The Conserved Macrodomain Is a Potential Therapeutic Target for Coronaviruses and Alphaviruses

Emerging and re-emerging viral diseases pose continuous public health threats, and effective control requires a combination of non-pharmacologic interventions, treatment with antivirals, and prevention with vaccines. The COVID-19 pandemic has demonstrated that the world was least prepared to provide effective treatments. This lack of preparedness has been due, in large part, to a lack of investment in developing a diverse portfolio of antiviral agents, particularly those ready to combat viruses of pandemic potential. Here, we focus on a drug target called macrodomain that is critical for the replication and pathogenesis of alphaviruses and coronaviruses. Some mutations in alphavirus and coronaviral macrodomains are not tolerated for virus replication. In addition, the coronavirus macrodomain suppresses host interferon responses. Therefore, macrodomain inhibitors have the potential to block virus replication and restore the host’s protective interferon response. Viral macrodomains offer an attractive antiviral target for developing direct acting antivirals because they are highly conserved and have a structurally well-defined (druggable) binding pocket. Given that this target is distinct from the existing RNA polymerase and protease targets, a macrodomain inhibitor may complement current approaches, pre-empt the threat of resistance and offer opportunities to develop combination therapies for combating COVID-19 and future viral threats.

Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning..

Background: Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning. Objective: Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning. Methods: Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP. Results: The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively. Conclusion: Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

Suppressing Effect of Na+/Ca2+ Exchanger (NCX) Inhibitors on the Growth of Melanoma Cells

The role of calcium ion (Ca2+) signaling in tumorigenicity has received increasing attention in melanoma research. Previous Ca2+ signaling studies focused on Ca2+ entry routes, but rarely explored the role of Ca2+ extrusion. Functioning of the Na+/Ca2+ exchanger (NCX) on the plasma membrane is the major way of Ca2+ extrusion, but very few associations between NCX and melanoma have been reported. Here, we explored whether pharmacological modulation of the NCX could suppress melanoma and promise new therapeutic strategies. Methods included cell viability assay, Ca2+ imaging, immunoblotting, and cell death analysis. The NCX inhibitors SN-6 and YM-244769 were used to selectively block reverse operation of the NCX. Bepridil, KB-R7943, and CB-DMB blocked either reverse or forward NCX operation. We found that blocking the reverse NCX with SN-6 or YM-244769 (5–100 μM) did not affect melanoma cells or increase cytosolic Ca2+. Bepridil, KB-R7943, and CB-DMB all significantly suppressed melanoma cells with IC50 values of 3–20 μM. Bepridil and KB-R7943 elevated intracellular Ca2+ level of melanoma. Bepridil-induced melanoma cell death came from cell cycle arrest and enhanced apoptosis, which were all attenuated by the Ca2+ chelator BAPTA-AM. As compared with melanoma, normal melanocytes had lower NCX1 expression and were less sensitive to the cytotoxicity of bepridil. In conclusion, blockade of the forward but not the reverse NCX leads to Ca2+-related cell death in melanoma and the NCX is a potential drug target for cancer therapy.

Multi-Omics Analysis of Lipid Metabolism for a Marine Probiotic Meyerozyma guilliermondii GXDK6 Under High NaCl Stress

Investigating microbial lipid regulation contributes to understanding the lipid-dependent signal transduction process of cells and helps to improve the sensitivity of microorganisms to environmental factors by interfering with lipid metabolism, thus beneficial for constructing advanced cell factories of novel molecular drugs. Integrated omics technology was used to systematically reveal the lipid metabolism mechanism of a marine Meyerozyma guilliermondii GXDK6 under high NaCl stress and test the sensitivity of GXDK6 to antibiotics when its lipid metabolism transformed. The omics data showed that when GXDK6 perceived 10% NaCl stress, the expression of AYR1 and NADPH-dependent 1-acyldihydroxyacetone phosphate reductase was inhibited, which weaken the budding and proliferation of cell membranes. This finding was further validated by decreased 64.39% of OD600 under 10% NaCl stress when compared with salt-free stress. In addition, salt stress promoted a large intracellular accumulation of glycerol, which was also verified by exogenous addition of glycerol. Moreover, NaCl stress remarkably inhibited the expression of drug target proteins (such as lanosterol 14-alpha demethylase), thereby increasing sensitivity to fluconazole. This study provided new insights into the molecular mechanism involved in the regulation of lipid metabolism in Meyerozyma guilliermondii strain and contributed to developing new methods to improve the effectiveness of killing fungi with lower antibiotics.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks

The tremendous success of graphical neural networks (GNNs) has already had a major impact on systems biology research. For example, GNNs are currently used for drug target recognition in protein-drug interaction networks as well as cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability, and explainability. In this work, we present a graph-based deep learning framework for disease subnetwork detection via explainable GNNs. In our framework, each patient is represented by the topology of a protein-protein network (PPI), and the nodes are enriched by molecular multimodal data, such as gene expression and DNA methylation. Therefore, our novel modification of the GNNexplainer for model-wide explanations can detect potential disease subnetworks, which is of high practical relevance. The proposed methods are implemented in the GNN-SubNet Python program, which we have made freely available on our GitHub for the international research community (https://github.com/pievos101/GNN-SubNet).

Alpha-Soluble NSF Attachment Protein Prevents the Cleavage of the SARS-CoV-2 Spike Protein by Functioning as an Interferon-Upregulated Furin Inhibitor

Some key mutations of SARS-CoV-2 spike protein, such as D614G and P681R mutations, increase the transmission or pathogenicity by enhancing the cleavage efficacy of spike protein by furin. Loss of the furin cleavage motif of SARS-CoV-2 spike protein reduces the virulence and transmission, suggesting that furin is an attractive antiviral drug target.