Abstract: For nearly a decade, sorafenib has served as a first-line chemotherapeutic drug for the treatment of hepatocellular carcinoma (HCC), but it displays only limited efficacy against advanced drug-resistant HCC. Regorafenib, the first second-line drug approved for treatment after sorafenib failure, can reverse resistance to sorafenib. We used bioinformatics methods to identify genes whose expression was differentially induced by sorafenib and regorafenib in HCC. We identified KIF14 as an oncogene involved in the acquired resistance to sorafenib in HCC and investigated its potential as a target for reversing this resistance. Sustained exposure of resistant HCC cells to sorafenib activated the AKT pathway, which in turn upregulated KIF14 expression by increasing expression of the transcription factor ETS1. Silencing KIF14 reversed the acquired resistance to sorafenib by inhibiting AKT activation and downregulating ETS1 expression by blocking the AKT-ETS1-KIF14 positive feedback loop. Moreover, injection of siKIF14 with sorafenib suppressed growth of sorafenib-resistant HCC tumors in mice. These results demonstrate that targeting KIF14 could be an effective means of reversing sorafenib failure or strengthening sorafenib's antitumor effects.

Abstract: Control of fungal pathogens is increasingly problematic due to the limited number of effective drugs available for antifungal therapy. Conventional antifungal drugs could also trigger human cytotoxicity associated with the kidneys and liver, including the generation of reactive oxygen species. Moreover, increased incidences of fungal resistance to the classes of azoles, such as fluconazole, itraconazole, voriconazole, or posaconazole, or echinocandins, including caspofungin, anidulafungin, or micafungin, have been documented. Of note, certain azole fungicides such as propiconazole or tebuconazole that are applied to agricultural fields have the same mechanism of antifungal action as clinical azole drugs. Such long-term application of azole fungicides to crop fields provides environmental selection pressure for the emergence of pan-azole-resistant fungal strains such as Aspergillus fumigatus having TR34/L98H mutations, specifically, a 34 bp insertion into the cytochrome P450 51A (CYP51A) gene promoter region and a leucine-to-histidine substitution at codon 98 of CYP51A. Altogether, the emerging resistance of pathogens to currently available antifungal drugs and insufficiency in the discovery of new therapeutics engender the urgent need for the development of new antifungals and/or alternative therapies for effective control of fungal pathogens. We discuss the current needs for the discovery of new clinical antifungal drugs and the recent drug repurposing endeavors as alternative methods for fungal pathogen control.

Abstract: BACKGROUND: Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. METHODS: A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo. RESULTS: The experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models. CONCLUSIONS: DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI .

Abstract: BACKGROUND: Guanxin V (GXV), a traditional Chinese medicine (TCM), has been widely used to treat coronary artery disease (CAD) in clinical practice in China. However, research on the active components and underlying mechanisms of GXV in CAD is still scarce. METHODS: A virtual screening and network pharmacological approach was utilized for predicting the pharmacological mechanisms of GXV in CAD. The active compounds of GXV based on various TCM-related databases were selected and then the potential targets of these compounds were identified. Then, after the CAD targets were built through nine databases, a PPI network was constructed based on the matching GXV and CAD potential targets, and the hub targets were screened by MCODE. Moreover, Metascape was applied to GO and KEGG functional enrichment. Finally, HPLC fingerprints of GXV were established. RESULTS: A total of 119 active components and 121 potential targets shared between CAD and GXV were obtained. The results of functional enrichment indicated that several GO biological processes and KEGG pathways of GXV mostly participated in the therapeutic mechanisms. Furthermore, 7 hub MCODEs of GXV were collected as potential targets, implying the complex effects of GXV-mediated protection against CAD. Six specific chemicals were identified. CONCLUSION: GXV could be employed for CAD through molecular mechanisms, involving complex interactions between multiple compounds and targets, as predicted by virtual screening and network pharmacology. Our study provides a new TCM for the treatment of CAD and deepens the understanding of the molecular mechanisms of GXV against CAD.

Abstract: Given the COVID-19 pandemic, currently, there are many drugs in clinical trials against this virus. Among the excellent drug targets of SARS-CoV-2 are its proteases (Nsp3 and Nsp5) that plays vital role in polyprotein processing giving rise to functional nonstructural proteins, essential for viral replication and survival. Nsp5 (also known as M(pro)) hydrolyzes replicase polyprotein (1ab) at eleven different sites. For targeting M(pro), we have employed drug repurposing approach to identify potential inhibitors of SARS-CoV-2 in a shorter time span. Screening of approved drugs through docking reveals Hyaluronic acid and Acarbose among the top hits which are showing strong interactions with catalytic site residues of M(pro). We have also performed docking of drugs Lopinavir, Ribavirin, and Azithromycin on SARS-CoV-2 M(pro). Further, binding of these compounds (Hyaluronic acid, Acarbose, and Lopinavir) is validated by extensive molecular dynamics simulation of 500 ns where these drugs show stable binding with M(pro). We believe that the high-affinity binding of these compounds will help in designing novel strategies for structure-based drug discovery against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Abstract: BACKGROUND: Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential response shown by altered kinases to drug treatment in patients and cell-based assays. However, an incomplete understanding of the relationships connecting genome, proteome and drug sensitivity profiles present a major bottleneck in targeting kinases for personalized medicine. RESULTS: In this study, we propose a multi-component Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model and neural networks framework for providing explainable models of protein kinase inhibition and drug response ([Formula: see text]) profiles in cell lines. Using non-small cell lung cancer as a case study, we show that interaction terms that capture associations between drugs, pathways, and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib). In particular, protein-protein interactions associated with the JNK apoptotic pathway, associations between lung development and axon extension, and interaction terms connecting drug substructures and the volume/charge of mutant residues at specific structural locations contribute significantly to the observed [Formula: see text] values in cell-based assays. CONCLUSIONS: By integrating multi-omics data in the QSMART model, we not only predict drug responses in cancer cell lines with high accuracy but also identify features and explainable interaction terms contributing to the accuracy. Although we have tested our multi-component explainable framework on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles.

Abstract: Among targets selected for studies aimed at identifying potential inhibitors against COVID-19, SARS-CoV2 main proteinase (M(pro)) is highlighted. M(pro) is indispensable for virus replication and is a promising target of potential inhibitors of COVID-19. Recently, monomeric SARS-CoV2 M(pro), drug repurposing, and docking methods have facilitated the identification of several potential inhibitors. Results were refined through the assessment of dimeric SARS-CoV2 M(pro), which represents the functional state of enzyme. Docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area (MM/GBSA) studies indicated that dimeric M(pro) most significantly impacts binding affinity tendency compared with the monomeric state, which suggests that dimeric state is most useful when performing studies aimed at identifying drugs targeting M(pro). In this study, we extend previous research by performing docking and MD simulation studies coupled with an MM/GBSA approach to assess binding of dimeric SARS-CoV2 M(pro) to 12 FDA-approved drugs (darunavir, indinavir, saquinavir, tipranavir, diosmin, hesperidin, rutin, raltegravir, velpatasvir, ledipasvir, rosuvastatin, and bortezomib), which were identified as the best candidates for the treatment of COVID-19 in some previous dockings studies involving monomeric SARS-CoV2 M(pro). This analysis identified saquinavir as a potent inhibitor of dimeric SARS-CoV2 M(pro); therefore, the compound may have clinical utility against COVID-19. Graphical abstract.

Abstract: COVID-19, caused by novel coronavirus or SARS-CoV-2, is a viral disease which has infected millions worldwide. Considering the urgent need of the drug for fighting against this infectious disease, we have performed in-silico drug repurposing followed by molecular dynamics (MD) simulation and MM-GBSA calculation. The main protease (M(pro)) is one of the best-characterized drug targets among coronaviruses, therefore, this was screened for already known FDA approved drugs and some natural compounds. Comparison of docking and MD simulation results of complexes of drugs with that of inhibitor N3 (experimentally obtained) suggests EGCG, withaferin, dolutegravir, artesunate as potential inhibitors of the main protease (M(pro)). Further, in silico docking and MD simulation suggest that EGCG analogues ZINC21992196 and ZINC 169337541 may act as a better inhibitor. Communicated by Ramaswamy H. Sarma.

Abstract: The ability to accurately predict the binding site, binding pose, and binding affinity for ligand-RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand-RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand-RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand-RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA-ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 A, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand-RNA binding with an ensemble of RNA conformations and the metal-ion effects.

Abstract: Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug synergy. Yet they normally require the drug-cell treatment results as an essential input, thus exclude the possibility to pre-screen those unexplored drugs without cell treatment profiling. Based on the largest dataset of 33,574 combinational scenarios, we proposed a handy webserver, H-RACS, to overcome the above problems. Being loaded with chemical structures and target information, H-RACS can recommend potential synergistic pairs between candidate drugs on 928 cell lines of 24 prevalent cancer types. A high model performance was achieved with AUC of 0.89 on independent combinational scenarios. On the second independent validation of DREAM dataset, H-RACS obtained precision of 67% among its top 5% ranking list. When being tested on new combinations and new cell lines, H-RACS showed strong extendibility with AUC of 0.84 and 0.81 respectively. As the first online server freely accessible at http://www.badd-cao.net/h-racs, H-RACS may promote the pre-screening of synergistic combinations for new chemical drugs on unexplored cancers.