Abstract: Deciphering the functional impact of genetic variation is required to understand phenotypic diversity and the molecular mechanisms of inherited disease and cancer. While millions of genetic variants are now mapped in genome sequencing projects, distinguishing functional variants remains a major challenge. Protein-coding variation can be interpreted using post-translational modification (PTM) sites that are core components of cellular signaling networks controlling molecular processes and pathways. ActiveDriverDB is an interactive proteo-genomics database that uses more than 260,000 experimentally detected PTM sites to predict the functional impact of genetic variation in disease, cancer and the human population. Using machine learning tools, we prioritize proteins and pathways with enriched PTM-specific amino acid substitutions that potentially rewire signaling networks via induced or disrupted short linear motifs of kinase binding. We then map these effects to site-specific protein interaction networks and drug targets. In the 2021 update, we increased the PTM datasets by nearly 50%, included glycosylation, sumoylation and succinylation as new types of PTMs, and updated the workflows to interpret inherited disease mutations. We added a recent phosphoproteomics dataset reflecting the cellular response to SARS-CoV-2 to predict the impact of human genetic variation on COVID-19 infection and disease course. Overall, we estimate that 16-21% of known amino acid substitutions affect PTM sites among pathogenic disease mutations, somatic mutations in cancer genomes and germline variants in the human population. These data underline the potential of interpreting genetic variation through the lens of PTMs and signaling networks. The open-source database is freely available at www.ActiveDriverDB.org.

Abstract: Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

Abstract: The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

Abstract: Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.

Abstract: Background: Huai Hua San (HHS), a famous Traditional Chinese Medicine (TCM) formula, has been widely applied in treating ulcerative colitis (UC). However, the interaction of bioactives from HHS with the targets involved in UC has not been elucidated yet. Aim: A network pharmacology-based approach combined with molecular docking and in vitro validation was performed to determine the bioactives, key targets, and potential pharmacological mechanism of HHS against UC. Materials and Methods: Bioactives and potential targets of HHS, as well as UC-related targets, were retrieved from public databases. Crucial bioactive ingredients, potential targets, and signaling pathways were acquired through bioinformatics analysis, including protein-protein interaction (PPI), as well as the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Subsequently, molecular docking was carried out to predict the combination of active compounds with core targets. Lastly, in vitro experiments were conducted to further verify the findings. Results: A total of 28 bioactive ingredients of HHS and 421 HHS-UC-related targets were screened. Bioinformatics analysis revealed that quercetin, luteolin, and nobiletin may be potential candidate agents. JUN, TP53, and ESR1 could become potential therapeutic targets. PI3K-AKT signaling pathway might play an important role in HHS against UC. Moreover, molecular docking suggested that quercetin, luteolin, and nobiletin combined well with JUN, TP53, and ESR1, respectively. Cell experiments showed that the most important ingredient of HHS, quercetin, could inhibit the levels of inflammatory factors and phosphorylated c-Jun, as well as PI3K-Akt signaling pathway in LPS-induced RAW264.7 cells, which further confirmed the prediction by network pharmacology strategy and molecular docking. Conclusion: Our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of HHS against UC. It also provided a promising strategy to uncover the scientific basis and therapeutic mechanism of TCM formulae in treating diseases.

Abstract: Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.

Abstract: Background: Metastasis represents an advanced stage of cancers, and matrix metalloproteinases are critical regulators. Calcium signal is crucial for appropriate cell behaviors. The efficacy and effects of calcium channel blockers in treating cancers are individually differ from each other. Here, we attempt to investigate the effects of nicardipine, a FDA-approved calcium channel blocker, in advanced breast cancers. Methods: We analyzed the influence of nicardipine on the colony-forming ability of triple negative breast cancer cell lines. Using cell culture inserts, cell migration was also examined. The expression of regulatory proteins was evaluated by real-time PCR, Western blot, and ELISA. Results: We have confirmed that nicardipine inhibits the breast cancer cells migration and colony formation. In addition, we also revealed that nicardipine increases the Nrf2 and HO-1 expression. The inhibition of HO-1 abrogates nicardipine-reduced matrix metalloproteinase-9 expression. Moreover, the end products of HO-1, namely, CO, Fe2+, and biliverdin (will converted to bilirubin), also decreases the expression of matrix metalloproteinase-9. Conclusion: These findings suggest that nicardipine-mediated matrix metalloproteinase-9 reduction is regulated by Nrf2/HO-1 axis and its catalytic end products. Therefore, nicardipine may be a potential candidate for repurposing against advanced breast cancers.

Abstract: Bile acids (BA) are recognized by their role in nutrient absorption. However, there is growing evidence that BA also have endocrine and metabolic functions. Besides, the steroidal-derived structure gives BA a toxic potential over the biological membrane. Thus, cholestatic disorders, characterized by elevated BA on the liver and serum, are a significant cause of liver transplant and extrahepatic complications, such as skeletal muscle, central nervous system (CNS), heart, and placenta. Further, the BA have an essential role in cellular damage, mediating processes such as membrane disruption, mitochondrial dysfunction, and the generation of reactive oxygen species (ROS) and oxidative stress. The purpose of this review is to describe the BA and their role on hepatic and extrahepatic complications in cholestatic diseases, focusing on the association between BA and the generation of oxidative stress that mediates tissue damage.

Abstract: Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%-on par with that of non-expert humans-and when applied to CORD'19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team's targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

Abstract: Methods: Screen the biologically active components and potential targets of SNFYT through Traditional Chinese Medicine Systems Pharmacology (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID), and related literature. In addition, DrugBank, OMIM, DisGeNET, and the Therapeutic Target Database were searched to explore the therapeutic targets of IS. The cross-targets of SNFYT potential targets and IS treatment targets were taken as candidate gene targets, and GO and KEGG enrichment analyses were performed on the candidate targets. On this basis, the SNFYT-component-target network and protein-protein interaction (PPI) network were constructed using Cytoscape 3.7.2. Finally, AutoDock was used to verify the molecular docking of core components and core targets. Results: We screened out 95 potentially active components and 143 candidate targets. SNFYT-component-target network, PPI network, and Cytoscape analysis identified four core active ingredients and 14 core targets. GO enrichment analyzed 2333 biological processes, 79 cell components, and 149 molecular functions. There are 170 KEGG-related signal pathways (P < 0.05), including the IL-17 signal pathway, TNF signal pathway, and HIF-1 signal pathway. The molecular docking results of the core components and the core targets showed good binding power. Conclusions: SNFYT may achieve the effect of treating ischemic stroke through its anti-inflammatory effect through a signal pathway with core targets as the core.