SiMMap: a web server for inferring site-moiety map to recognize interaction preferences between protein pockets and compound moieties

Combining virtual screening with cis-/trans-cleavage enzymatic assays effectively reveals broad-spectrum inhibitors that target the main proteases of SARS-CoV-2 and MERS-CoV

The main protease (M^pro) of SARS-CoV-2 is essential for viral replication, which suggests that the M^pro is a critical target in the development of small molecules to treat COVID-19. This study used an in-silico prediction approach to investigate the complex structure of SARS-CoV-2 M^pro in compounds from the United States National Cancer Institute (NCI) database, then validate potential inhibitory compounds against the SARS-CoV-2 M^pro in cis- and trans-cleavage proteolytic assays. Virtual screening of ∼280,000 compounds from the NCI database identified 10 compounds with highest site-moiety map scores. Compound NSC89640 (coded C1) showed marked inhibitory activity against the SARS-CoV-2 M^pro in cis-/trans-cleavage assays. C1 strongly inhibited SARS-CoV-2 M^pro enzymatic activity, with a half maximal inhibitory concentration (IC₅₀) of 2.69 μM and a selectivity index (SI) of >74.35. The C1 structure served as a template to identify structural analogs based on AtomPair fingerprints to refine and verify structure-function associations. M^pro-mediated cis-/trans-cleavage assays conducted with the structural analogs revealed that compound NSC89641 (coded D2) exhibited the highest inhibitory potency against SARS-CoV-2 M^pro enzymatic activity, with an IC₅₀ of 3.05 μM and a SI of >65.57. Compounds C1 and D2 also displayed inhibitory activity against MERS-CoV-2 with an IC₅₀ of <3.5 μM. Thus, C1 shows potential as an effective M^pro inhibitor of SARS-CoV-2 and MERS-CoV. Our rigorous study framework efficiently identified lead compounds targeting the SARS-CoV-2 M^pro and MERS-CoV M^pro.

Antiviral Approaches against Influenza Virus

Preventing and controlling influenza virus infection remains a global public health challenge, as it causes seasonal epidemics to unexpected pandemics. These infections are responsible for high morbidity, mortality, and substantial economic impact. Vaccines are the prophylaxis mainstay in the fight against influenza. However, vaccination fails to confer complete protection due to inadequate vaccination coverages, vaccine shortages, and mismatches with circulating strains. Antivirals represent an important prophylactic and therapeutic measure to reduce influenza-associated morbidity and mortality, particularly in high-risk populations. Here, we review current FDA-approved influenza antivirals with their mechanisms of action, and different viral- and host-directed influenza antiviral approaches, including immunomodulatory interventions in clinical development. Furthermore, we also illustrate the potential utility of machine learning in developing next-generation antivirals against influenza.

Structure-based virtual screening for potent inhibitors of GH-20 β-N-acetylglucosaminidase: classical and machine learning scoring functions, and molecular dynamics simulations

GH-20 β-N-acetylglucosaminidases (GlcNAcases) are promising targets in the development of antimicrobial agents against Vibrio infections in humans and aquatic animals. In this study, we set up structure-based virtual screening to identify potential GH-20 GlcNAcase inhibitors from the Reaxys commercial database, using VhGlcNAcase from V. campbellii type strain ATCC® BAA 1116 as the protein target and Redoxal as the reference ligand. Using ChemPLP and RF-Score-VS machine learning scoring functions, eight lead compounds were identified and further evaluated for protein interaction preference and pharmacological properties. Protein-ligand analysis demonstrated that all selected compounds interacted exclusively at subsite - 1 with five hydrophobic residues W487, W505, W546, W582 and V544 at site S1, and with two polar residues, D437 and E438, at site 3. For subsite + 1, the most common residues were R274 and E584 at site 2 and I397 and Q398 at site 4. Based on the data obtained from binding free energy changes (ΔG°_binding), pharmacological property analysis and molecular dynamic simulations, two ChemPLP compounds, 338175 and 1146525, and one RF-Score-VS compound, 337447, were considered as the likely lead compounds. The most promising compound, 1146525, could serve as a scaffold for the future design of novel antimicrobial agents against Vibrio infections.

In vitro characterization of a small molecule PD-1 inhibitor that targets the PD-l/PD-L1 interaction

Targeting the programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) axis with monoclonal antibodies (mAbs) represents a crucial breakthrough in anticancer therapy, but mAbs are limited by their poor oral bioavailability, adverse events in multiple organ systems, and primary, adaptive, and acquired resistance, amongst other issues. More recently, the advent of small molecule inhibitors that target the PD-1/PD-L1 axis have shown promising cellular inhibitory activity and the potential to counteract the disadvantages of mAbs. In this study, structure-based virtual screening identified small molecule inhibitors that effectively inhibited the PD-1/PD-L1 interaction. Six of those small molecule inhibitors were applied to cell-based experiments targeting PD-1: CH-1, CH-2, CH-3, CH-4, CH-5, and CH-6. Of all 6, CH-4 displayed the lowest cytotoxicity and strongest inhibitory activity towards the PD-1/PD-L1 interaction. The experiments revealed that CH-4 inhibited the interaction of soluble form PD-L1 (sPD-L1) with PD-1 surface protein expressed by KG-1 cells. Investigations into CH-4 analogs revealed that CH-4.7 effectively blocked the PD-1/sPD-L1 interaction, but sustained the secretion of interleukin-2 and interferon-γ by Jurkat cells. Our experiments revealed a novel small molecule inhibitor that blocks the interaction of PD-1/sPD-L1 and potentially offers an alternative PD-1 target for immune checkpoint therapy.

Pharmacophore anchor models of ATAT1 to discover potential inhibitors and lead optimization

Post-translation modification of microtubules is associated with many diseases like cancer. Alpha Tubulin Acetyltransferase 1 (ATAT1) is a major enzyme that acetylates 'Lys-40' in alpha-tubulin on the luminal side of microtubules and is a drug target that lacks inhibitors. Here, we developed pharmacophore anchor models of ATAT1 which were constructed statistically using thousands of docked compounds, for drug design and investigating binding mechanisms. Our models infer the compound moiety preferences with the physico-chemical properties for the ATAT1 binding site. The results from the pharmacophore anchor models show the three main sub-pockets, including S1 acetyl site, S2 adenine site, and S3 diphosphate site with anchors, where conserved moieties interact with respective sub-pocket residues in each site and help in guiding inhibitor discovery. We validated these key anchors by analyzing 162 homologous protein sequences (>99 species) and over 10 structures with various bound ligands and mutations. Our results were consistent with previous works also providing new interesting insights. Our models applied in virtual screening predicted several ATAT1 potential inhibitors. We believe that our model is useful for future inhibitor discovery and for guiding lead optimization.

Potent sialic acid inhibitors that target influenza A virus hemagglutinin

Eradicating influenza A virus (IAV) is difficult, due to its genetic drift and reassortment ability. As the infectious cycle is initiated by the influenza glycoprotein, hemagglutinin (HA), which mediates the binding of virions to terminal sialic acids moieties, HA is a tempting target of anti-influenza inhibitors. However, the complexity of the HA structure has prevented delineation of the structural characterization of the HA protein–ligand complex. Our computational strategy efficiently analyzed > 200,000 records of compounds held in the United States National Cancer Institute (NCI) database and identified potential HA inhibitors, by modeling the sialic acid (SA) receptor binding site (RBS) for the HA structure. Our modeling revealed that compound NSC85561 showed significant antiviral activity against the IAV H1N1 strain with EC₅₀ values ranging from 2.31 to 2.53 µM and negligible cytotoxicity (CC₅₀ > 700 µM). Using the NSC85561 compound as the template to generate 12 derivatives, robust bioassay results revealed the strongest antiviral efficacies with NSC47715 and NSC7223. Virtual screening clearly identified three SA receptor binding site inhibitors that were successfully validated in experimental data. Thus, our computational strategy has identified SA receptor binding site inhibitors against HA that show IAV-associated antiviral activity.

Uncovering Flexible Active Site Conformations of SARS-CoV-2 3CL Proteases through Protease Pharmacophore Clusters and COVID-19 Drug Repurposing

The infectious SARS-CoV-2 causes COVID-19, which is now a global pandemic. Aiming for effective treatments, we focused on the key drug target, the viral 3C-like (3CL) protease. We modeled a big dataset with 42 SARS-CoV-2 3CL protease-ligand complex structures from ∼98.7% similar SARS-CoV 3CL protease with abundant complex structures. The diverse flexible active site conformations identified in the dataset were clustered into six protease pharmacophore clusters (PPCs). For the PPCs with distinct flexible protease active sites and diverse interaction environments, we identified pharmacophore anchor hotspots. A total of 11 "PPC consensus anchors" (a distinct set observed in each PPC) were observed, of which three "PPC core anchors" EHV2, HV1, and V3 are strongly conserved across PPCs. The six PPC cavities were then applied in virtual screening of 2122 FDA drugs for repurposing, using core anchor-derived "PPC scoring S" to yield seven drug candidates. Experimental testing by SARS-CoV-2 3CL protease inhibition assay and antiviral cytopathic effect assays discovered active hits, Boceprevir and Telaprevir (HCV drugs) and Nelfinavir (HIV drug). Specifically, Boceprevir showed strong protease inhibition with micromolar IC₅₀ of 1.42 μM and an antiviral activity with EC₅₀ of 49.89 μM, whereas Telaprevir showed moderate protease inhibition only with an IC₅₀ of 11.47 μM. Nelfinavir solely showed antiviral activity with a micromolar EC₅₀ value of 3.28 μM. Analysis of binding mechanisms of protease inhibitors revealed the role of PPC core anchors. Our PPCs revealed the flexible protease active site conformations, which successfully enabled drug repurposing.

A site-moiety map and virtual screening approach for discovery of novel 5-LOX inhibitors

The immune system works in conjunction with inflammation. Excessive inflammation underlies various human diseases, such as asthma, diabetes and heart disease. Previous studies found that 5-lipoxygenase (5-LOX) plays a crucial role in metabolizing arachidonic acid into inflammatory mediators and is a potential therapeutic target. In this study, we performed an in silico approach to establish a site-moiety map (SiMMap) to screen for new 5-LOX inhibitors. The map is composed of several anchors that contain key residues, moiety preferences, and their interaction types (i.e., electrostatic (E), hydrogen-bonding (H), and van der Waals (V) interactions) within the catalytic site. In total, we identified one EH, one H, and five V anchors, within the 5-LOX catalytic site. Based on the SiMMap, three 5-LOX inhibitors (YS1, YS2, and YS3) were identified. An enzyme-based assay validated inhibitory activity of YS1, YS2, and YS3 against 5-LOX with an IC₅₀ value of 2.7, 4.2, and 5.3 μM, respectively. All three inhibitors significantly decrease LPS-induced TNF-α and IL-6 production, which suggests its potential use an anti-inflammatory agent. In addition, the identified 5-LOX inhibitors contain a novel scaffold. The discovery of these inhibitors presents an opportunity for designing specific anti-inflammatory drugs.

Zika Virus NS3 Protease Pharmacophore Anchor Model and Drug Discovery

Zika virus (ZIKV) of the flaviviridae family, is the cause of emerging infections characterized by fever, Guillain-Barré syndrome (GBS) in adults and microcephaly in newborns. There exists an urgent unmet clinical need for anti-ZIKV drugs for the treatment of infected individuals. In the current work, we aimed at the promising virus drug target, ZIKV NS3 protease and constructed a Pharmacophore Anchor (PA) model for the active site. The PA model reveals a total of 12 anchors (E, H, V) mapped across the active site subpockets. We further identified five of these anchors to be critical core anchors (CEH1, CH3, CH7, CV1, CV3) conserved across flaviviral proteases. The ZIKV protease PA model was then applied in anchor-enhanced virtual screening yielding 14 potential antiviral candidates, which were tested by in vitro assays. We discovered FDA drugs Asunaprevir and Simeprevir to have potent anti-ZIKV activities with EC₅₀ values 4.7 µM and 0.4 µM, inhibiting the viral protease with IC₅₀ values 6.0 µM and 2.6 µM respectively. Additionally, the PA model anchors aided in the exploration of inhibitor binding mechanisms. In conclusion, our PA model serves as a promising guide map for ZIKV protease targeted drug discovery and the identified ‘previr’ FDA drugs are promising for anti-ZIKV treatments.

Alternative splicing in human cancer cells is modulated by the amiloride derivative 3,5-diamino-6-chloro-N-(N-(2,6-dichlorobenzoyl)carbamimidoyl)pyrazine-2-carboxide

Alternative splicing (AS) is a process that enables the generation of multiple protein isoforms with different biological properties from a single mRNA. Cancer cells often use the maneuverability conferred by AS to produce proteins that contribute to growth and survival. In our previous studies, we identified that amiloride modulates AS in cancer cells. However, the effective concentration of amiloride required to modulate AS is too high for use in cancer treatment. In this study, we used computational algorithms to screen potential amiloride derivatives for their ability to regulate AS in cancer cells. We found that 3,5-diamino-6-chloro-N-(N-(2,6-dichlorobenzoyl)carbamimidoyl)pyrazine-2-carboxamide (BS008) can regulate AS of apoptotic gene transcripts, including HIPK3, SMAC, and BCL-X, at a lower concentration than amiloride. This splicing regulation involved various splicing factors, and it was accompanied by a change in the phosphorylation state of serine/arginine-rich proteins (SR proteins). RNA sequencing was performed to reveal that AS of many other apoptotic gene transcripts, such as AATF, ATM, AIFM1, NFKB1, and API5, was also modulated by BS008. In vivo experiments further indicated that treatment of tumor-bearing mice with BS008 resulted in a marked decrease in tumor size. BS008 also had inhibitory effects in vitro, either alone or in a synergistic combination with the cytotoxic chemotherapeutic agents sorafenib and nilotinib. BS008 enabled sorafenib dose reduction without compromising antitumor activity. These findings suggest that BS008 may possess therapeutic potential for cancer treatment.

Targeting dePARylation selectively suppresses DNA repair-defective and PARP inhibitor-resistant malignancies

While poly(ADP-ribosyl)ation (PARylation) plays an important role in DNA repair, the role of dePARylation in DNA repair remains elusive. Here, we report that a novel small molecule identified from the NCI database, COH34, specifically inhibits poly(ADP-ribose) glycohydrolase (PARG), the major dePARylation enzyme, with nanomolar potency in vitro and in vivo. COH34 binds to the catalytic domain of PARG, thereby prolonging PARylation at DNA lesions and trapping DNA repair factors. This compound induces lethality in cancer cells with DNA repair defects and exhibits antitumor activity in xenograft mouse cancer models. Moreover, COH34 can sensitize tumor cells with DNA repair defects to other DNA-damaging agents, such as topoisomerase I inhibitors and DNA-alkylating agents, which are widely used in cancer chemotherapy. Notably, COH34 also efficiently kills PARP inhibitor-resistant cancer cells. Together, our study reveals the molecular mechanism of PARG in DNA repair and provides an effective strategy for future cancer therapies.

Bioinformatics characterization of a cathepsin B transcript from the giant river prawn, Macrobrachium rosenbergii: Homology modeling and expression analysis after Aeromonas hydrophila infection

Cathepsin B is a lysosomal proteolytic enzyme that has been suggested to play a role in pathological processes of immune system. In this study, the full-length cDNA sequence of cathepsin B transcript in the giant river prawn Macrobrachium rosenbergii (MrCTSB) was obtained from 454 pyrosequencing of cDNAs from hepatopancreas and muscle. It was 1158 bp in length, containing an open reading frame (ORF) of 987 bp corresponding to 328 amino acids. The predicted molecular mass and pI of MrCTSB protein was 36.04 kDa and 4.73. The major characteristics of MrCTSB protein consisted of a propeptide of C1 peptidase family at the N-terminus and a cysteine protease (Pept_C1) domain at the C-terminus. The 3-dimentional structure of MrCTSB was constructed by computer-assisted homology modeling. The folding of MrCTSB was highly conserved to human CTSB structure and the modeled MrCTSB displayed characteristics of cysteine proteinases superfamily. The docking study was performed to investigate binding interactions between known inhibitors against MrCTSB. Known inhibitors were oriented in the groove of catalytic site cleft. They bound to subsites from S2, S1, S1', and S2', respectively, with key residues in each subsite. Challenge of juvenile prawns with Aeromonas hydrophila revealed that the MrCTSB transcript in hepatopancreas significantly increased at 60-96 h post injection (hpi). This suggested that MrCTSB may play roles in innate immunity of M. rosenbergii. Our results provide useful information for a more comprehensive study in immune-related functions of MrCTSB.

Pharmacophore anchor models of flaviviral NS3 proteases lead to drug repurposing for DENV infection

Background

Viruses of the flaviviridae family are responsible for some of the major infectious viral diseases around the world and there is an urgent need for drug development for these diseases. Most of the virtual screening methods in flaviviral drug discovery suffer from a low hit rate, strain-specific efficacy differences, and susceptibility to resistance. It is because they often fail to capture the key pharmacological features of the target active site critical for protein function inhibition. So in our current work, for the flaviviral NS3 protease, we summarized the pharmacophore features at the protease active site as anchors (subsite-moiety interactions).

Results

For each of the four flaviviral NS3 proteases (i.e., HCV, DENV, WNV, and JEV), the anchors were obtained and summarized into ‘Pharmacophore anchor (PA) models’. To capture the conserved pharmacophore anchors across these proteases, were merged the four PA models. We identified five consensus core anchors (CEH1, CH3, CH7, CV1, CV3) in all PA models, represented as the “Core pharmacophore anchor (CPA) model” and also identified specific anchors unique to the PA models. Our PA/CPA models complied with 89 known NS3 protease inhibitors. Furthermore, we proposed an integrated anchor-based screening method using the anchors from our models for discovering inhibitors. This method was applied on the DENV NS3 protease to screen FDA drugs discovering boceprevir, telaprevir and asunaprevir as promising anti-DENV candidates. Experimental testing against DV2-NGC virus by in-vitro plaque assays showed that asunaprevir and telaprevir inhibited viral replication with EC₅₀ values of 10.4 μM & 24.5 μM respectively. The structure-anchor-activity relationships (SAAR) showed that our PA/CPA model anchors explained the observed in-vitro activities of the candidates. Also, we observed that the CEH1 anchor engagement was critical for the activities of telaprevir and asunaprevir while the extent of inhibitor anchor occupation guided their efficacies.

Conclusion

These results validate our NS3 protease PA/CPA models, anchors and the integrated anchor-based screening method to be useful in inhibitor discovery and lead optimization, thus accelerating flaviviral drug discovery.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1957-5) contains supplementary material, which is available to authorized users.

Identification of neuraminidase inhibitors against dual H274Y/I222R mutant strains

Influenza is an annual seasonal epidemic that has continually drawn public attentions, due to the potential death toll and drug resistance. Neuraminidase, which is essential for the spread of influenza virus, has been regarded as a valid target for the treatment of influenza infection. Although neuraminidase drugs have been developed, they are susceptible to drug-resistant mutations in the sialic-binding site. In this study, we established computational models (site-moiety maps) of H1N1 and H5N1 to determine properties of the 150-cavity, which is adjacent to the drug-binding site. The models reveal that hydrogen-bonding interactions with residues R118, D151, and R156 and van der Waals interactions with residues Q136, D151, and T439 are important for identifying 150-cavitiy inhibitors. Based on the models, we discovered three new inhibitors with IC₅₀ values <10 μM that occupies both the 150-cavity and sialic sites. The experimental results identified inhibitors with similar activities against both wild-type and dual H274Y/I222R mutant neuraminidases and showed little cytotoxic effects. Furthermore, we identified three new inhibitors situated at the sialic-binding site with inhibitory effects for normal neuraminidase, but lowered effects for mutant strains. The results suggest that the new inhibitors can be used as a starting point to combat drug-resistant strains.

Exploring the chemical space of influenza neuraminidase inhibitors

The fight against the emergence of mutant influenza strains has led to the screening of an increasing number of compounds for inhibitory activity against influenza neuraminidase. This study explores the chemical space of neuraminidase inhibitors (NAIs), which provides an opportunity to obtain further molecular insights regarding the underlying basis of their bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the literature. Molecular and quantum chemical descriptors were obtained from low-energy conformational structures geometrically optimized at the PM6 level. The bioactivities of NAIs were classified as active or inactive according to their half maximum inhibitory concentration (IC₅₀) value in which IC₅₀ < 1µM and ≥ 10µM were defined as active and inactive compounds, respectively. Interpretable decision rules were derived from a quantitative structure–activity relationship (QSAR) model established using a set of substructure descriptors via decision tree analysis. Univariate analysis, feature importance analysis from decision tree modeling and molecular scaffold analysis were performed on both data sets for discriminating important structural features amongst active and inactive NAIs. Good predictive performance was achieved as deduced from accuracy and Matthews correlation coefficient values in excess of 81% and 0.58, respectively, for both influenza A and B NAIs. Furthermore, molecular docking was employed to investigate the binding modes and their moiety preferences of active NAIs against both influenza A and B neuraminidases. Moreover, novel NAIs with robust binding fitness towards influenza A and B neuraminidase were generated via combinatorial library enumeration and their binding fitness was on par or better than FDA-approved drugs. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.

Navigating the chemical space of dipeptidyl peptidase-4 inhibitors

This study represents the first large-scale study on the chemical space of inhibitors of dipeptidyl peptidase-4 (DPP4), which is a potential therapeutic protein target for the treatment of diabetes mellitus. Herein, a large set of 2,937 compounds evaluated for their ability to inhibit DPP4 was compiled from the literature. Molecular descriptors were generated from the geometrically optimized low-energy conformers of these compounds at the semiempirical AM1 level. The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 μM and greater than 10 μM, respectively. Decision tree analysis revealed the importance of molecular weight, total energy of a molecule, topological polar surface area, lowest unoccupied molecular orbital, and number of hydrogen-bond donors, which correspond to molecular size, energy, surface polarity, electron acceptors, and hydrogen bond donors, respectively. The prediction model was subjected to rigorous independent testing via three external sets. Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties. Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues. The results of this study are anticipated to be useful in guiding the rational design of novel and robust DPP4 inhibitors for the treatment of diabetes.

Anchor-based classification and type-C inhibitors for tyrosine kinases

Tyrosine kinases regulate various biological processes and are drug targets for cancers. At present, the design of selective and anti-resistant inhibitors of kinases is an emergent task. Here, we inferred specific site-moiety maps containing two specific anchors to uncover a new binding pocket in the C-terminal hinge region by docking 4,680 kinase inhibitors into 51 protein kinases, and this finding provides an opportunity for the development of kinase inhibitors with high selectivity and anti-drug resistance. We present an anchor-based classification for tyrosine kinases and discover two type-C inhibitors, namely rosmarinic acid (RA) and EGCG, which occupy two and one specific anchors, respectively, by screening 118,759 natural compounds. Our profiling reveals that RA and EGCG selectively inhibit 3% (EGFR and SYK) and 14% of 64 kinases, respectively. According to the guide of our anchor model, we synthesized three RA derivatives with better potency. These type-C inhibitors are able to maintain activities for drug-resistant EGFR and decrease the invasion ability of breast cancer cells. Our results show that the type-C inhibitors occupying a new pocket are promising for cancer treatments due to their kinase selectivity and anti-drug resistance.

Computational methods for analysis and inference of kinase/inhibitor relationships

The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies.

Peptide inhibitors against dengue virus infection

Dengue virus (DENV) infection has become a public health problem worldwide. The development of anti-DENV drug is urgently needed because neither licensed vaccine nor specific drug is currently available. Inhibition of DENV attachment and entry to host cells by blocking DENV envelope (E) protein is an attractive strategy for anti-DENV drug development. A hydrophobic pocket on the DENV E protein is essential for structural transition in the membrane fusion, and inhibition of this process is able to inhibit DENV infection. To search for a safe anti-DENV drug, we identified short peptides targeting the hydrophobic pocket by molecular docking. In addition, the information of predicted ligand-binding site of reported active compounds of DENV2 hydrophobic pocket was also used for peptide inhibitors selection. The di-peptide, EF, was the most effective on DENV2 infection inhibition in vitro with a half maximal inhibition concentration (IC50) of 96 μm. Treatment of DENV2 with EF at the concentration of 200 μm resulted in 83.47% and 84.15% reduction in viral genome and intracellular E protein, respectively. Among four DENV serotypes, DENV2 was the most effective for the inhibition. Our results provide the proof of concept for the development of therapeutic peptide inhibitors against DENV infection by the computer-aided molecular design.

Homology modeling and virtual screening for antagonists of protease from yellow head virus

Yellow head virus (YHV) is one of the causative agents of shrimp viral disease. The prevention of YHV infection in shrimp has been developed by various methods, but it is still insufficient to protect the mass mortality in shrimp. New approaches for the antiviral drug development for viral infection have been focused on the inhibition of several potent viral enzymes, and thus the YHV protease is one of the interesting targets for developing antiviral drugs according to the pivotal roles of the enzyme in an early stage of viral propagation. In this study, a theoretical modeling of the YHV protease was constructed based on the folds of several known crystal structures of other viral proteases, and was subsequently used as a target for virtual screening—molecular docking against approximately 1364 NCI structurally diversity compounds. A complex between the protease and the hit compounds was investigated for intermolecular interactions by molecular dynamics simulations. Five best predicted compounds (NSC122819, NSC345647, NSC319990, NSC50650, and NSC5069) were tested against bacterial expressed YHV. The NSC122819 showed the best inhibitory characteristic among the candidates, while others showed more than 50 % of inhibition in the assay condition. These compounds could potentially be inhibitors for curing YHV infection.

Moiety-linkage map reveals selective nonbisphosphonate inhibitors of human geranylgeranyl diphosphate synthase

Bisphosphonates are potent inhibitors of farnesyl pyrophosphate synthase (FPPS) and geranylgeranyl diphosphate synthase (GGPPS). Current bisphosphonate drugs (e.g., Fosamax and Zometa) are highly efficacious in the treatment of bone diseases such as osteoporosis, Paget's disease, and tumor-induced osteolysis, but they are often less potent in blood and soft-tissue due to their phosphate moieties. The discovery of nonbisphosphonate inhibitors of FPPS and/or GGPPS for the treatment of bone diseases and cancers is, therefore, a current goal. Here, we propose a moiety-linkage-based method, combining a site-moiety map with chemical structure rules (CSRs), to discover nonbisphosphonate inhibitors from thousands of commercially available compounds and known crystal structures. Our moiety-linkage map reveals the binding mechanisms and inhibitory efficacies of 51 human GGPPS (hGGPPS) inhibitors. To the best of our knowledge, we are the first team to discover two novel selective nonbisphosphonate inhibitors, which bind to the inhibitory site of hGGPPS, using CSRs and site-moiety maps. These two compounds can be considered as a novel lead for the potent inhibitors of hGGPPS for the treatment of cancers and mevalonate-pathway diseases. Moreover, based on our moiety-linkage map, we identified two key residues of hGGPPS, K202, and K212, which play an important role for the inhibitory effect of zoledronate (IC50 = 3.4 μM and 2.4 μM, respectively). This result suggests that our method can discover specific hGGPPS inhibitors across multiple prenyltransferases. These results show that the compounds that highly fit our moiety-linkage map often inhibit hGGPPS activity and induce tumor cell apoptosis. We believe that our method is useful for discovering potential inhibitors and binding mechanisms for pharmaceutical targets.

Pathway-based screening strategy for multitarget inhibitors of diverse proteins in metabolic pathways

Many virtual screening methods have been developed for identifying single-target inhibitors based on the strategy of “one–disease, one–target, one–drug”. The hit rates of these methods are often low because they cannot capture the features that play key roles in the biological functions of the target protein. Furthermore, single-target inhibitors are often susceptible to drug resistance and are ineffective for complex diseases such as cancers. Therefore, a new strategy is required for enriching the hit rate and identifying multitarget inhibitors. To address these issues, we propose the pathway-based screening strategy (called PathSiMMap) to derive binding mechanisms for increasing the hit rate and discovering multitarget inhibitors using site-moiety maps. This strategy simultaneously screens multiple target proteins in the same pathway; these proteins bind intermediates with common substructures. These proteins possess similar conserved binding environments (pathway anchors) when the product of one protein is the substrate of the next protein in the pathway despite their low sequence identity and structure similarity. We successfully discovered two multitarget inhibitors with IC₅₀ of <10 µM for shikimate dehydrogenase and shikimate kinase in the shikimate pathway of Helicobacter pylori. Furthermore, we found two selective inhibitors (IC₅₀ of <10 µM) for shikimate dehydrogenase using the specific anchors derived by our method. Our experimental results reveal that this strategy can enhance the hit rates and the pathway anchors are highly conserved and important for biological functions. We believe that our strategy provides a great value for elucidating protein binding mechanisms and discovering multitarget inhibitors.

Many drug development strategies focus on designing inhibitors for single targets. These inhibitors often lose potency owing to mutations in the protein binding sites and are ineffective for complex diseases. Multitarget inhibitors can decrease probability of drug resistance and enhance the therapeutic efficiency; however, identifying them is still a challenge because targets often have low sequence and structure similarities in their binding sites. Here we propose a pathway-based screening strategy that simultaneously screens proteins in a metabolic pathway for discovering multitarget inhibitors. Because these proteins interact with similar metabolites and modify them step-by-step, the proteins share similarities in binding sites. We developed pathway site-moiety maps that present the conserved binding environments of the proteins without relying on the sequence or structure alignment. Compounds that bind these conserved binding environments are often multitarget inhibitors. We applied this strategy to the shikimate pathway of Helicobacter pylori, and discovered two multitarget inhibitors (IC₅₀<10 µM) for shikimate dehydrogenase and shikimate kinase. In addition, we found two selective inhibitors based on specific binding environments for shikimate dehydrogenase. Thus the pathway-based screening strategy is useful for identifying multitarget inhibitors and elucidating protein-ligand binding mechanisms and has the potential to be applied to human diseases.

Parallel screening of wild-type and drug-resistant targets for anti-resistance neuraminidase inhibitors

Infection with influenza virus is a major public health problem, causing serious illness and death each year. Emergence of drug-resistant influenza virus strains limits the effectiveness of drug treatment. Importantly, a dual H275Y/I223R mutation detected in the pandemic influenza A 2009 virus strain results in multidrug resistance to current neuraminidase (NA) drugs. Therefore, discovery of new agents for treating multiple drug-resistant (MDR) influenza virus infections is important. Here, we propose a parallel screening strategy that simultaneously screens wild-type (WT) and MDR NAs, and identifies inhibitors matching the subsite characteristics of both NA-binding sites. These may maintain their potency when drug-resistant mutations arise. Initially, we analyzed the subsite of the dual H275Y/I223R NA mutant. Analysis of the site-moiety maps of NA protein structures show that the mutant subsite has a relatively small volume and is highly polar compared with the WT subsite. Moreover, the mutant subsite has a high preference for forming hydrogen-bonding interactions with polar moieties. These changes may drive multidrug resistance. Using this strategy, we identified a new inhibitor, Remazol Brilliant Blue R (RB19, an anthraquinone dye), which inhibited WT NA and MDR NA with IC₅₀ values of 3.4 and 4.5 µM, respectively. RB19 comprises a rigid core scaffold and a flexible chain with a large polar moiety. The former interacts with highly conserved residues, decreasing the probability of resistance. The latter forms van der Waals contacts with the WT subsite and yields hydrogen bonds with the mutant subsite by switching the orientation of its flexible side chain. Both scaffolds of RB19 are good starting points for lead optimization. The results reveal a parallel screening strategy for identifying resistance mechanisms and discovering anti-resistance neuraminidase inhibitors. We believe that this strategy may be applied to other diseases with high mutation rates, such as cancer and human immunodeficiency virus type 1.

KIDFamMap: a database of kinase-inhibitor-disease family maps for kinase inhibitor selectivity and binding mechanisms

Kinases play central roles in signaling pathways and are promising therapeutic targets for many diseases. Designing selective kinase inhibitors is an emergent and challenging task, because kinases share an evolutionary conserved ATP-binding site. KIDFamMap (http://gemdock.life.nctu.edu.tw/KIDFamMap/) is the first database to explore kinase-inhibitor families (KIFs) and kinase-inhibitor-disease (KID) relationships for kinase inhibitor selectivity and mechanisms. This database includes 1208 KIFs, 962 KIDs, 55 603 kinase-inhibitor interactions (KIIs), 35 788 kinase inhibitors, 399 human protein kinases, 339 diseases and 638 disease allelic variants. Here, a KIF can be defined as follows: (i) the kinases in the KIF with significant sequence similarity, (ii) the inhibitors in the KIF with significant topology similarity and (iii) the KIIs in the KIF with significant interaction similarity. The KIIs within a KIF are often conserved on some consensus KIDFamMap anchors, which represent conserved interactions between the kinase subsites and consensus moieties of their inhibitors. Our experimental results reveal that the members of a KIF often possess similar inhibition profiles. The KIDFamMap anchors can reflect kinase conformations types, kinase functions and kinase inhibitor selectivity. We believe that KIDFamMap provides biological insights into kinase inhibitor selectivity and binding mechanisms.

Core site-moiety maps reveal inhibitors and binding mechanisms of orthologous proteins by screening compound libraries

Members of protein families often share conserved structural subsites for interaction with chemically similar moieties despite low sequence identity. We propose a core site-moiety map of multiple proteins (called CoreSiMMap) to discover inhibitors and mechanisms by profiling subsite-moiety interactions of immense screening compounds. The consensus anchor, the subsite-moiety interactions with statistical significance, of a CoreSiMMap can be regarded as a "hot spot" that represents the conserved binding environments involved in biological functions. Here, we derive the CoreSiMMap with six consensus anchors and identify six inhibitors (IC(50)<8.0 µM) of shikimate kinases (SKs) of Mycobacterium tuberculosis and Helicobacter pylori from the NCI database (236,962 compounds). Studies of site-directed mutagenesis and analogues reveal that these conserved interacting residues and moieties contribute to pocket-moiety interaction spots and biological functions. These results reveal that our multi-target screening strategy and the CoreSiMMap can increase the accuracy of screening in the identification of novel inhibitors and subsite-moiety environments for elucidating the binding mechanisms of targets.

In silico screening of epidermal growth factor receptor (EGFR) in the tyrosine kinase domain through a medicinal plant compound database

The unregulated epidermal growth factor receptor tyrosine kinase (ErbB1-TK or EGFR-TK) protein is involved in the proliferation of more than 50% of all cancer types. The reduction of EGFR-TK activity by small or medium-sized molecules has been proven to be an effective treatment for cancer. There is a widespread belief that Chinese medicinal herbs are active against several diseases, including various types of cancer. In this study, 29,960 compounds from the Chemiebase medicinal compound database were virtually screened against the EGFR-TK using AutoDock4.0, GOLD and GLIDE (XP). The results revealed eight potential hits: CAS nos. 104096-45-9, 112649-21-5, 113866-89-0, 142608-98-8, 142608-99-9, 144761-33-1, 155233-17-3 and 80510-05-0. These compounds have been reported to show anticancer activities in the literature. With the help of SiMMap and MOE interaction analysis, the protein-ligand interaction patterns between the functional groups of these compounds and the binding pocket residues were analyzed. Hydrogen bonding and hydrophobic forces are the main components of the interactions of these hits, similar to those observed for the known inhibitors erlotinib, gefitinib and AEE. The physicochemical filter indicates that compounds CAS nos. 104096-45-9 and 144761-33-1 are likely to be potential leads in the drug discovery process.

Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome--clozapine-induced agranulocytosis as a case study

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs.

Idiosyncratic drug reactions (IDR) generally cannot be identified until after a drug is taken by a large population, but usually result in restricted use or withdrawal. Clozapine provides the most effective treatment for schizophrenia but its use is limited because of a life-threatening IDR, i.e., the agranulocytosis. A high impact clinical study demonstrated the necessity of moving clozapine from 3^rd line to 1^st line drug; therefore, intensive research has aimed at identifying genes responsible for clozapine-induced agranulocytosis (CIA). Olanzapine, an analog of clozapine, has much lower incidence of agranulocytosis. Based on this phenomenon, we proposed an in silico methodology termed as antithesis chemical-protein interactome (CPI), which mimics the differences in the drug-protein interactions of the two drugs across a panel of human proteins. e.g., HSPA1A was identified to be targeted by clozapine not olanzapine. Furthermore, the gene expression of the HSPA1A-related gene system was also found up-regulated after clozapine treatment. This approach can examine the system's perturbation in terms of both the off-target and the off-system's interaction with the drug, providing theoretical basis for decoding the adverse drug reactions or the new uses for old drugs.