TarFisDock is a web-based tool for automating the procedure of searching for small molecule–protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand–protein docking program. In contrast to conventional ligand–protein docking, reverse ligand–protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand–protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at .

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE−based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

Human infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause coronavirus disease 19 and there is currently no cure. The 3C-like protease (3CLpro), a highly conserved protease indispensable for replication of coronaviruses, is a promising target for development of broad-spectrum antiviral drugs. To advance the speed of drug discovery and development, we investigated the inhibition of SARS-CoV-2 3CLpro by natural products derived from Chinese traditional medicines. Baicalin and baicalein were identified as the first non-covalent, non-peptidomimetic inhibitors of SARS-CoV-2 3CLpro and exhibited potent antiviral activities in a cell-based system. Remarkably, the binding mode of baicalein with SARS-CoV-2 3CLpro determined by X-ray protein crystallography is distinctly different from those of known inhibitors. Baicalein is perfectly ensconced in the core of the substrate-binding pocket by interacting with two catalytic residues, the crucial S1/S2 subsites and the oxyanion loop, acting as a "shield" in front of the catalytic dyad to prevent the peptide substrate approaching the active site. The simple chemical structure, unique mode of action, and potent antiviral activities in vitro, coupled with the favorable safety data from clinical trials, emphasize that baicalein provides a great opportunity for the development of critically needed anti-coronaviral drugs.

Shikimate dehydrogenase (SDH) is the fourth enzyme involved in the shikimate pathway. It catalyzes the NADPH‐dependent reduction of 3‐dehydroshikimate to shikimate, and has been developed as a promising target for the discovery of antimicrobial agent. In this report, we identified a new aroE gene encoding SDH from Helicobacter pylori strain SS1. The recombinant H. pylori shikimate dehydrogenase (HpSDH) was cloned, expressed, and purified in Escherichia coli system. The enzymatic characterization of HpSDH demonstrates its activity with kcat of 7.7 s−1 and Km of 0.148 mm toward shikimate, kcat of 7.1 s−1 and Km of 0.182 mm toward NADP, kcat of 5.2 s−1 and Km of 2.9 mm toward NAD. The optimum pH of the enzyme activity is between 8.0 and 9.0, and the optimum temperature is around 60 °C. Using high throughput screening against our laboratory chemical library, five compounds, curcumin (1), 3‐(2‐naphthyloxy)‐4‐oxo‐2‐(trifluoromethyl)‐4H‐chromen‐7‐yl 3‐chlorobenzoate (2), butyl 2‐{[3‐(2‐naphthyloxy)‐4‐oxo‐2‐(trifluoromethyl)‐4H‐chromen‐7‐yl]oxy}propanoate (3), 2‐({2‐[(2‐{[2‐(2,3‐dimethylanilino)‐2‐oxoethyl]sulfanyl}‐1,3‐benzothiazol‐6‐yl)amino]‐2‐oxoethyl}sulfanyl)‐N‐(2‐naphthyl)acetamide (4), and maesaquinone diacetate (5) were discovered as HpSDH inhibitors with IC50 values of 15.4, 3.9, 13.4, 2.9, and 3.5 µm, respectively. Further investigation indicates that compounds 1, 2, 3, and 5 demonstrate noncompetitive inhibition pattern, and compound 4 displays competitive inhibition pattern with respect to shikimate. Compounds 1, 4, and 5 display noncompetitive inhibition mode, and compounds 2 and 3 show competitive inhibition mode with respect to NADP. Antibacterial assays demonstrate that compounds 1, 2, and 5 can inhibit the growth of H. pylori with MIC of 16, 16, and 32 µg·mL−1, respectively. This current work is expected to favor better understanding the features of SDH and provide useful information for the development of novel antibiotics to treat H. pylori‐associated infection.

Most low GC Gram-positive bacteria possess an essential walKR two-component system (TCS) for signal transduction involved in regulating cell wall homoeostasis. Despite the well-established intracellular regulatory mechanism, the role of this TCS in extracellular signal recognition and factors that modulate the activity of this TCS remain largely unknown. Here we identify the extracellular receptor of the kinase ‘WalK' (erWalK) as a key hub for bridging extracellular signal input and intracellular kinase activity modulation in Staphylococcus aureus. Characterization of the crystal structure of erWalK revealed a canonical Per-Arnt-Sim (PAS) domain for signal sensing. Single amino-acid mutation of potential signal-transduction residues resulted in severely impaired function of WalKR. A small molecule derived from structure-based virtual screening against erWalK is capable of selectively activating the walKR TCS. The molecular level characterization of erWalK will not only facilitate exploration of natural signal(s) but also provide a template for rational design of erWalK inhibitors.

Based on a homology model of the Kv1.3 potassium channel, the recognitions of the six scorpion toxins, viz. agitoxin2, charybdotoxin, kaliotoxin, margatoxin, noxiustoxin, and Pandinus toxin, to the human Kv1.3 potassium channel have been investigated by using an approach of the Brownian dynamics (BD) simulation integrating molecular dynamics (MD) simulation. Reasonable three-dimensional structures of the toxin-channel complexes have been obtained employing BD simulations and triplet contact analyses. All of the available structures of the six scorpion toxins in the Research Collaboratory for Structural Bioinformatics Protein Data Bank determined by NMR were considered during the simulation, which indicated that the conformations of the toxin significantly affect both the molecular recognition and binding energy between the two proteins. BD simulations predicted that all the six scorpion toxins in this study use their beta-sheets to bind to the extracellular entryway of the Kv1.3 channel, which is in line with the primary clues from the electrostatic interaction calculations and mutagenesis results. Additionally, the electrostatic interaction energies between the toxins and Kv1.3 channel correlate well with the binding affinities (-logK(d)s), R(2) = 0.603, suggesting that the electrostatic interaction is a dominant component for toxin-channel binding specificity. Most importantly, recognition residues and interaction contacts for the binding were identified. Lys-27 or Lys-28, residues Arg-24 or Arg-25 in the separate six toxins, and residues Tyr-400, Asp-402, His-404, Asp-386, and Gly-380 in each subunit of the Kv1.3 potassium channel, are the key residues for the toxin-channel recognitions. This is in agreement with the mutation results. MD simulations lasting 5 ns for the individual proteins and the toxin-channel complexes in a solvated lipid bilayer environment confirmed that the toxins are flexible and the channel is not flexible in the binding. The consistency between the results of the simulations and the experimental data indicated that our three-dimensional models of the toxin-channel complex are reasonable and can be used as a guide for future biological studies, such as the rational design of the blocking agents of the Kv1.3 channel and mutagenesis in both toxins and the Kv1.3 channel. Moreover, the simulation result demonstrates that the electrostatic interaction energies combined with the distribution frequencies from BD simulations might be used as criteria in ranking the binding configuration of a scorpion toxin to the Kv1.3 channel.

Aim: This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods. Methods: All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors α (ERα), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS. Results: Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS. Conclusion: The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery. discovery, especially in the cases when no three-dimensional (3D) structural information on the protein target of interest is available. Even when the 3D structures of targets are known, PBVS is also used as a complementary approach to DBVS for pre-processing databases (libraries) of small molecules to remove compounds not possessing features known to be essential for binding or for post-filtering compounds selected by docking approaches [5] . A recent case study by Muthas et al indicated that post-filtering with pharmacophores was shown to increase enrichment rates in their investigated targets compared with docking alone [6] . Several studies have been performed to assess various DBVS methods and compare which docking programs are the most successful in identifying active hits [7][8][9][10] . The conclusion is that no docking program may outperform other docking programs for all the tested targets, and the performance of each tested docking program is highly dependent on the nature of the target binding site [5] .Nevertheless, few case studies for a direct comparison on the performances between PBVS and DBVS have been reported [11] . To gain a general view for the discrimination between these two types of approach in prioritizing actives from a database with decoys, we performed a benchmark comparison between the performances of PBVS and DBVS. Eight structurally diverse protein targets were selected in this study. The pharmacophore models were generated from the ligand co-crystallized complex structures of these targets using the LigandScout program [12] , and each PBVS was performed using the program Catalyst [13,14] . To avoid the target dependency of docking pro...

The recently discovered 150-cavity (formed by loop residues 147–152, N2 numbering) adjacent to the enzymatic active site of group 1 influenza A neuraminidase (NA) has introduced a novel target for the design of next-generation NA inhibitors. However, only group 1 NAs, with the exception of the 2009 pandemic H1N1 NA, possess a 150-cavity, and no 150-cavity has been observed in group 2 NAs. The role of the 150-cavity played in enzymatic activity and inhibitor binding is not well understood. Here, we demonstrate for the first time that oseltamivir carboxylate can induce opening of the rigid closed N2 150-loop and provide a novel mechanism for 150-loop movement using molecular dynamics simulations. Our results provide the structural and biophysical basis of the open form of 150-loop and illustrates that the inherent flexibility and the ligand induced flexibility of the 150-loop should be taken into consideration for future drug design.