Candidate Drug Targets Research Articles

Astragaloside IV against Alzheimer's disease via microglia-mediated neuroinflammation using network pharmacology and experimental validation

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in the world. The effective therapeutic methods and drugs are still not clear. Astragaloside IV (AS-IV), a triterpenoid saponin isolated from the root of Huangqi, has a beneficial effect in the treatment of AD. However, whether AS-IV alters microglia in the inflammation of AD is still ambiguous. In our study, 99 common targets were collected between AS-IV and AD. BCL2 apoptosis regulator (Bcl-2), pro-apoptotic BCL-2 protein BAX, epidermal growth factor receptor (EGFR), and receptor tyrosine phosphatase type C (PTPRC) were screened for inflammation and microglia in the above targets by network pharmacology. Interleukin-1β (IL-1β) and EGFR both interact with signal transducer and activator of transcription 3 (STAT3) by a protein interaction network, and IL-1β had a higher affinity for AS-IV based on molecular docking. Enrichment revealed targets involved in the regulation of neuronal cell bodies, growth factor receptor binding, EGFR tyrosine kinase inhibitor resistance., etc. Besides, AS-IV alleviated the reduced cell proliferation in amyloid-beta (Aβ)-treated microglial BV2 cells. AS-IV affected BV2 cell morphological changes and decreased cluster of differentiation 11b (CD11b) gene, IL-1β, and EGFR mRNA levels increment during lipopolysaccharide (LPS) injury in BV2 cell activation. Therefore, AS-IV may regulate microglial activation and inflammation via EGFR-dependent pathways in AD. EGFR and IL-1β are vital targets that may relate to each other to coregulate downstream molecular functions in the cure of AD. Our study provides a candidate drug and disease target for the treatment of neurodegenerative diseases in the clinic.

Development of an interfering peptide M1-20 with potent anti-cancer effects by targeting FOXM1

Disrupting protein–protein interactions (PPIs) has emerged as a promising strategy for cancer drug development. Interfering peptides disrupting PPIs can be rationally designed based on the structures of natural sequences mediating these interactions. Transcription factor FOXM1 overexpresses in multiple cancers and is considered an effective target for cancer therapeutic drug development. Using a rational design approach, we have generated a peptide library from the FOXM1 C-terminal sequence and screened FOXM1-binding peptides. Combining FOXM1 binding and cell inhibitory results, we have obtained a FOXM1-targeting interfering peptide M1-20 that is optimized from the natural parent peptide to the D-retro-inverso peptide. With improved stability characteristics, M1-20 inhibits proliferation and migration, and induces apoptosis of cancer cells. Mechanistically, M1-20 inhibits FOXM1 transcriptional activities by disrupting its interaction between the MuvB complex and the transcriptional co-activator CBP. These are consistent with the results that M1-20 suppresses cancer progression and metastasis without noticeable toxic and side effects in wild-type mice. These findings reveal that M1-20 has the potential to be developed as an anti-cancer drug candidate targeting FOXM1.

Lipids, lipid-modifying drug target genes and migraine: a Mendelian randomization study

IntroductionMigraine, a prevalent headache disorder with unclear mechanisms and limited treatments, may be influenced by dyslipidemia and genetic factors. Statins and emerging lipid-modifying agents show potential but lack evidence for migraine management. Mendelian Randomization analysis offers insights into causal relationships and therapeutic targets. This study aims to explore genetically predicted lipid traits, drug targets, and their association with migraine risk.MethodWe conducted Mendelian randomization (MR) analyses utilizing genetic variants associated with lipid traits and variants in genes encoding the protein targets of various classes of lipid-lowering drugs. The specific drug classes investigated included HMGCR, PCSK9, NPC1L1, ABCG5/ABCG8, LDLR, LPL, ANGPTL3, APOB, CETP, and APOC3. To determine the effects on migraine risk, we meta-analyzed MR estimates for regional variants using data from two large sample sets. The genetic variants were weighted based on their associations with specific lipid traits, such as low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), Apolipoprotein A1, and Apolipoprotein B. To obtain association weights, we utilized data from lipid genetics consortia. For lipid-modifying drug targets that exhibited suggestive significance, we further employed expression quantitative trait locus (eQTL) data. Additionally, we performed colocalization analysis to assess genetic confounding.ResultThe use of genetic proxies for HMGCR inhibition demonstrated a significant association with a decreased risk of migraine in the FinnGen dataset (OR = 0.64, 95% CI: 0.46–0.88, p = 0.0006) and a nearly significant association in the Choquet dataset (OR = 0.78, 95% CI: 0.60–1.01, p = 0.06). When pooling the estimates, the overall effect size showed a reduced risk of migraine (OR = 0.73, 95% CI: 0.60–0.89, p = 0.0016). Similarly, genetic mimicry of LPL enhancement was associated with a lower risk of migraine in the FinnGen dataset (OR = 0.82, 95% CI: 0.69–0.96, p = 0.01) and the Choquet dataset (OR = 0.91, 95% CI: 0.83–0.99, p = 0.03). Pooling the estimates showed a consistent effect size (OR = 0.89, 95% CI: 0.83–0.96, p = 0.002). Sensitivity analyses yielded no statistically significant evidence of bias arising from pleiotropy or genetic confounding.ConclusionIn the study, it was observed that among the 10 lipid-lowering drug targets investigated, LPL and HMGCR showed significant associations with migraine risk. These findings indicate that LPL and HMGCR have the potential to serve as candidate drug targets for the treatment or prevention of migraines.

The discovery of the natural compound Boeravinone-C as a potential antiobesity drug candidate targeting pancreatic lipase using chemo-informatics-based approaches.

Obesity is a metabolic disorder distinguished by excess fat deposition in fatty tissues. Pancreatic lipase is one of the promising drug targets for treating obesity due to its critical role in the hydrolysis of triglycerides into mono-glycerides and free fatty acids. Due to unsatisfactory results and severe side effects of the current drugs available for treating obesity, there is an urgent need to identify novel therapeutic options. Boerhaavia diffusa is one of the widely known species of flowering plant commonly known as Punamava. Extracts from Punamava plants have been widely used in treating countless ailments in traditional medicine. Recently, multiple reports demonstrated the potential antiobesity activity of B. diffusa plant extracts. In this scenario, we have evaluated numerous reported B. diffusa against pancreatic lipase drug targets to identify which reported phytochemicals to have the most promising potential to act as an inhibitor for pancreatic lipase using computational approaches. All the twenty-four phytochemicals from Boerhaavia diffusa were identified as significantly strong binders with a range of binding energies between -6.0 to -8.0 Kcal/mol inside the pancreatic lipase active binding site. On the other hand, we calculated 2D Quantitative Structure-Activity Relationship (QSAR) molecular descriptor properties adhered to Lipinski's rule of five. Between twenty-four phytochemicals evaluated, Boeravinone-C, with a range binding energy of -8.0 Kcal/mol, was discovered as the best lead-like molecule, compared to marketed Orlistat, which has shown -5.6 Kcal/mol of binding energy. Conclusively, Boeravinone-C from B. diffusa extract showed promising inhibitory potential against pancreatic lipase worth further evaluation.

Leveraging Genomic and Bioinformatic Analysis to Enhance Drug Repositioning for Dermatomyositis.

Dermatomyositis (DM) is an autoimmune disease that is classified as a type of idiopathic inflammatory myopathy, which affects human skin and muscles. The most common clinical symptoms of DM are muscle weakness, rash, and scaly skin. There is currently no cure for DM. Genetic factors are known to play a pivotal role in DM progression, but few have utilized this information geared toward drug discovery for the disease. Here, we exploited genomic variation associated with DM and integrated this with genomic and bioinformatic analyses to discover new drug candidates. We first integrated genome-wide association study (GWAS) and phenome-wide association study (PheWAS) catalogs to identify disease-associated genomic variants. Biological risk genes for DM were prioritized using strict functional annotations, further identifying candidate drug targets based on druggable genes from databases. Overall, we analyzed 1239 variants associated with DM and obtained 43 drugs that overlapped with 13 target genes (JAK2, FCGR3B, CD4, CD3D, LCK, CD2, CD3E, FCGR3A, CD3G, IFNAR1, CD247, JAK1, IFNAR2). Six drugs clinically investigated for DM, as well as eight drugs under pre-clinical investigation, are candidate drugs that could be repositioned for DM. Further studies are necessary to validate potential biomarkers for novel DM therapeutics from our findings.

Genotyping and population characteristics of the China Kadoorie Biobank

The China Kadoorie Biobank (CKB) is a population-based prospective cohort of >512,000 adults recruited from 2004 to 2008 from 10 geographically diverse regions across China. Detailed data from questionnaires and physical measurements were collected at baseline, with additional measurements at three resurveys involving ∼5% of surviving participants. Analyses of genome-wide genotyping, for >100,000 participants usingcustom-designed Axiom arrays, reveal extensive relatedness, recent consanguinity, and signatures reflecting large-scale population movements from recent Chinese history. Systematic genome-wide association studies of incident disease, captured through electronic linkage to death and disease registries and to the national health insurance system, replicate established disease loci and identify 14 novel disease associations. Together with studies of candidate drug targets and disease risk factors and contributions to international genetics consortia, these demonstrate the breadth, depth, and quality of the CKB data. Ongoing high-throughput omics assays of collected biosamples and planned whole-genome sequencing will further enhance the scientific value of this biobank.

Analogs of the Catechol Derivative Dynasore Inhibit HIV-1 Ribonuclease H, SARS-CoV-2 nsp14 Exoribonuclease, and Virus Replication.

Viral replication often depends on RNA maturation and degradation processes catalyzed by viral ribonucleases, which are therefore candidate targets for antiviral drugs. Here, we synthesized and studied the antiviral properties of a novel nitrocatechol compound (1c) and other analogs that are structurally related to the catechol derivative dynasore. Interestingly, compound 1c strongly inhibited two DEDD box viral ribonucleases, HIV-1 RNase H and SARS-CoV-2 nsp14 3'-to-5' exoribonuclease (ExoN). While 1c inhibited SARS-CoV-2 ExoN activity, it did not interfere with the mRNA methyltransferase activity of nsp14. In silico molecular docking placed compound 1c in the catalytic pocket of the ExoN domain of nsp14. Finally, 1c inhibited SARS-CoV-2 replication but had no toxicity to human lung adenocarcinoma cells. Given its simple chemical synthesis from easily available starting materials, these results suggest that 1c might be a lead compound for the design of new antiviral compounds that target coronavirus nsp14 ExoN and other viral ribonucleases.

ArkDTA: attention regularization guided by non-covalent interactions for explainable drug-target binding affinity prediction.

Protein-ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein-ligand attention mechanism for more explainable deep drug-target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by NCIs. Experimental results show that ArkDTA achieves predictive performance comparable to current state-of-the-art models while significantly improving model explainability. Qualitative investigation into our novel attention mechanism reveals that ArkDTA can identify potential regions for NCIs between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner. ArkDTA is available at https://github.com/dmis-lab/ArkDTA. kangj@korea.ac.kr.

Comprehensive essentiality analysis of the Mycobacterium kansasii genome by saturation transposon mutagenesis and deep sequencing.

Mycobacterium kansasii (Mk) is an opportunistic pathogen that is frequently isolated from urban water systems, posing a health risk to susceptible individuals. Despite its ability to cause tuberculosis-like pulmonary disease, very few studies have probed the genetics of this opportunistic pathogen. Here, we report a comprehensive essentiality analysis of the Mk genome. Deep sequencing of a high-density library of Mk Himar1 transposon mutants revealed that 86.8% of the chromosomal thymine-adenine (TA) dinucleotide target sites were permissive to insertion, leaving 13.2% TA sites unoccupied. Our analysis identified 394 of the 5,350 annotated open reading frames (ORFs) as essential. The majority of these essential ORFs (84.8%) share essential mutual orthologs with Mycobacterium tuberculosis (Mtb). A comparative genomics analysis identified 139 Mk essential ORFs that share essential orthologs in four other species of mycobacteria. Thirteen Mk essential ORFs share orthologs in all four species that were identified as being not essential, while only two Mk essential ORFs are absent in all species compared. We used the essentiality data and a comparative genomics analysis reported here to highlight differences in essentiality between candidate Mtb drug targets and the corresponding Mk orthologs. Our findings suggest that the Mk genome encodes redundant or additional pathways that may confound validation of potential Mtb drugs and drug target candidates against the opportunistic pathogen. Additionally, we identified 57 intergenic regions containing four or more consecutive unoccupied TA sites. A disproportionally large number of these regions were located upstream of pe/ppe genes. Finally, we present an essentiality and orthology analysis of the Mk pRAW-like plasmid, pMK1248. IMPORTANCE Mk is one of the most common nontuberculous mycobacterial pathogens associated with tuberculosis-like pulmonary disease. Drug resistance emergence is a threat to the control of Mk infections, which already requires long-term, multidrug courses. A comprehensive understanding of Mk biology is critical to facilitate the development of new and more efficacious therapeutics against Mk. We combined transposon-based mutagenesis with analysis of insertion site identification data to uncover genes and other genomic regions required for Mk growth. We also compared the gene essentiality data set of Mk to those available for several other mycobacteria. This analysis highlighted key similarities and differences in the biology of Mk compared to these other species. Altogether, the genome-wide essentiality information generated and the results of the cross-species comparative genomics analysis represent valuable resources to assist the process of identifying and prioritizing potential Mk drug target candidates and to guide future studies on Mk biology.

Molecular Assays Simulator to Unravel Predictors Hacking in Goal-Directed Molecular Generations.

Generative models are being increasingly used in drug discovery, very often coupled with absorption, distribution, metabolism, and excretion (ADME) bioassays or quantitative structure-activity relationship (QSAR) models to optimize a given set of properties. The molecules proposed by these algorithms are often revealed to be false positives; that is, they are predicted to be active and turn out to be inactive after synthesis and testing, mostly due to overoptimization of the predicted scores, which leads to an actual decrease or stagnation of the real scores. This behavior is also known as the "hacking" of the predictive models by the generative model during the optimization step. This issue is reminiscent of adversarial examples in machine learning and it can be seen as enunciated by Goodhart's law: "when a measure becomes a target, it ceases to be a good measure." This issue is even more apparent in a multiparameter optimization (MPO) case, where the models need to extrapolate outside the training set distribution because there are no known molecules satisfying all the objectives simultaneously in the initial training set. Experimental evaluation of this problem is a hard and expensive task since it requires synthesis and testing of the generated molecules. Thus, efforts have been made to develop in silico "oracles"─real-valued functions used as proxies for molecular properties─to help with the evaluation of these generative-model-based pipelines. However, these oracles have had a limited value so far because they are often too easy to model in comparison with biological assays and are usually limited to mono-objective cases. In this work, we introduce a simulator of multitarget assays using a smartly initialized neural network (NN) that returns continuous values for any input molecule. We use this oracle to replicate a real-world prospective lead optimization (LO) scenario. First, we trained predictive models on an initial small sample of molecules aimed at predicting their oracle values. Afterward, we generated new optimized molecules using the open-source GuacaMol package coupled with the previously built predictive models. Finally, we selected compounds matching the candidate drug target profile (CDTP) according to the predicted values and evaluated them by computing the true oracle values. We observed that even when the predictive models had excellent estimated performance metrics, the final selection still contained multiple false positives according to the NN-based oracle. Then, we evaluated the optimization behavior in mono- and bi-objective scenarios using either a logistic regression or a random forest predictive model. We also propose and evaluate several methods to help mitigate the hacking issue.

#3336 EFFECTS OF LIPID-LOWERING DRUGS ON IMPROVING KIDNEY FUNCTION: A DRUG TARGET MENDELIAN RANDOMIZATION STUDY

Abstract Background and Aims Chronic kidney disease (CKD) affects one in ten adults worldwide. CKD frequently co-occurs with cardiovascular diseases, hypertension, and diabetes. Despite this high prevalence, few specific therapies exist. Thus, using drug-target Mendelian randomization (MR), we investigated the existing treatments for cardiovascular diseases, hypertension, and diabetes as potential treatment options for improving kidney function. Methods We identified SNPs as genetic proxies for the effects of targets of statins, ezetimibe, and alirocumab/evolocumab (as hypercholesterolemia drugs); angiotensin-converting enzyme (ACE) inhibitors, β-blockers (BB), and calcium channel blockers (CCB: as antihypertensive drugs); and metformin (as antidiabetic drug) on kidney function. Effects of these SNPs on exposures, including low-density lipoprotein cholesterol (LDL-C) for hypercholesterolemia drugs, systolic blood pressure (SBP) for antihypertensive drugs, and HbA1c for antidiabetic drug, were obtained from published genome wide association studies (GWASs) summary statistics. Similarly, SNP-outcome data for kidney related markers, including serum creatinine and serum cystatin C based estimated glomerular filtration rate (eGFRcrea and eGFRcys, respectively) and blood urea nitrogen (BUN), were obtained using the largest GWAS for these phenotypes. Two-sample MR analysis using the inverse variance weighted (IVW) method and additional sensitivity analyses including weighted median, weighted mode, and MR-Egger methods were carried out in R (version 4.2.1). The results are presented as standard deviation (SD) change in outcome per 1 SD change in exposure caused by the drug. Results Each 1 SD lowering in LDL-C caused by HMGCR variants, proxying the effect of statins, was associated with higher serum creatinine and cystatin-C based eGFR of approximately 0.01 SD (eGFRcrea β = 0.012, 95% CI = 0.007-0.018, p = 0.00032; eGFRcys β = 0.012, 95% CI = 0.002-0.022, p = 0.017), and lower BUN of 0.010 SD (β = -0.010, 95% CI = -0.020-0.000020, p = 0.050) (Figure 1), with consistent findings from sensitivity methods and no bias due to pleiotropy (PEgger = 0.22-0.70). Findings for other lipid-lowering drugs including NPC1L1 (target of ezetimibe) and PCSK9 (target of alirocumab/evolocumab) remained inconclusive. Similarly, we found limited evidence for the causal role of genetically proxied antihypertensive or antidiabetic drugs in improving kidney function, though beta values were close to null (Figure 1). Conclusion The genetically proxied LDL-C lowering effect of HMGCR is associated with improved kidney function, which is consistent with the reported causal association of LDL-C with kidney function in a previous trans-ethnic MR study (1). Limited evidence was found for antihypertensive and antidiabetic drugs. These results provide insights into potential drug target candidates for future trials to address the treatment of CKD and comorbidities.

Ion channels involved in inflammation and pain in osteoarthritis and related musculoskeletal disorders.

Osteoarthritis (OA) is a currently incurable, chronic, progressive and debilitating musculoskeletal condition. One of its hallmark symptoms is chronic nociceptive and neuropathic pain, which significantly reduces the quality of life of OA patients. While research into the pathomechanisms of OA pain is ongoing and several pain pathways are well understood, the true source of OA pain remains unclear. Ion channels and transporters are key mediators of nociceptive pain. In this narrative review article, we summarize the state-of-the-art in relation to the distribution and function of ion channels in all major synovial joint tissues in the context of pain generation. We provide an update on the ion channels likely involved in mediating peripheral and central nociceptive pathways in the nervous system in OA pain, including voltage-gated sodium and potassium channels, members of the transient receptor potential (TRP) channel family, and purinergic receptor complexes. We focus on ion channels and transporters that have a potential to be candidate drug targets for pain management in OA patients. We propose that ion channels expressed by the cells of constituent tissues of OA-afflicted synovial joints including cartilage, bone, synovium, ligament and muscle, should be more thoroughly investigated and targeted in the context of OA pain. Based on key findings from recent basic research articles as well as clinical trials, we propose novel directions for the development of future analgesic therapies to improve the quality of life of OA patients.

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

F.2 Multiplatform molecular analysis of vestibular schwannoma reveals two robust subgroups with distinct microenvironment

Background: Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle and poses a significant morbidity for patients. While many exhibit benign behaviour, others have a more aggressive nature. There is a need for a better understanding of the molecular landscape, and important subgroups therein, of this disease. Methods: We select all VS from our tumour bank with both methylation and RNA profiling. Unsupervised clustering methods were used to define two distinct molecular subgroups of VS which were explored using computational techniques including bulk deconvolution analysis, gene pathway enrichment analysis, and drug repurposing analysis. Methylation data from two other cohorts were used to validate our findings. Results: A total of 75 tumours were analyzed. Consensus clustering and similarity network fusion defined two subgroups (“immunogenic” and “proliferative”) with significant differences in immune, stroma, and tumour cell abundance. Gene network analysis and computational drug repurposing found critical differences in targets of immune checkpoint inhibition PD-1 and CTLA-4, the MEK pathway, and the epithelial-to-mesenchymal transition program with associated candidate drug targets, suggesting a need for subgroup-specific treatment/trial design in the future. Conclusions: We leverage computational tools with multi-omic molecular data to define two robust subgroups of vestibular schwannoma with differences in microenvironment and therapeutic vulnerabilities.

Overexpression of Eimeria tenella Rhoptry Kinase 2 Induces Early Production of Schizonts.

Eimeria tenella is an obligate intracellular parasite responsible for avian coccidiosis. Like other apicomplexan parasites, such as Toxoplasma gondii, cell invasion and intracellular development rely on apical organelle content discharge, named micronemes and rhoptries. Some rhoptry (ROP) kinases (ROPK) are key virulence factors in T. gondii. To date, among the 28 ropk genes carried by E. tenella, only two to four were confirmed by proteomic analysis or immunostaining to be expressed at the sporozoite stage. We have previously shown that EtROP1 is implicated in the inhibition of host cell apoptosis by interacting with the cellular p53. This work functionally described the second ROP kinase expressed at the sporozoite stage in E. tenella. EtROP2 is an active kinase that phosphorylates cell substrates of approximately 50 kDa. Its overexpression leads to the shortening of the prepatent period and to the early development of first-generation schizonts. Conduction of RNA sequencing analysis and reverse transcriptase quantitative PCR (RT-qPCR) on the host cell allowed us to identify the mitogen-activated protein kinase (MAPK) pathway and the transcription factor cFos to be upregulated by EtROP2. We also showed by immunofluorescence assay that the active kinase EtROP2 is implicated in the p38 MAPK pathway activation. We established here that EtROP2 activates the p38 MAPK pathway through a direct or indirect phosphorylation, leading to the overexpression of the master transcription factor cFos known to be implicated in E. tenella development. IMPORTANCE Rhoptries are specialized secretory organelles found in zoite stages of apicomplexan parasites. In addition to well-conserved rhoptry neck proteins, their protein consists mostly of kinase proteins, highly divergent from eukaryotic kinases. Some of those kinases are described as major virulence factors in Toxoplasma gondii, secreted into the host cell to hijack signaling pathways. Most of those kinases remain to be characterized in Eimeria tenella. Deciphering their cellular function is a prerequisite to supporting their relevance as a druggable target in development of new means of Eimeria tenella control. Secreted divergent kinases that interact with host cell partners to modulate pathways are good candidates, as they coevolve with their host targets to ensure their function within the host and are less prone to mutations that would lead to drug resistance. The absence of any orthologous kinase in host cells makes these parasite kinases a promising drug target candidate.

Identification of Biological Risk Genes and Candidate Drugs for Psoriasis Vulgaris by Utilizing the Genomic Information

Psoriasis is an autoimmune disease that causes inflammation on the skin's surface, characterized by the appearance of pink plaques covered with white scales. Currently, the availability of psoriasis vulgaris therapy is still limited. Therefore, considering the discovery of new drug candidates by utilizing genetic variations, such as single nucleotide polymorphisms (SNP) through drug repurposing, is a profitable method. The SNP associated with psoriasis was obtained from Genome-Wide Association Studies (GWAS) and Phenom-Wide Association Studies (PheWAS) databases. We identified 245 SNPs associated with psoriasis vulgaris with criteria of r2 >0.8. To prioritize the candidate of a gene associated with psoriasis, we used five criteria of functional annotation (missense/nonsense, cis-eQTL, PPI, KEGG, and KO mice) where if there were more than two criteria of assessment, they were defined as the risk gene of psoriasis vulgaris. Fifty-two genes were identified as the risk gene of psoriasis vulgaris, then expanded using the STRING database to obtain more gene candidates of drug targets. The result is 104 genes candidates for drug targets, of which 24 overlapped with 96 drugs, according to DrugBank. Of the 96 drugs that have been approved for other indications, we found that five drugs (ustekinumab, tildrakizumab, risankizumab, guselkumab, and etanercept) are currently in clinical trials for the treatment of psoriasis that target two genes (IL23A and TNF). We argue that these two genes are the most promising targets based on their high target scores on functional annotations. This research explains the potential that utilizing genomic variation can contribute to drug discovery.

Discovery of CLEC2B as a diagnostic biomarker and screening of celastrol as a candidate drug for psoriatic arthritis through bioinformatics analysis

BackgroundPsoriatic arthritis (PSA) is a chronic, immune-mediated inflammatory joint disease that is liked to mortality due to cardiovascular disease. Diagnostic markers and effective therapeutic options for PSA remain limited due to the lack of understanding of the pathogenesis. We aimed to identify potential diagnostic markers and screen the therapeutic compounds for PSA based on bioinformatics analysis.MethodsDifferentially expressed genes (DEGs) of PSA were identified from the GSE61281 dataset. WGCNA was used to identify PSA-related modules and prognostic biomarkers. Clinical samples were collected to validate the expression of the diagnostic gene. These DEGs were subjected to the CMap database for the identification of therapeutic candidates for PSA. Potential pathways and targets for drug candidates to treat PSA were predicted using Network Pharmacology. Molecular docking techniques were used to validate key targets.ResultsCLEC2B was identified as a diagnostic marker for PSA patients (AUC > 0.8) and was significantly upregulated in blood samples. In addition, celastrol was identified as a candidate drug for PSA. Subsequently, the network pharmacology approach identified four core targets (IL6, TNF, GAPDH, and AKT1) of celastrol and revealed that celastrol could treat PSA by modulating inflammatory-related pathways. Finally, molecular docking demonstrated stable binding of celastrol to four core targets in the treatment of PSA. Animal experiments indicated celastrol alleviated inflammatory response in the mannan-induced PSA.ConclusionCLEC2B was a diagnostic marker for PSA patients. Celastrol was identified as a potential therapeutic drug for PSA via regulating immunity and inflammation.

Histoplasma capsulatum Relies on Tryptophan Biosynthesis To Proliferate within the Macrophage Phagosome.

Histoplasma capsulatum yeasts reside and proliferate within the macrophage phagosome during infection. This nutrient-depleted phagosomal environment imposes challenges to Histoplasma yeasts for nutrition acquisition. Histoplasma yeasts require all 20 amino acids, which can be formed by de novo biosynthesis and/or acquired directly from the phagosomal environment. We investigated how Histoplasma obtains aromatic amino acids (i.e., phenylalanine, tyrosine, and tryptophan) within the phagosome during infection of macrophages. Depletion of key enzymes of the phenylalanine or tyrosine biosynthetic pathway neither impaired Histoplasma's ability to proliferate within macrophages nor resulted in attenuated virulence in vivo. However, loss of tryptophan biosynthesis resulted in reduced growth within macrophages and severely attenuated virulence in vivo. Together, these results indicate that phenylalanine and tyrosine, but not tryptophan, are available to Histoplasma within the macrophage phagosome. The herbicide glyphosate, which targets 5-enolpyruvylshikimate-3-phosphate synthase of the aromatic amino acid biosynthetic pathway, inhibited Histoplasma yeast growth, and this growth inhibition was partially reversed by aromatic amino acid supplementation or overexpression of ARO1. These results suggest that the aromatic amino acid biosynthetic pathway is a candidate drug target to develop novel antifungal therapeutics.

Translational protein RpsE as an alternative target for novel nucleoside analogues to treat MDR Enterobacter cloacae ATCC 13047: network analysis and molecular dynamics study

The pathogenic Enterobacter cloacae subsp. cloacae str. ATCC 13047 has contemporarily emerged as a multi-drug resistant strain. To formulate an effective treatment option, alternative therapeutic methods need to be explored. The present study focused on Gene Interaction Network study of 46 antimicrobial resistance genes to reveal the densely interconnecting and functional hub genes in E. cloacae ATCC 13047. The AMR genes were subjected to clustering, topological and functional enrichment analysis, revealing rpsE (RpsE), acrA (AcrA) and arnT (ArnT) as novel therapeutic drug targets for hindering drug resistance in the pathogenic strain. Network topology further indicated translational protein RpsE to be exploited as a promising drug-target candidate for which the structure was predicted, optimized and validated through molecular dynamics simulations (MDS). Absorption, distribution, metabolism and excretion screening recognized ZINC5441082 (N-Isopentyladenosine) (Lead_1) and ZINC1319816 (cyclopentyl-aminopurinyl-hydroxymethyl-oxolanediol) (Lead_2) as orally bioavailable compounds against RpsE. Molecular docking and MDS confirmed the binding efficacy and protein-ligand complex stability. Furthermore, binding free energy (Gbind) calculations, principal component and free energy landscape analyses affirmed the predicted nucleoside analogues against RpsE protein to be comprehensively examined as a potential treatment strategy against E. cloacae ATCC 13047.

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug-Target Binding Affinity Prediction.

The accurate prediction of drug-target binding affinity (DTA) is an essential step in drug discovery and drug repositioning. Although deep learning methods have been widely adopted for DTA prediction, the complexity of extracting drug and target protein features hampers the accuracy of these predictions. In this study, we propose a novel model for DTA prediction named MSGNN-DTA, which leverages a fused multi-scale topological feature approach based on graph neural networks (GNNs). To address the challenge of accurately extracting drug and target protein features, we introduce a gated skip-connection mechanism during the feature learning process to fuse multi-scale topological features, resulting in information-rich representations of drugs and proteins. Our approach constructs drug atom graphs, motif graphs, and weighted protein graphs to fully extract topological information and provide a comprehensive understanding of underlying molecular interactions from multiple perspectives. Experimental results on two benchmark datasets demonstrate that MSGNN-DTA outperforms the state-of-the-art models in all evaluation metrics, showcasing the effectiveness of the proposed approach. Moreover, the study conducts a case study based on already FDA-approved drugs in the DrugBank dataset to highlight the potential of the MSGNN-DTA framework in identifying drug candidates for specific targets, which could accelerate the process of virtual screening and drug repositioning.