Structure of the Spring Viraemia of Carp Virus Ribonucleoprotein Complex Reveals Its Assembly Mechanism and Application in Antiviral Drug Screening.

Spring viremia of carp virus (SVCV) is a highly pathogenic Vesiculovirus infecting the common carp, yet neither a vaccine nor effective therapies are available to treat spring viremia of carp (SVC). Like all negative-sense viruses, SVCV contains an RNA genome that is encapsidated by the nucleoprotein (N) in the form of a ribonucleoprotein (RNP) complex, which serves as the template for viral replication and transcription. Here, the three-dimensional (3D) structure of SVCV RNP was resolved through cryo-electron microscopy (cryo-EM) at a resolution of 3.7 Å. RNP assembly was stabilized by N and C loops; RNA was wrapped in the groove between the N and C lobes with 9 nt nucleotide per protomer. Combined with mutational analysis, our results elucidated the mechanism of RNP formation. The RNA binding groove of SVCV N was used as a target for drug virtual screening, and it was found suramin had a good antiviral effect. This study provided insights into RNP assembly, and anti-SVCV drug screening was performed on the basis of this structure, providing a theoretical basis and efficient drug screening method for the prevention and treatment of SVC. IMPORTANCE Aquaculture accounts for about 70% of global aquatic products, and viral diseases severely harm the development of aquaculture industry. Spring viremia of carp virus (SVCV) is the pathogen causing highly contagious spring viremia of carp (SVC) disease in cyprinids, especially common carp (Cyprinus carpio), yet neither a vaccine nor effective therapies are available to treat this disease. In this study, we have elucidated the mechanism of SVCV ribonucleoprotein complex (RNP) formation by resolving the 3D structure of SVCV RNP and screened antiviral drugs based on the structure. It is found that suramin could competitively bind to the RNA binding groove and has good antiviral effects both in vivo and in vitro. Our study provides a template for rational drug discovery efforts to treat and prevent SVCV infections.

RALF peptide signaling controls the polytubey block in Arabidopsis.

Fertilization of an egg by multiple sperm (polyspermy) leads to lethal genome imbalance and chromosome segregation defects. In Arabidopsis thaliana, the block to polyspermy is facilitated by a mechanism that prevents polytubey (the arrival of multiple pollen tubes to one ovule). We show here that FERONIA, ANJEA, and HERCULES RECEPTOR KINASE 1 receptor-like kinases located at the septum interact with pollen tube-specific RALF6, 7, 16, 36, and 37 peptide ligands to establish this polytubey block. The same combination of RALF (rapid alkalinization factor) peptides and receptor complexes controls pollen tube reception and rupture inside the targeted ovule. Pollen tube rupture releases the polytubey block at the septum, which allows the emergence of secondary pollen tubes upon fertilization failure. Thus, orchestrated steps in the fertilization process in Arabidopsis are coordinated by the same signaling components to guarantee and optimize reproductive success.

ePharmer: An integrated and graphical software for pharmacophore-based virtual screening.

Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software.

Scutellaria baicalensis extract and baicalein inhibit replication of SARS-CoV-2 and its 3C-like protease in vitro.

COVID-19 has become a global pandemic and there is an urgent call for developing drugs against the virus (SARS-CoV-2). The 3C-like protease (3CLpro) of SARS-CoV-2 is a preferred target for broad spectrum anti-coronavirus drug discovery. We studied the anti-SARS-CoV-2 activity of S. baicalensis and its ingredients. We found that the ethanol extract of S. baicalensis and its major component, baicalein, inhibit SARS-CoV-2 3CLpro activity in vitro with IC50's of 8.52 µg/ml and 0.39 µM, respectively. Both of them inhibit the replication of SARS-CoV-2 in Vero cells with EC50's of 0.74 µg/ml and 2.9 µM, respectively. While baicalein is mainly active at the viral post-entry stage, the ethanol extract also inhibits viral entry. We further identified four baicalein analogues from other herbs that inhibit SARS-CoV-2 3CLpro activity at µM concentration. All the active compounds and the S. baicalensis extract also inhibit the SARS-CoV 3CLpro, demonstrating their potential as broad-spectrum anti-coronavirus drugs.

Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach.

Chinese herbal medicine (CHM) addresses complex diseases through polypharmacological interactions. However, systematic studies of herbal medicine pharmacology remain challenging due to the complexity of CHM ingredients and their interactions with various targets. In this study, we aim to address this challenge with computational approaches. We investigated the herb-target-disease associations of 197 commonly prescribed CHMs using the similarity ensemble approach and DisGeNET database. We demonstrated that this method can be applied to associate herbs with their putative targets. In the case study of three well-known herbs, Radix Glycyrrhizae, Flos Lonicerae, and Rhizoma Coptidis, approximately 70% of the predicted targets were supported by scientific literature. By linking 406 targets to 2439 annotated diseases, we further analyzed the pharmacological functions of 197 herbs. Finally, we proposed a strategy of target-oriented herbal formula design and illustrated the target profiles for four common chronic diseases, namely, Alzheimer's disease, depressive disorder, hypertensive disease, and non-insulin-dependent diabetes mellitus. This computational approach holds great potential in the target identification of herbs, understanding the molecular mechanisms of CHM, and designing novel herbal formulas.

Efficient ligand discovery from natural herbs by integrating virtual screening, affinity mass spectrometry and targeted metabolomics.

Although natural herbs have been a rich source of compounds for drug discovery, identification of bioactive components from natural herbs suffers from low efficiency and prohibitive cost of the conventional bioassay-based screening platforms. Here we develop a new strategy that integrates virtual screening, affinity mass spectrometry (MS) and targeted metabolomics for efficient discovery of herb-derived ligands towards a specific protein target site. Herb-based virtual screening conveniently selects herbs of potential bioactivity whereas affinity MS combined with targeted metabolomics readily screens candidate compounds in a high-throughput manner. This new integrated approach was benchmarked on screening chemical ligands that target the hydrophobic pocket of the nucleoprotein (NP) of Ebola viruses for which no small molecule ligands have been reported. Seven compounds identified by this approach from the crude extracts of three natural herbs were all validated to bind to the NP target in pure ligand binding assays. Among them, three compounds isolated from Piper nigrum (HJ-1, HJ-4 and HJ-6) strongly promoted the formation of large NP oligomers and reduced the protein thermal stability. In addition, cooperative binding between these chemical ligands and an endogenous peptide ligand was observed, and molecular docking was employed to propose a possible mechanism. Taken together, we established a platform integrating in silico and experimental screening approaches for efficient discovery of herb-derived bioactive ligands especially towards non-enzyme protein targets.

Herb-target interaction network analysis helps to disclose molecular mechanism of traditional Chinese medicine.

Though many studies have been performed to elucidate molecular mechanism of traditional Chinese medicines (TCMs) by identifying protein-compound interactions, no systematic analysis at herb level was reported. TCMs are prescribed by herbs and all compounds from a certain herb should be considered as a whole, thus studies at herb level may provide comprehensive understanding of TCMs. Here, we proposed a computational strategy to study molecular mechanism of TCM at herb level and used it to analyze a TCM anti-HIV formula. Herb-target network analysis was carried out between 17 HIV-related proteins and SH formula as well as three control groups based on systematic docking. Inhibitory herbs were identified and active compounds enrichment was found to contribute to the therapeutic effectiveness of herbs. Our study demonstrates that computational analysis of TCMs at herb level can catch the rationale of TCM formulation and serve as guidance for novel TCM formula design.

Identification of acylthiourea derivatives as potent Plk1 PBD inhibitors.

Thiourea derivatives have drawn much attention for their latent capacities of biological activities. In this study, we designed acylthiourea compounds as polo-like kinase 1 (Plk1) polo-box domain (PBD) inhibitors. A series of acylthiourea derivatives without pan assay interference structure (PAINS) were synthesized. Four compounds with halogen substituents exhibited binding affinities to Plk1 PBD in low micromole range. The most potent compound (3v) showed selectivity over other subtypes of Plk PBDs and inhibited the kinase activity of full-length Plk1.

Structure-based Inhibitor Design for the Intrinsically Disordered Protein c-Myc.

Intrinsically disordered proteins (IDPs) are associated with various diseases and have been proposed as promising drug targets. However, conventional structure-based approaches cannot be applied directly to IDPs, due to their lack of ordered structures. Here, we describe a novel computational approach to virtually screen for compounds that can simultaneously bind to different IDP conformations. The test system used c-Myc, an oncoprotein containing a disordered basic helix-loop-helix-leucine zipper (bHLH-LZ) domain that adopts a helical conformation upon binding to Myc-associated factor X (Max). For the virtual screen, we used three binding pockets in representative conformations of c-Myc370-409, which is part of the disordered bHLH-LZ domain. Seven compounds were found to directly bind c-Myc370-409 in vitro, and four inhibited the growth of the c-Myc-overexpressing cells by affecting cell cycle progression. Our approach of IDP conformation sampling, binding site identification, and virtual screening for compounds that can bind to multiple conformations provides a useful strategy for structure-based drug discovery targeting IDPs.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

Discovery of highly potent TNFα inhibitors using virtual screen.

Tumor necrosis factor-α (TNFα) is a validated therapeutic target for various autoimmune disorders such as rheumatoid arthritis and asthma. All TNFα inhibitors currently on the market are biologics, making the development of small molecule alternatives in urgent need. However, only a few successful cases of direct TNFα antagonization in vitro have been reported. Here, we present the identification of several small molecule candidates able to effectively reduce TNFα activity in vitro and in cell assays. Virtual screen targeting TNFα dimer was performed on the SPECS database and 101 compounds were selected for experimental testing. Two compounds, 1 and 2, displayed considerable inhibitory activity. Follow-up structure-activity relationship analysis of compound 1 identified 3 molecules with low micromolar cell-level inhibitory activity. Compound 11 showed an IC50 value of 14 µM, making it among the most potent TNFα small molecule inhibitors reported. These compounds provide new scaffolds for future development of small molecule drugs against TNFα.

Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening.

Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets.

A protein engineered to bind uranyl selectively and with femtomolar affinity.

Uranyl (UO2(2+)), the predominant aerobic form of uranium, is present in the ocean at a concentration of ~3.2 parts per 10(9) (13.7 nM); however, the successful enrichment of uranyl from this vast resource has been limited by the high concentrations of metal ions of similar size and charge, which makes it difficult to design a binding motif that is selective for uranyl. Here we report the design and rational development of a uranyl-binding protein using a computational screening process in the initial search for potential uranyl-binding sites. The engineered protein is thermally stable and offers very high affinity and selectivity for uranyl with a Kd of 7.4 femtomolar (fM) and >10,000-fold selectivity over other metal ions. We also demonstrated that the uranyl-binding protein can repeatedly sequester 30-60% of the uranyl in synthetic sea water. The chemical strategy employed here may be applied to engineer other selective metal-binding proteins for biotechnology and remediation applications.

Understanding molecular mechanisms of traditional Chinese medicine for the treatment of influenza viruses infection by computational approaches.

The battle against influenza is an enduring one. For hundreds of years, people have fought such small viruses with practices such as traditional Chinese medicine (TCM), however only recently has it been possible to use cutting-edge technology to investigate their mechanisms. Here, we re-created this ancient Chinese knowledge to explore the chemistry of herbs and elucidate their mechanism of action using molecular computational methods. Our results show that TCM compounds can inhibit influenza viral proteins in a multi-target/multi-component manner, revealing the versatility of TCM for treating different influenza virus subtypes, including the recently emerged H7N9.

Understanding traditional Chinese medicine anti-inflammatory herbal formulae by simulating their regulatory functions in the human arachidonic acid metabolic network.

Through history, traditional Chinese medicine (TCM) has adopted oriental philosophical practices of drug combination and interaction to address human diseases. To investigate this from a systems biology point of view, we analysed 28 TCM herbs for their anti-inflammatory function, using molecular docking and arachidonic acid (AA) metabolic network simulation. The inhibition potential of each herb toward five essential enzymes as well as their possible side effects were examined. Three commonly prescribed anti-inflammatory formulae were simulated to discover the combinatorial properties of each contained herb in regulating the whole metabolic network. We discovered that different ingredients of a formula tend to inhibit different targets, which almost covered all the targets in the whole network. We also found that herbal combinations could achieve the same therapeutic effect at lower doses compared with individual usage. New herbal combinations were also predicted based on the inhibition potentials and two types of synergistic drug combinations of TCM theory were discussed from the perspective of systems biology. Using this combined approach of molecular docking and network simulation, we were able to computationally elucidate the combinatorial effects of TCM to intervene disease networks. We expect novel TCM formulae or modern drug combinations to be developed based on this research.

Discovery of highly potent microsomal prostaglandin e2 synthase 1 inhibitors using the active conformation structural model and virtual screen.

Microsomal prostaglandin E2 synthase 1 (mPGES-1) has been identified as a promising drug target due to its key role in prostaglandin biosynthesis. However, the lack of a well-characterized structure constitutes a great challenge for the development of inhibitors. Recently, we have built a model for the active conformation of mPGES-1. In the present study, the model was used for structure-based virtual screen of novel mPGES-1 inhibitors. Of the 142 compounds tested in the cell-free assay, 10 molecules are highly potent with IC50 values of single digit nanomolar and the strongest inhibition of 1.1 nM. Moreover, nine compounds showed strong activity in the human whole blood (HWB) assay with IC50 values of less than 10 µM. The lead compounds 1 and 2 showed HWB IC50 values of 0.3 and 0.7 µM which are among the most potent mPGES-1 inhibitors reported. These compounds represent new scaffolds for future development of drugs against mPGES-1.

Identification of small-molecule inhibitors against human leukocyte antigen-death receptor 4 (HLA-DR4) through a comprehensive strategy.

Rheumatoid arthritis (RA) is an autoimmune disease mediated by T-lymphocytes and associated with the human leukocyte antigen-death receptor 4 (HLA-DR4). The HLA-DR4 protein selectively interacts with the antigenic peptides on the cell surface and presents them to the T cell receptor (TCR) on CD4+ T cells. The HLA-DR4-antigen-TCR complex initiates the autoimmune response and eventually causes the chronic inflammation within patients bodies. To inhibit HLA-DR4-restricted T cell activation, an ideal approach is to discover non-T cell stimulating substrates that specifically bind to HLA-DR4. In this paper, a comprehensive structure-based design strategy involved de novo design approach, pharmacophore search, and dock method was presented and applied to "simplify" the known binding peptide ligand of HLA-DR4 and identified specific small-molecule inhibitors for HLA-DR4. The designed three-step strategy successfully identified five nonpeptide ligands with novel scaffolds from a chemical library containing 4 × 10(6) commercially available compounds within a tolerable computing time. The identified five chemicals, BAS-0219606, T0506-2494, 6436645, 3S-71981, and KM 11073, are all non-T cell stimulators and are able to significantly inhibit HLA-DR4-restricted T cell activation induced by type II collagen (CII) 263-272 peptide. IC(50) for the best two potentials, BAS-0219606 and T0506-2494, was 31 and 17 µM, respectively, which is equivalent or better than the known peptide ligands. It is hopeful that they can be used as effective therapeutic means for further treatment of RA patients. In addition, the comprehensive strategy presented in this paper exhibited itself to be an effective flow line from peptide ligands to small-molecule inhibitors and will have applications to other targets.

Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential.

MOTIVATION: Experimental evidence suggests that certain short protein segments have stronger amyloidogenic propensities than others. Identification of the fibril-forming segments of proteins is crucial for understanding diseases associated with protein misfolding and for finding favorable targets for therapeutic strategies. RESULT: In this study, we used the microcrystal structure of the NNQQNY peptide from yeast prion protein and residue-based statistical potentials to establish an algorithm to identify the amyloid fibril-forming segment of proteins. Using the same sets of sequences, a comparable prediction performance was obtained from this study to that from 3D profile method based on the physical atomic-level potential ROSETTADESIGN. The predicted results are consistent with experiments for several representative proteins associated with amyloidosis, and also agree with the idea that peptides that can form fibrils may have strong sequence signatures. Application of the residue-based statistical potentials is computationally more efficient than using atomic-level potentials and can be applied in whole proteome analysis to investigate the evolutionary pressure effect or forecast other latent diseases related to amyloid deposits. AVAILABILITY: The fibril prediction program is available at ftp://mdl.ipc.pku.edu.cn/pub/software/pre-amyl/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quaternary structure, substrate selectivity and inhibitor design for SARS 3C-like proteinase.

The SARS coronavirus 3C-like proteinase is recognized as a potential drug design target for the treatment of severe acute respiratory syndrome. In the past few years, much work has been done to understand the catalytic mechanism of this target protein and to design its selective inhibitors. The protein exists as a dimer/monomer mixture in solution and the dimer was confirmed to be the active species for the enzyme reaction. Quantitative dissociation constants have been reported for the dimer by using analytic ultracentrifuge, gel filtration and enzyme assays. Though the enzyme is a cysteine protease with a chymotrypsin fold, SARS 3C-like proteinase follows the general base catalytic mechanism similar to chymotrypsin. As the enzyme can cut eleven different sites on the viral polyprotein, the substrate specificity has been studied by synthesized peptides corresponding or similar to the cleavage sites on the polyprotein. Predictive model was built for substrate structure and activity relationships and can be applied in inhibitor design. Due to the lack of potential drugs for the treatment of SARS, the discovery of inhibitors against SARS 3C-like proteinase, which can potentially be optimized as drugs appears to be highly desirable. Various groups have been working on inhibitor discovery by virtual screening, compound library screening, modification of existing compounds or natural products. High-throughput in vitro assays, auto-cleavage assays and viral replication assays have been developed for inhibition activity tests. Inhibitors with IC50 values as low as 60 nM have been reported.

The interaction between severe acute respiratory syndrome coronavirus 3C-like proteinase and a dimeric inhibitor by capillary electrophoresis.

3C-like proteinase of severe acute respiratory syndrome (SARS) coronavirus has been demonstrated to be a key target for drug design against SARS. The interaction between SARS coronavirus 3C-like (3CL) proteinase and an octapeptide interface inhibitor was studied by affinity capillary electrophoresis (ACE). The binding constants were estimated by the change of migration time of the analytes in the buffer solution containing different concentrations of SARS 3CL proteinase. The results showed that SARS 3CL proteinase was able to complex with the octapeptide competitively, with binding constants of 2.44 x 10(4) M(-1) at 20 degrees C and 2.11 x 10(4)M(-1) at 37 degrees C. In addition, the thermodynamic parameters deduced reveal that hydrophobic interaction might play major roles, along with electrostatic force, in the binding process. The ACE method used here could be developed to be an effective and simple way of applying large-scale drug screening and evaluation.