR (L858R/T790M/C797S), the inhibitory activity of L-1, L-6, D-14 and D-16 ranged from 2.72 to 8.99 μM. L-1 that shows the highest biological activity across BaF3 cell, mutant EGFR kinase and LSD1 assays due to its dual targeting of LSD1/EGFR, emerges as a promising lead compound for non-small cell lung cancer treatment. This study demonstrates that L-1 efficiently inhibits lung cancer growth in vitro and in vivo, suggesting it as a potential lead for non-small cell lung cancer treatment, highlighting the utility of virtual screening methods in discovering multi-target inhibitors and strategies for other diseases.

BackgroundColorectal cancer (CRC) has emerged as a growing global health challenge, while immunotherapy, particularly mRNA-based cancer vaccines, has emerged as a promising approach due to its ability to induce targeted immune responses with minimal systemic toxicity. This study aimed to design a multi-epitope mRNA vaccine targeting tumor-specific antigens (TSAs) as a cancer therapeutic regimen.ResultsWe chose six CRC-specific TSAs and selected their appropriate epitopes with immunoinformatic tools. In order to enhance the vaccine stability, we subsequently optimized the open reading frame (ORF) sequences, which demonstrated the highest structural stability among all evaluated approaches. Furthermore, we built a CNN model combined with RNA large language model (RNA-FM) embeddings to screen 212 candidate 5’UTR sequences and identify variants that boost the vaccine’s translational efficiency. Finally, in silico immune simulations confirmed the vaccine’s ability to elicit robust humoral and cellular immune responses.ConclusionThis study presents an in silico designed mRNA vaccine against colorectal cancer (CRC). Immune simulations demonstrated that this mRNA vaccine can elicit strong antitumor immune responses, indicating it is an effective and promising candidate that warrants further in vitro and in vivo investigations. Additionally, this work highlights the potential of in silico approaches in vaccine design and provides valuable insights for the development of effective vaccines targeting CRC.

Tea polyphenols and theabrownin possess antioxidant properties, but thermal processing effects on their composition and intestinal protection are unclear. This study used oxidized fish oil (peroxide value: 28.87 mmol/kg) to simulate dietary exposure to oxidized lipids, inducing oxidative stress and intestinal barrier impairment in rats. Heating reduced tea polyphenols (10.3 %), diminishing their protective effect, as evidenced by a 4.6-fold rise in ROS-positive cells (2.6 % to 12.1 %) and decreased activation of NRF2 signaling pathway. Conversely, thermal processing increased the levels of low-molecular-weight compounds in theabrownin (e.g., Teadenol A), and theabrownin maintained antioxidant bioactivity after heating, as indicated by a 25.6 % increase in BCL2/BAX ratio and cytoprotective effects. These findings indicate that theabrownin exhibits thermal stability with maintained antioxidant functionality, while tea polyphenols undergo significant alterations. This supports the potential application of thermostable theabrownin as a functional food ingredient targeting oxidative stress-related intestinal damage.

This study aimed to investigate the effect of Broussonetia papyrifera polysaccharides (BPP) on the jejunal intestinal integrity of rats ingesting oxidized fish oil (OFO) induced oxidative stress. Polysaccharides (Mw 16,956 Da) containing carboxyl groups were extracted from Broussonetia papyrifera leaves. In vitro antioxidant assays showed that this polysaccharide possessed antioxidant capabilities. Thirty-two male weaned rats were allocated into two groups orally infused BPP solution and PBS for 26 days, respectively. From day 9 to day 26, half of the rats in each group were fed food containing OFO, where the lipid peroxidation can induce intestinal oxidative stress. OFO administration resulted in diarrhea, decreased growth performance ( p < 0.01), impaired jejunal morphology ( p < 0.05) and antioxidant capacity ( p < 0.01), increased the levels of ROS and its related products, IL-1 beta and IL -17 ( p < 0.01) of jejunum, as well as down -regulated Bcl-2/Bax ( p < 0.01) and Nrf2 signaling ( p < 0.01) of jejunum in rats. BPP gavage effectively alleviated the negative effects of OFO on growth performance, morphology, enterocyte apoptosis, antioxidant capacity and inflammation of jejunum ( p < 0.05) in rats. In the oxidative stress model cell assay, the use of receptor inhibitors inhibited the enhancement of antioxidant capacity by BPP. These results suggested that BPP protected intestinal morphology, thus improving growth performance and reducing diarrhea in rats ingesting OFO. This protective effect may be attributed to scavenging free radicals and activating the Nrf2 pathway, which enhances antioxidant capacity, consequently reducing inflammation and mitigating intestinal cell death.

This study isolated and purified a novel homogeneous arabinogalactan polysaccharide from Yucca schidigera extract (YSE), unveiled its unique structure and explored its antioxidant function. Firstly, the antioxidant potential of YSE was demonstrated in piglet trials. A homogeneous polysaccharide with a molecular weight of 24.2 kDa, designated as Yucca schidigera polysaccharide B (YPB), was isolated and purified from YSE. The monosaccharide composition of YPB was Rha, Araf, Galp, and Glcp, whose molar percentages were 2.8 %, 11.6 %, 45.5 %, and 40.0 %, respectively. Methylation analysis combined with 1D and 2D nuclear magnetic resonance showed that YPB was a complex polysaccharide with a main glycosidic linkage pattern of →2)-α-ʟ-Rha-(1 → 3)-β-ᴅ-Galp-(1→3)-β-ᴅ-Galp-(1 → 3)-β-ᴅ-Galp-(1 → 3)-β-ᴅ-Glcp-(1→, and branched Araf and Galp fragments were connected with the main chain through →3,6)-β-ᴅ-Galp-(1→, →3,4)-β-ᴅ-Glcp-(1→, and →2,4)-α-ʟ-Rha-(1→ linkages. Following the in vitro biochemical assays of bioactive components, YPB should be the contributor to the antioxidant activity in YSE. Based on the establishment of oxidative stress model, YPB exhibited strong antioxidant capacity and activated NRF2 pathway, and then provided protection against the damage induced oxidative stress in IPEC-J2 cells and rats. Further analysis with inhibitors found that this antioxidant effect was attributed to its interaction with epidermal growth factor receptor and mannose receptor, and stimulating PI3K/AKT pathway.

The purpose of this study was to investigate whether dietary lactose supplementation relieves rotavirus (RV)-induced diarrhea and gut dysfunction. Thirty-six crossbred weaned piglets were randomly allocated into three groups and fed diets containing 0, 4%, and 6% lactose for 20 days. On Day 15, half of the piglets in each group were orally infused with RV. RV infection impaired growth performance; induced severe diarrhea; decreased serum D-xylose concentration and morphology and sIgA level of jejunal mucosa; downregulated MUC1, MUC2, occludin, Bcl-2, IL-4, pBD3, pBD2, and pBD1 mRNA expression of jejunal mucosa and/or mesenteric lymph nodes; upregulated Bax, caspase-3, IL-2, IFN-γ, and IFN-β mRNA expression of jejunal mucosa and/or mesenteric lymph nodes; and damaged microbiota and metabolites of cecal digesta in weaned piglets (p < 0.05). Dietary lactose supplementation improved nutrient digestibility and growth performance and relieved the negative influence of RV challenge on intestinal barrier function, mRNA expression of cytokines, and host defense peptides of jejunal mucosa and/or mesenteric lymph nodes in weaned piglets (p < 0.05). Dietary administration of 6% lactose tended to relieve diarrhea (p = 0.07). These results suggest that lactose in feed increases growth performance and has a tendency to alleviate RV-induced diarrhea, derived from the improvement of nutrient utilization, gut barrier function, and immunity.

Diabetic retinopathy (DR), a diabetic microangiopathy caused by diabetes, affects approximately 93 million people, worldwide. However, the drugs used to treat DR have limited efficacy and the variety of side effects. This is possibly because the complicated pathogenesis of DR is associated with multiple proteins. In this work, we attempted to identify potential drugs against DR-associated proteins and predict potential targets for drugs using in silico prediction of chemical-protein interactions (CPI) based on multitarget quantitative structure-activity relationship (mt-QSAR) method. Therefore, we developed 128 binary classifiers to predict the CPI for 15 DR targets using random forest (RF), k-nearest neighbours (KNN), support vector machine (SVM), and neural network (NN) algorithms with MACCS, extended connectivity fingerprints (ECFP6) fingerprints, and protein descriptors. In order to facilitate discovery of the novel drugs and target identification using the 128 binary classifiers, a free web server (DRDB) was developed. Compound Danshen Dripping Pills (CDDP), composed of Salvia miltiorrhiza, Panax notoginseng, and borneol, is commonly used in the treatment of cardiovascular diseases. To explore the applicability of DRDB, the potential CPIs of CDDP in treatment of DR were investigated based on DRDB. In vitro experimental validation demonstrated that cryptotanshinone and protocatechuic acid, two key components of CDDP, are capable of targeting ICAM-1 which is one of the key target of DR. We hope that this work can facilitate development of more effective clinical strategies for the treatment of DR.

Many advances in modifying enzyme properties have been made based on the computation of stable transition states and experimental directed evolution. However, there are still great challenges related to difficult transition state (TS) calculations and the large number of evolutionary experiments needed for verification. The transition state analogue (TSA) has chemical similarity with the TS structure and can be used as a proxy for the unknown TS. Here, we proposed a computation-driven strategy to optimize structures of enzymes via simulation-based iterative saturation mutagenesis (ISM), aiming to stabilize the spatial effects of the TS with a TSA as a placeholder. This approach was applied to redesign structures of chitinase in order to increase its catalytic activity. After three rounds of simulation-based ISM, the catalytic activity of the triple mutant P55T/S216M/R301T was 29.3-fold higher than that of the wild type. The enrichment ratio of variants with increased activity reached 83% in the library selected for experimental verification. In this study, the strategy of optimizing enzymatic activity by computational ISM based on TSA has been streamlined, which facilitates the process of ISM and can be used to extend the bounds of the local optimal solution space.

Motivation: In the process of discovery and optimization of lead compounds, it is difficult for non-expert pharmacologists to intuitively determine the contribution of substructure to a particular property of a molecule. Results: In this work, we develop a user-friendly web service, named interpretable-absorption, distribution, metabolism, excretion and toxicity (ADMET), which predict 59 ADMET-associated properties using 90 qualitative classification models and 28 quantitative regression models based on graph convolutional neural network and graph attention network algorithms. In interpretable-ADMET, there are 250 729 entries associated with 59 kinds of ADMET-associated properties for 80 167 chemical compounds. In addition to making predictions, interpretable-ADMET provides interpretation models based on gradient-weighted class activation map for identifying the substructure, which is important to the particular property. Interpretable-ADMET also provides an optimize module to automatically generate a set of novel virtual candidates based on matched molecular pair rules. We believe that interpretable-ADMET could serve as a useful tool for lead optimization in drug discovery.

During infection of a host, Pseudomonas aeruginosa orchestrates global gene expression to adapt to the host environment and counter the immune attacks. P. aeruginosa harbours hundreds of regulatory genes that play essential roles in controlling gene expression. However, their contributions to the bacterial pathogenesis remain largely unknown. In this study, we analysed the transcriptomic profile of P. aeruginosa cells isolated from lungs of infected mice and examined the roles of upregulated regulatory genes in bacterial virulence. Mutation of a novel regulatory gene pvrA (PA2957) attenuated the bacterial virulence in an acute pneumonia model. Chromatin immunoprecipitation (ChIP)-Seq and genetic analyses revealed that PvrA directly regulates genes involved in phosphatidylcholine utilization and fatty acid catabolism. Mutation of the pvrA resulted in defective bacterial growth when phosphatidylcholine or palmitic acid was used as the sole carbon source. We further demonstrated that palmitoyl coenzyme A is a ligand for the PvrA, enhancing the binding affinity of PvrA to its target promoters. An arginine residue at position 136 was found to be essential for PvrA to bind palmitoyl coenzyme A. Overall, our results revealed a novel regulatory pathway that controls genes involved in phosphatidylcholine and fatty acid utilization and contributes to the bacterial virulence.

Objective: Pei?Yuan?Tong?Nao (PYTN) capsule has been widely used for the treatment of cerebrovascular disease (CVD), including cerebral ischemia. However, due to the complexity of traditional Chinese Medicine (TCM) and the lack of proper inspection methods, the pharmacological mechanisms of the actions of PYTN capsule on CVD remain ambiguous. In this investigation, a network pharmacology method was used to investigate the mechanism of action of PYTN capsule in the treatment of CVD. Methods: In this method, a chemical similarity ensemble approach was employed to predict putative targets for chemicals derived from PYTN capsule. Results: Thus, a total of 351 compounds and 650 proteins were identified as potential active ingredients and putative CVD?related targets. In addition, two interaction networks were constructed, including a network between putative targets of PYTN capsule and known CVD?related targets, and a network between candidate active compounds and putative CVD?related targets. Conclusion: The mechanism of PYTN capsule in the treatment of CVD was found to be based on three functional modules, namely, antioxidation, anti?inflammation, and antiapoptosis modules through multiple signaling pathways, such as PI3K?Akt, mitogen?activated protein kinase, and TNF signaling pathways. We hope that this work can provide insight into the pharmacological mechanisms of TCMs in the treatment of CVD and provide a basis for further development and the discovery of more effective clinical strategies for the treatment of complicated diseases.

Cyclocarya paliurus (Batalin) Iljinsk is a medicinal plant belonging to the Juglandaceae family, and its leaves are used for a traditional sweet herbal tea with bioactivity against obesity and hyperglycaemia in China. It contains various bioactive specialised metabolites, such as flavonoids, triterpenes and their glucosides, while no glycosyltransferases (GTs) have been reported in C. paliurus to date. Herein, we identified and cloned the first glucosyltransferase C. paliurus GT1. The expression profiles of C. paliurus GT1 showed very high expression in young leaves, callus and branches, but relatively low expression in old leaves and bark and no expression in root. The recombinant C. paliurus GT1 protein was heterologously expressed in Escherichia coli and exhibited catalytic activity towards multiple flavonoids favouring substrate- and regio-specific biosynthesis. Further enzyme assays indicated a preference for certain hydroxyl group glucosylation by C. paliurus GT1. C. paliurus GT1 actively catalysed the glucosylation of flavones and flavonols, but it was less active towards isoflavones, flavanones or triterpenes. C. paliurus GT1 was also able to catalyse the attachment of sugars to the thiol (S-) or amine (N-) sites on aromatic compounds but not on aliphatic compounds. Molecular docking and site-directed mutagenesis analyses indicated that A43F, V84P, and M201Y dramatically altered the regio-selectivity and activity, and the W283M mutation and deletion of the V309-D320 region enhanced the activity and the formation of disaccharides. Herein, we present the identification and characterization of the first multi-functional glucosyltransferase in C. paliurus and provide a basis for understanding the biosynthesis of flavonoid glucosides. C. paliurus GT1 could be utilized as a synthetic biology tool for the synthesis of O-, N-, or S-glucosylated natural/unnatural products.

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.

Co-infection between HIV-1 and HCV is common today in certain populations. However, treatment of co-infection is full of challenges with special consideration for potential hepatic safety and drug-drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV co-infection. However, identification of one molecule acting on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining naive Bayesian (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints (MACCS and ECFP6), 60 classification models were constructed to predict the active compounds toward 11 HIV-1 targets and 4 HCV targets based on the multitarget-quantitative structure-activity relationships (mt-QSAR). 5-fold cross-validation and test set validation was performed to confirm the performance of 60 classification models. Our results show that 60 mt-QSAR models appeared to have high classification accuracy in terms of ROC-AUC values ranging from 0.83 to 1 with a mean value of 0.97 for HIV-1 models, and ROC-AUC values ranging from 0.84 to 1 with a mean value of 0.96 for HCV. Furthermore, the 60 models were applied to comprehensively predict the potential targets for additional 46 compounds including 27 approved HIV-1 drugs, 10 approved HCV drugs and 9 selected compounds known to be active on one or more targets of HIV-1 or those of HCV. Finally, 18 hits including 7 HIV-1 approved drugs, 4 HCV approved drugs and 7 compounds were predicted to be HIV/HCV co-infection multitarget inhibitors. The reported bioactivity data confirmed that 7 compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. Of those remaining predicted hits and chemical-protein interaction pairs involving the potential ability to suppress HIV/HCV co-infection deserve further investigation by experiments. This investigation shows that the mt-QSAR method is available to predict chemical-protein interaction for discovering multitarget inhibitors and provide a unique perspective on HIV/HCV co-infection treatment.

The use of antagonists for each adenosine receptor (AR) subtype as potent clinical candidates is of growing interest due to their involvement in the treatment of various diseases. The recent resolution of several A(1) and A(2A) ARs X-ray structures provides opportunities for structure-based drug design. In this study, we describe the discovery of novel A(1)AR antagonists by applying a multistage virtual screening approach, which is based on random forest (RF), e-pharmacophore modeling and docking methods. A multistage virtual screening approach was applied to screen the ChemDiv library (1,492,362 compounds). Among the final hits, 22 compounds were selected for further radioligand binding assay analysis against human A(1)AR, and 18 compounds (81.82% success) exhibited nanomolar or low micromolar binding potency (K-i). Then, we selected six compounds (pK(i) > 6) to further evaluate their antagonist profile in a cAMP functional assay, and we found that they had low micromolar antagonistic activity (pIC(50) = 5.51-6.38) for the A(1)AR. Particularly, four of six compounds (pK(i) > 6) showed very good affinity (pKi = 6.11-7.13) and selectively (>100-fold) for A(1)AR over A(2A)AR. Moreover, the novelty analysis suggested that four of six compounds (pK(i) > 6) were dissimilar to existing A(1)AR antagonists and hence represented novel A(1)AR antagonists. Further molecular docking and molecular dynamics (MD) studies showed that the three selective compounds 15, 20 and 22 were stabilized (RMSlig value <= 2 angstrom) inside the binding pocket of AAR with similar orientations to the docking pose in 100-ns MD simulations, whereas they escaped from the binding area of A(2A)AR with larger values of RMSlig (RMSlig >= 2 angstrom). We hope that these findings provide new insights into the discovery of drugs targeting A(1)AR and facilitate research on new drugs and treatments for A(1)AR-related human pathologies. (C) 2019 Elsevier Masson SAS. All rights reserved.

Glaucoma is a group of neurodegenerative diseases that can cause irreversible blindness. The current medications, which mainly reduce intraocular pressure to slow the progression of disease, may have local and systemic side effects. Recently, medications with possible neuroprotective effects have attracted much attention. To assist in the identification of new glaucoma drugs, we created a glaucomatous chemogenomics database (GCDB; http://cadd. pharmacy. nankai. edu. cn/gcdb/home) in which various glaucoma-related chemogenomics data records are assembled, including 275 genes, 105 proteins, 83 approved or clinical trial drugs, 90 206 chemicals associated with 213 093 records of reported bioactivities from 22 324 corresponding bioassays and 5630 references. Moreover, an improved chemical similarity ensemble approach computational algorithm was incorporated in the GCDB to identify new targets and design new drugs. Further, we demonstrated the application of GCDB in a case study screening two chemical libraries, Maybridge and Specs, to identify interactions between small molecules and glaucoma-related proteins. Finally, six and four compounds were selected from the final hits for in vitro human glucocorticoid receptor (hGR) and adenosine A3 receptor (A3AR) inhibitory assays, respectively. Of these compounds, six were shown to have inhibitory activities against hGR, with IC50 values ranging from 2.92-28.43 mu M, whereas one compound showed inhibitory activity against A3AR, with an IC50 of 6.15 mu M. Overall, GCDB will be helpful in target identification and glaucoma chemogenomics data exchange and sharing, and facilitate drug discovery for glaucoma treatment.

The rising antibiotic resistance of bacteria imposes a severe threat on human health. Inhibition of bacterial virulence is an alternative approach to develop new antimicrobials. Molecules targeting antibiotic resistant enzymes have been used in combination with cognate antibiotics. It might be ideal that a molecule can simultaneously suppress virulence factors and antibiotic resistance. Here we combined genetic and computer-aided inhibitor screening to search for such molecules against the bacterial pathogen Pseudomonas aeruginosa. To identify target proteins that control both virulence and antibiotic resistance, we screened for mutants with defective cytotoxicity and biofilm formation from 93 transposon insertion mutants previously reported with increased antibiotic susceptibility. A pyrD mutant displayed defects in cytotoxicity, biofilm formation, quorum sensing and virulence in an acute mouse pneumonia model. Next, we employed a computer-aided screening to identify potential inhibitors of the PyrD protein, a dihydroorotate dehydrogenase (DHODase) involved in pyrimidine biosynthesis. One of the predicted inhibitors was able to suppress the enzymatic activity of PyrD as well as bacterial cytotoxicity, biofilm formation and antibiotic resistance. A single administration of the compound reduced the bacterial colonization in the acute mouse pneumonia model. Therefore, we have developed a strategy to identify novel treatment targets and antimicrobial molecules.

1.4 Protein arginine deiminases 4 (PAD4) is an attractive target for the development of novel and selective inhibitors of Rheumatoid Arthritis (RA). F-amidine is known as mechanism-based inhibitor targeting PAD4 and used as inactivators by covalently modifying the active site Cys645. To identify novel structural inhibitors of PAD4, we investigated the flexibility of protein on basis of the transition state geometry of PAD4 inhibited by F-amidine from our previous QM/MM calculation. And a pharmacophore model was generated containing four features (ADHH) using five representative structures from molecular dynamic (MD) simulation on basis of the transition state geometry of PAD4 inhibited by F-amidine. We performed virtual screening using the pharmacophore model and molecular docking methods, resulting in the discovery of two molecules with KD (dissociation equilibrium constant) values of 112μM and 218μΜ against PAD4 through Surface Plasmon Resonance (SPR) experiments. These two molecules could potentially serve as PAD4 inhibitors.

The NS5B polymerase is one of the most attractive targets for developing new drugs to block Hepatitis C virus (HCV) infection. We describe the discovery of novel potent HCV NS5B polymerase inhibitors by employing a virtual screening (VS) approach, which is based on random forest (RB-VS), e-pharmacophore (PB-VS), and docking (DB-VS) methods. In the RB-VS stage, after feature selection, a model with 16 descriptors was used. In the PB-VS stage, six energy-based pharmacophore (e-pharmacophore) models from different crystal structures of the NS5B polymerase with ligands binding at the palm I, thumb I and thumb II regions were used. In the DB-VS stage, the Glide SP and XP docking protocols with default parameters were employed. In the virtual screening approach, the RB-VS, PB-VS and DB-VS methods were applied in increasing order of complexity to screen the InterBioScreen database. From the final hits, we selected 5 compounds for further anti-HCV activity and cellular cytotoxicity assay. All 5 compounds were found to inhibit NS5B polymerase with IC50 values of 2.01-23.84 μM and displayed anti-HCV activities with EC50 values ranging from 1.61 to 21.88 μM, and all compounds displayed no cellular cytotoxicity (CC50 > 100 μM) except compound N2, which displayed weak cytotoxicity with a CC50 value of 51.3 μM. The hit compound N2 had the best antiviral activity against HCV, with a selective index of 32.1. The 5 hit compounds with new scaffolds could potentially serve as NS5B polymerase inhibitors through further optimization and development.