Drug Target Proteins Research Articles

Network pharmacology and molecular docking to explore the treatment potential and molecular mechanism of Si-Miao decoction against gouty arthritis.

Gouty arthritis (GA) is a common metabolic rheumatological disease. Si-Miao decoction has therapeutic effects on GA. In our study, we investigated the mechanism of Si-Miao decoction against GA using network pharmacology and molecular docking analytical methods. The Traditional Chinese Medicine Systems Pharmacology Database was used as the basis for screening the main targets and agents of the Si-Miao decoction, and the Genecards, OMIM, and Drugbank databases were used to screen GA-related targets. They were analyzed using Venn with the drug targets to obtain the intersection targets. We used Cytoscape 3.9.1 to draw the "Drugs-Compounds-Targets" network and the String database for creative protein-protein interaction networks of target genes and filtered core targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to analyze the core targets. Molecular docking was performed using AutoDockTools to predict the binding capacity between nuclear targets and active components in the Si-Miao decoction. A total of 50 chemically active components containing 53 common targets of Si-Miao decoction anti-GA and 53 potential drug target proteins were identified. Core targets, namely, TNF, STAT3, SRC, PPARG, TLR4, PTGS2, MMP9, RELA, TGFB1, and SIRT1, were obtained through PPI network analysis. GO and KEGG analyses showed that the mechanism of anti-GA in Si-Miao decoction may proceed by regulating biological processes such as inflammatory factor levels, cell proliferation, apoptosis, and lipid and glucose metabolism, and modulating the NOD-like receptor signaling pathway, IL-17 signaling pathway, TNF signaling pathway, NF-kappa B signaling pathway, and Toll-like receptor signaling pathway. We further screened the core targets, including PTGS2, MMP9, and PPAGR, as receptor proteins based on their degree value and molecular docking with the main active compounds in Si-Miao decoction, and found that baicalein had high affinity. In conclusion, Si-Miao decoction, through anti-inflammatory, apoptosis-regulating, and anti-oxidative stress action mechanisms in the treatment of GA.

Identifying potential drug targets for non-alcoholic fatty liver disease: a drug target Mendelian randomization study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Research and Innovation Training Grant: Centre for Doctoral Training in AI-enabled Healthcare Systems Background Non-alcoholic fatty liver disease (NAFLD), a range of conditions caused by the build-up of fat in the liver, is the most common form of chronic liver disease, with an estimated prevalence of ~25% globally. NAFLD is associated with increased risk of all-cause mortality, with CVD emerging as the primary cause of death amongst affected individuals. The aetiology of NAFLD is not yet clearly understood, and currently no drugs exist for its treatment, with therapeutics aimed at symptom management instead. Purpose To identify and prioritise candidate protein drug targets and pathways relevant for therapeutic intervention in NAFLD. Methods To identify plasma proteins causally associated with NAFLD, we analysed genetic associations on 1,561 proteins from 35,559 participants in the deCODE study, and compared these to associations from a GWAS of biopsy confirmed NAFLD cases (1,483 cases, 17,781 controls). We sourced genetic variants from a 400kbp region around each protein-coding gene, pruning variants on an F-statistic of 15, minor allele frequency threshold of 1% and linkage disequilibrium r-squared of 0.4. cis-Mendelian randomization (MR) was used to identify proteins associated with NAFLD. Bias due to pleiotropy was limited by excluding variants with leverage statistic more than 3 times the mean, or outlier statistic larger than 10.83. We used colocalization analysis to identify proteins with a posterior probability of at least 0.8, indicating the presence of common causal variants. We further prioritised proteins by annotating them with information on druggability (i.e. whether a protein is a target for a known drug, or predicted to be a target for a drug), mRNA expression in pathology-relevant tissues, and involvement in known biological pathways. Results We identified 5 proteins with a multiplicity-corrected (p<3.2×10-5) association with NAFLD, and strong evidence of genetic colocalization: CYB5A, NT5C, NCAN, TGFBI, DAPK2. TGFBI and DAPK2 are potentially druggable, with all five found to be expressed in both liver and adipose tissues. Pathway analysis indicated the involvement of NT5C, CYB5A, NCAN in Reactome pathway R-HSA-1430728 reflecting cellular energy metabolism, including mitochondrial lipid metabolism, which is strongly implicated with NAFLD. Conclusion We identified a prioritised set of five plasma proteins influencing NAFLD, including druggable proteins informing de novo and ongoing drug development programs. Figure: MR effect estimates of proteins on NAFLD A: Effect of each protein on NAFLD. Each point represents a protein, effect estimates are reported in log(odds ratio) and statistical significance in -log(p-value). Coloured points represent proteins passing the multiplicity-corrected p-value threshold B: MR effect estimates of prioritised proteins. Estimates are reported in log(odds ratio). Proteins are annotated according to their druggability based on the British National Formulary and ChEMBL

Proteomic Profiling of Antimalarial Plasmodione Using 3-Benz(o)ylmenadione Affinity-Based Probes.

Understanding the mechanisms of drug action in malarial parasites is crucial for the development of new drugs to combat infection and to counteract drug resistance. Proteomics is a widely used approach to study host-pathogen systems and to identify drug protein targets. Plasmodione is an antiplasmodial early-lead drug exerting potent activities against young asexual and sexual blood stages in vitro with low toxicity to host cells. To elucidate its molecular mechanisms, an affinity-based protein profiling (AfBPP) approach was applied to yeast and P. falciparum proteomes. New (pro-) AfBPP probes based on the 3-benz(o)yl-6-fluoro-menadione scaffold were synthesized. With optimized conditions of both photoaffinity labeling and click reaction steps, the AfBPP protocol was then applied to a yeast proteome, yielding 11 putative drug-protein targets. Among these, we found four proteins associated with oxidoreductase activities, the hypothesized type of targets for plasmodione and its metabolites, and other proteins associated with the mitochondria. In Plasmodium parasites, the MS analysis revealed 44 potential plasmodione targets that need to be validated in further studies. Finally, the localization of a 3-benzyl-6-fluoromenadione AfBPP probe was studied in the subcellular structures of the parasite at the trophozoite stage.

Population-based nanopore sequencing of the HIV-1 pangenome to identify drug resistance mutations.

HIV-1 drug resistance genotypic tests have primarily been performed by Sanger sequencing of gene segments encoding different drug target proteins. Since the number of targets has increased with the addition of a new class of antiretroviral drugs, a simple high-throughput system for assessing nucleotide sequences throughout the HIV-1 genome is required. Here, we developed a new solution using nanopore sequencing of viral pangenomes amplified by PCR. Benchmark tests using HIV-1 molecular clones demonstrated an accuracy of up to 99.9%. In addition, validation tests of our protocol in 106 clinical samples demonstrated high concordance of drug resistance and tropism genotypes (92.5% and 98.1%, respectively) between the nanopore sequencing-based results and archived clinical determinations made based on Sanger sequencing data. These results suggest that our new approach will be a powerful solution for the comprehensive survey of HIV-1 drug resistance mutations in clinical settings.

Druggability Analysis of Protein Targets for Drug Discovery to Combat Listeria monocytogenes.

Extensive research has been conducted to identify key proteins governing stress responses, virulence, and antimicrobial resistance, as well as to elucidate their interactions within Listeria monocytogenes. While these proteins hold promise as potential targets for novel strategies to control L. monocytogenes, given their critical roles in regulating the pathogen's metabolism, additional analysis is needed to further assess their druggability-the chance of being effectively bound by small-molecule inhibitors. In this work, 535 binding pockets of 46 protein targets for known drugs (mainly antimicrobials) were first analyzed to extract 13 structural features (e.g., hydrophobicity) in a ligand-protein docking platform called Molsoft ICM Pro. The extracted features were used as inputs to develop a logistic regression model to assess the druggability of protein binding pockets, with a value of one if ligands can bind to the protein pocket. The developed druggability model was then used to evaluate 23 key proteins from L. monocytogenes that have been identified in the literature. The following proteins are predicted to be high-potential druggable targets: GroEL, FliH/FliI complex, FliG, FlhB, FlgL, FlgK, InlA, MogR, and PrfA. These findings serve as an initial point for future research to identify specific compounds that can inhibit druggable target proteins and to design experimental work to confirm their effectiveness as drug targets.

Protein sequence analysis in the context of drug repurposing

MotivationDrug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs.MethodsIn this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases.ResultsWe found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.

Mechanism of Si Ni San Combined with Astragalus in Treating Hepatic Fibrosis: A Network Pharmacology and Molecular Docking Study.

Si Ni San combined with Astragalus (SNSQ) has demonstrated significant efficacy in the treatment of hepatic fibrosis (HF), as confirmed by clinical practice. However, its pharmacological mechanism remains unclear. This study employs network pharmacology to identify key targets and proteins for molecular docking. Additionally, animal experiments were conducted to validate the network pharmacology results, providing further insights into the mechanism of SNSQ in treating HF. Effective compounds of SNSQ were screened from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Encyclopedia of Traditional Chinese Medicine (ETCM) databases. Molecular formula structures of these effective compounds were obtained from the PubChem database. Partial target proteins with a probability greater than 0.6 were sourced from the SWISS database. Uniprot IDs corresponding to these target proteins were retrieved from the SUPERPRED database. The remaining target proteins of the compounds were obtained from the Uniprot database based on the Uniprot IDs. The drug target proteins were then summarized. Target points related to HF were selected from the GeneCards and OMIM databases. Common target points were identified in the Venn diagram and imported into Cytoscape 3.9.1 software to construct the "SNSQ-effective compound-target pathway-HF" network. AutoDock software was used for molecular docking of compounds and target proteins with high-degree values. The common target points underwent GO function enrichment and KEGG pathway enrichment analysis using the DAVID database. An HF rat model was established, and serum AST and ALT activities were measured. The Hyp assay kit was utilized to detect the Hyp content in liver tissue. To the transcription levels of pro-inflammatory factors (IL-1β, TNF-α, IL-6) and anti-inflammatory factors (IL-10, TGF-β1, IL-4) in rat serum and liver.IL-1β, TNF-α, IL-10, and TGF-β1 were chosen for validation through ELISA. Western blotting and qRT-PCR were used to assess the expression of related proteins, namely NFKB1, NF-κBp65, NF-κBp50, α-SMA, and Col-1 in liver tissue. qRT-PCR was also employed to study the expression of ECM synthesis and proliferation-related genes, including Cyclin D1, TIMP1, COL1A1 in HSC-T6 cells and rat liver tissue, as well as the inhibition of the ECM-related gene MMP13 in HSC-T6 cells and rat liver tissue. A total of 16 valid compounds were predicted, with kaempferol, sitosterol, and isorhamnetin exhibiting high-degree values. KEGG enrichment analysis revealed that the target genes of SNSQ were enriched in multiple pathological pathways, with the NF-Kappa B signaling pathway being predominant. Molecular docking simulations indicated strong affinities between SNSQ's primary components-kaempferol, sitosterol, isorhamnetin-and NFKB1. Experimental results demonstrated significant reductions in AST, ALT, and Hyp levels in the SNSQ group. Pro-inflammatory factors (IL-1β, TNF-ɑ) were markedly reduced, while anti-inflammatory factors (IL-10, TGF-β1) were substantially increased. The protein expression and transcription levels of α-SMA and Col-1 were significantly decreased, whereas those of NFKB1, NF-κBp65, and NF-κBp50 were notably elevated. mRNA expression levels of Cyclin D1, TIMP1, COL1A1 in HSC-T6 cells and rat liver tissue were significantly decreased, whereas MMP13 mRNA expression level was significantly increased. Treatment of HF with SNSQ involves multiple targets and pathways, with a close association with the overexpression of NFKB1 and activation of the NF-Kappa B signaling pathway. Its mechanism is closely linked to the activation of inflammatory responses, HSC activation, and proliferation.

Abstract PO5-13-09: Clinical usage of a CLIA accredited protein/phosphoprotein assay for breast cancer: Primary usage cases and significant findings in the HER2- setting

Abstract Background: A CLIA-accredited assay that measures the expression and/or activation levels of 32 clinically actionable protein and phosphoprotein analytes in clinical tissue biopsy samples was launched in January 2021. The current intended use population for the assay is for patients with late/advanced stage breast cancer wherein molecular profiling is being utilized for clinical decision making. Assessment of physician ordering practices, clinical use dynamics as well as protein/phosphoprotein actionability frequencies were determined for clinically actionable biomarkers. Methods: A total of 219 cases have been processed to date, with mean turn-around time of 16.4 days. The assay utilizes laser capture microdissection to enrich tumor epithelium from FFPE core needle biopsy and resection material coupled to reverse phase protein array. Results for each of the 32 analytes are generated by comparing each patient to a reference population and reported both as a relative quartile and as a continuous percentile compared to the reference population (0-100%). For the purposes of this analysis, significant expression/activation cutpoints were set at the 40th percentile threshold. Results: To date, clinicians have requested the test most frequently to identify potential therapeutic targets in patients with HER2-negative disease (84% of all tested patients, 36% were triple-negative). Despite the majority of cases being HER2-negative, we found that phosphorylated (p) HER2 (Y1248) was found to be activated in 17% of all HER2-negative cases and 19% of triple negative breast cancer cases (TNBC). pHER2 was concurrently activated with pHER3 (Y1289) in 15% (12/80) of TNBC cases and 13% (24/187) of all HER2-negative cases. pHER2 presented concurrently with pEGFR (Y1068 or T654) in 14% (11/80) of TNBC cases and 14% (26/187) of all HER2- cases. Expression of TROP2 was observed in 16% of HER2-negative patients including 11% of ER+/HER2- and 23% of TNBC cancers. MHCII expression, a recently described candidate predictive biomarker of anti-PDL1/PD1 response in TNBC, was found to be expressed in 29% of TNBC patients. Activation frequencies of other clinically relevant drug targets in HER2-negative disease such AKT and mTOR pathway activation was also assessed. We found that pAKT (S473 and/or T308) was activated in 7% of ER+/HER2- cases and 14% of TNBC. Concurrent with AKT activation, at least one additional member of the AKT-mTOR pathway (mTORC1 (S2448), 4E-BP1 (S65), p70S6K (T389), or S6RP (S235/S236) was co-activated in 50% and 45% of ER+/HER2- and TNBC cancers respectively. Conclusion: This initial assessment of a CLIA LDT protein/phosphoprotein assay utilized to date found that physicians most often order the test for advanced stage HER2-negative breast cancer. Activation/expression of clinically important protein drug targets with potential actionability in HER2-negative disease was observed in a significant number of cases and demonstrates the power of the assay to elucidate actionable targets for potential therapeutic intervention. Citation Format: Claudius Mueller, Brian Corgiat, Emanuel Petricoin, Kayla Sparks, Joyce O'Shaughnessy, Chelsea Gawryletz, Matthew Schwartz, Justin Davis. Clinical usage of a CLIA accredited protein/phosphoprotein assay for breast cancer: Primary usage cases and significant findings in the HER2- setting [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-09.

QUBO Problem Formulation of Fragment-Based Protein-Ligand Flexible Docking.

Protein-ligand docking plays a significant role in structure-based drug discovery. This methodology aims to estimate the binding mode and binding free energy between the drug-targeted protein and candidate chemical compounds, utilizing protein tertiary structure information. Reformulation of this docking as a quadratic unconstrained binary optimization (QUBO) problem to obtain solutions via quantum annealing has been attempted. However, previous studies did not consider the internal degrees of freedom of the compound that is mandatory and essential. In this study, we formulated fragment-based protein-ligand flexible docking, considering the internal degrees of freedom of the compound by focusing on fragments (rigid chemical substructures of compounds) as a QUBO problem. We introduced four factors essential for fragment-based docking in the Hamiltonian: (1) interaction energy between the target protein and each fragment, (2) clashes between fragments, (3) covalent bonds between fragments, and (4) the constraint that each fragment of the compound is selected for a single placement. We also implemented a proof-of-concept system and conducted redocking for the protein-compound complex structure of Aldose reductase (a drug target protein) using SQBM+, which is a simulated quantum annealer. The predicted binding pose reconstructed from the best solution was near-native (RMSD = 1.26 Å), which can be further improved (RMSD = 0.27 Å) using conventional energy minimization. The results indicate the validity of our QUBO problem formulation.

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals.

Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes.

Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.

Identification of prospective aging drug targets via Mendelian randomization analysis.

Aging represents a multifaceted process culminating in the deterioration of biological functions. Despite the introduction of numerous anti-aging strategies, their therapeutic outcomes have often been less than optimal. Consequently, discovering new targets to mitigate aging effects is of critical importance. We applied Mendelian randomization (MR) to identify potential pharmacological targets against aging, drawing upon summary statistics from both the Decode and FinnGen cohorts, with further validation in an additional cohort. To address potential reverse causality, bidirectional MR analysis with Steiger filtering was utilized. Additionally, Bayesian co-localization and phenotype scanning were implemented to investigate previous associations between genetic variants and traits. Summary-data-based Mendelian randomization (SMR) analysis was conducted to assess the impact of genetic variants on aging via their effects on protein expression. Additionally, mediation analysis was orchestrated to uncover potential intermediaries in these associations. Finally, we probed the systemic implications of drug-target protein expression across diverse indications by MR-PheWas analysis. Utilizing a Bonferroni-corrected threshold, our MR examination identified 10 protein-aging associations. Within this cohort of proteins, MST1, LCT, GMPR2, PSMB4, ECM1, EFEMP1, and ISLR2 appear to exacerbate aging risks, while MAX, B3GNT8, and USP8 may exert protective influences. None of these proteins displayed reverse causality except EFEMP1. Bayesian co-localization inferred shared variants between aging and proteins such as B3GNT8 (rs11670143), ECM1 (rs61819393), and others listed. Mediator analysis pinpointed 1,5-anhydroglucitol as a partial intermediary in the influence LCT exhibits on telomere length. Circulating proteins play a pivotal role in influencing the aging process, making them promising candidates for therapeutic intervention. The implications of these proteins in aging warrant further investigation in future clinical research.

Identification of an inhibitory pocket in falcilysin provides a new avenue for malaria drug development

Identification of new druggable protein targets remains the key challenge in the current antimalarial development efforts. Here we used mass-spectrometry-based cellular thermal shift assay (MS-CETSA) to identify potential targets of several antimalarials and drug candidates. We found that falcilysin (FLN) is a common binding partner for several drug candidates such as MK-4815, MMV000848, and MMV665806 but also interacts with quinoline drugs such as chloroquine and mefloquine. Enzymatic assays showed that these compounds can inhibit FLN proteolytic activity. Their interaction with FLN was explored systematically by isothermal titration calorimetry and X-ray crystallography, revealing a shared hydrophobic pocket in the catalytic chamber of the enzyme. Characterization of transgenic cell lines with lowered FLN expression demonstrated statistically significant increases in susceptibility toward MK-4815, MMV000848, and several quinolines. Importantly, the hydrophobic pocket of FLN appears amenable to inhibition and the structures reported here can guide the development of novel drugs against malaria.

Identification of Drug Targets and Agents Associated with Ferroptosis-related Osteoporosis through Integrated Network Pharmacology and Molecular Docking Technology.

Osteoporosis is a systemic bone disease characterized by progressive reduction of bone mineral density and degradation of trabecular bone microstructure. Iron metabolism plays an important role in bone; its imbalance leads to abnormal lipid oxidation in cells, hence ferroptosis. In osteoporosis, however, the exact mechanism of ferroptosis has not been fully elucidated. The main objective of this project was to identify potential drug target proteins and agents for the treatment of ferroptosis-related osteoporosis. In the current study, we investigated the differences in gene expression of bone marrow mesenchymal stem cells between osteoporosis patients and normal individuals using bioinformatics methods to obtain ferroptosis-related genes. We could predict their protein structure based on the artificial intelligence database of AlphaFold, and their target drugs and binding sites with the network pharmacology and molecular docking technology. We identified five genes that were highly associated with osteoporosis, such as TP53, EGFR, TGFB1, SOX2 and MAPK14, which, we believe, can be taken as the potential markers and targets for the diagnosis and treatment of osteoporosis. Furthermore, we observed that these five genes were highly targeted by resveratrol to exert a therapeutic effect on ferroptosis-related osteoporosis. We examined the relationship between ferroptosis and osteoporosis based on bioinformatics and network pharmacology, presenting a promising direction to the pursuit of the exact molecular mechanism of osteoporosis so that a new target can be discovered for the treatment of osteoporosis.

Targeting MurB from Helicobacter pylori: insights from virtual screening, molecular docking and molecular dynamics simulation

ABSTRACT Helicobacter pylori is a gram-negative, helical-shaped bacteria found in the mucus lining of the stomach of humans responsible for causing sores or an ulcer. H. pylori is becoming a multi-drug resistant bacterium, which in turn demands the need for development of alternative treatment strategies for the proper management of diseases. The peptidoglycan biosynthesis pathway is critical for the synthesis of cell walls and for the survival of H. pylori. There are several crucial enzymes in this biosynthesis pathway which can act as significant drug targets against H. pylori. One of these important enzymes is UDP-N-acetylenolpyruvoylglucosamine reductase (MurB), which catalyses an important committed step of cell wall biosynthesis. In the present study, we performed a high throughput virtual screening using the Enamine HTSC library against the MurB enzyme. The molecules were also screened for druglike properties by ADME screening, toxicity, and tumorigenic nature, followed by MD simulations. We found that Z102621114_1 and Z228235240_1 ligand molecules can act as potential inhibitors of MurB protein. Our results suggest, these molecules may be able to meet the demand for novel antimicrobials to treat H. pylori medication resistance. The subsequent experimental investigations can increase their potential as a novel agents against H. pylori infections. Research highlights Helicobacter pylori infects 50% of the Global population. Helicobacter pylori infection is associated with gastric cancer. MurB is a key drug target protein as it is responsible for cell wall synthesis. Z102621114_1 and Z228235240_1 were identified as potential inhibitors of MurB of Helicobacter pylori.

Abstract 1154: Spatial profiling of the tumor microenvironment in head and neck squamous cell carcinoma revealed immune cell type infiltration into the TME as a predictor of immunotherapy response

Abstract Background: Immunotherapy has shown promising results in advanced head and neck cancer patients, however, only a subset of patients are responsive to immune checkpoint blockade therapy. The molecular and cellular characterization of the tumor microenvironment (TME) may reveal clues as to why some head and neck tumors are responsive or resistant to the therapy. Methods: Formalin-fixed paraffin-embedded (FFPE) tissue samples were collected from 34 patients at two major Queensland Hospitals with advanced head and neck squamous cell carcinoma (HNSCC), prior to immunotherapy. To investigate the cellular and molecular characteristics of the HNSCC TME, we used the Nanostring GeoMx Digital Spatial Profiler (DSP) spanning modules in an immune-oncology panel, which included immune cell typing and profiling, cell death, PI3K/AKT signaling, drug target, immune activation, and pan tumor protein biomarkers. A deeper structural analysis was performed using the Nanostring whole transcriptome atlas (WTA) panel of tertiary lymphoid structures (TLSs) and germinal centers (GCs). Results: Out of 34 patients, 3 had a complete response (CR), 7 had a partial response (PR), 5 had stable disease (SD), and 19 had progressive disease (PD), based on the Response Evaluation Criteria in Solid Tumors (RECIST). When we compared patient responders (patients with CR, PR, and SD) to patient non-responders (patients with PD), we found that the former had a higher infiltration of immune cell type markers representatives of CD8+ T cells, dendritic cells, and macrophages, into their tumor microenvironment than the latter. Then, to investigate differentially expressed genes between patients with complete response and patients with partial response to immunotherapy, we performed DE analyses and we found that patients with PR had higher Ki-67 expression than those with CR. Spatial transcriptomic mapping of TLS’s (peripheral, intra-tumoral) and normal germinal centers revealed interferon pathway enrichment in the TLS’s. In a subset, these appear to be linked with HPV viral infection. Conclusion: In this study, we found that immune cell type infiltration into the tumor microenvironment of head and neck cancers could be predictive of response to immunotherapy. Furthermore, it was revealed that patients with a high expression of Ki-67 might be less likely to respond completely to immunotherapy. Spatially resolved whole transcriptome analysis of HNSCC tissues revealed the presence of tertiary lymphoid structures with a higher enrichment of interferon pathway genes. Citation Format: Habib Sadeghirad, Chin Wee Tan, Ning Liu, James Monkman, Caroline Cooper, Ken O’Byrne, Melissa Davis, Brett Hughes, Arutha Kulasinghe. Spatial profiling of the tumor microenvironment in head and neck squamous cell carcinoma revealed immune cell type infiltration into the TME as a predictor of immunotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1154.

Abstract 1127: Single cell network-based analyses reveal dose-dependent increase in myeloid suppressive cell sub-populations in response to radiation therapy

Abstract Background: More than half of all cancer patients undergo radiation therapy (RT) with curative or palliative intent. With the widespread use of immunotherapy to treat cancer, immune effects of radiation are an important consideration in design of sequential and combination therapies. While radiation has been found to deplete lymphocytes, less is known about its effects on the myeloid compartment, which includes both immune activating and suppressive populations. We hypothesized that a network-based analysis of single cell proteogenomic data generated from tumor-infiltrating immune cells can identify druggable proteins in pathways driving unwanted immunosuppressive changes with radiation, particularly among myeloid cells. Methods: Proteogenomic (CITE-Seq) analysis was conducted on CD45+ immune cells from unirradiated and irradiated orthotopic 4T1 murine mammary tumors three- and ten-days after tumor irradiation (8 Gy x 1 or 8 Gy x 3). Based on expression of select surface proteins, analysis of the CITE-Seq data resulted in eight distinct immune clusters, including three myeloid clusters — monocytes, macrophages, and granulocytes. Using VIPER (virtual inference of protein activity by enriched regulon), an algorithm that infers protein activities from weighted expression on each protein’s downstream targets, we identified distinct functional subpopulations within these eight clusters. Further profiling subclusters enriched by RT using the OncoTarget algorithm, we identified druggable proteins with significant inferred activity as potential targets of therapeutic interest. Results: As expected, lymphocytes were persistently depleted after RT on both radiotherapy schedules. However, three distinct subclusters of myeloid cells with immune-suppressive phenotype became enriched following tumor irradiation, with greater enrichment at the lower radiation dose of 8 Gy x 1: TAM2, PMN1, and PMN4. Surface protein expression enabled us to define the immunophenotype and isolate these three cell clusters from murine 4T1 tumors as well as human renal cell carcinoma tumors for further functional analyses. OncoTarget identified a number of druggable targets, including PARP1 for the PMN1 subcluster. Conclusion: Suppressive myeloid subpopulations induced by radiation were identified among the immune cells. Combining radiation therapy with inhibitors of these cell populations may provide therapeutic benefit over radiation therapy alone or in combination with checkpoint inhibitor immunotherapies. Future work will experimentally determine if inhibition of druggable protein targets in these myeloid subpopulations improves the overall efficacy of RT. Citation Format: Aparna Krishnan, Shruti Bansal, Samanta Sarti, Michael D. Kissner, Charles Karan, Andrea Califano, Aleksandar Z. Obradovic, Catherine S. Spina. Single cell network-based analyses reveal dose-dependent increase in myeloid suppressive cell sub-populations in response to radiation therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1127.

Abstract 5776: Target identification, selectivity profiling and binding site mapping of small molecule and peptide drugs by LiP-MS

Abstract Target identification in the cellular context is a critical step for both target-based and phenotypic drug discovery. Limited proteolysis coupled with mass spectrometry (LiP-MS) has emerged as a powerful technique for target deconvolution of small molecules or peptides in cell lysate without compound modification or genetic manipulation of cell lines [1,2,3,4]. Utilizing a non-specific protease under well-controlled conditions, LiP-MS exploits drug-induced structural alteration or steric hindrance on protein targets and utilizes quantitative mass spectrometry to probe over 250’000 peptides covering more than 9000 proteins in the proteome. To aid target ID, this workflow incorporates a 7-concentration dose response experiment and a machine-learning framework [1] to compute a LiP-score to rank identified target proteins, as well as predict potential binding site. In this study, we present target deconvolution results on four small molecule compounds and one peptide compound with very distinct pharmacological profile: 1) staurosporine, a broad-specific kinase inhibitor; 2) roniciclib, a failed clinical-stage pan-CDK inhibitor; 3) selumetinib, an allosteric, non-ATP-competitive MEK 1 and 2 inhibitor; 4) NST-628, a newly developed non-degradating molecular glue inhibiting the RAS/MAPK pathway; and 5) Acetyl-calpastatin, a 24 amino-acid polypeptide inhibitor of calpain 1 and 2. Target ID experiment with staurosporine and roniciclib yielded &amp;gt; 150 and 68 kinase targets were identified respectively. For the pan-kinase inhibitor roniciclib, both cell-cycle CDKs (CDK1/cyclin B, CDK2/cyclin E, CDK4 cyclin D) and transcriptional CDK9 were among the targets. For Selumetinib, LiP-score ranking identified MEK1 and MEK2 as the top two targets, and only kinases among a total of 23 targets. Furthermore, mapping the LiP-peptides from this experiment onto MEK1 showed that they are in close proximity to the selumetinib allosteric binding site on co-crystal structure. Last but not least, we assessed the target landscape of NST-628, a novel non-degrading molecular glue and identified MEK1 and MEK2 clearly as the primary targets. This finding is in line with orthogonal data obtained from X-ray crystallography [5]. Collectively, this data demonstrates that LiP-MS can be deployed to effectively identify protein drug targets and predict binding sites in complex cellular millieu independent of the compound’s mechanism of action and without compound modification or labeling. These capabilities make LiP-MS a powerful addition to the target deconvolution toolbox. [1] Piazza and Beaton et al. Nature Comm 2020 [2] Hendricks and Beaton et al. ACS Chem Bio 2021 [3] AACR 2022 [4] AACR 2023 [5] AACR-NCI-EORTC 2023 Citation Format: Fabio Sabino, Martin Soste, Dominique Kamber, Maria Stella Ritorto, Klaus Hoeflich, Michael Hale, Bradley Quade, Xin Huang, Nigel Beaton, Roland Bruderer, Yuehan Feng, Lukas Reiter. Target identification, selectivity profiling and binding site mapping of small molecule and peptide drugs by LiP-MS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5776.

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening. To solve this problem, a machine learning-based approach with great ability to generalize DTI prediction across molecules is desirable. Many existing machine learning approaches for DTI identification failed to exploit the full information with respect to the topological structures of candidate molecules. To develop a better approach for DTI prediction, we propose GraphormerDTI, which employs the powerful Graph Transformer neural network to model molecular structures. GraphormerDTI embeds molecular graphs into vector-format representations through iterative Transformer-based message passing, which encodes molecules' structural characteristics by node centrality encoding, node spatial encoding and edge encoding. With a strong structural inductive bias, the proposed GraphormerDTI approach can effectively infer informative representations for out-of-sample molecules and as such, it is capable of predicting DTIs across molecules with an exceptional performance. GraphormerDTI integrates the Graph Transformer neural network with a 1-dimensional Convolutional Neural Network (1D-CNN) to extract the drugs' and target proteins' representations and leverages an attention mechanism to model the interactions between them. To examine GraphormerDTI's performance for DTI prediction, we conduct experiments on three benchmark datasets, where GraphormerDTI achieves a superior performance than five state-of-the-art baselines for out-of-molecule DTI prediction, including GNN-CPI, GNN-PT, DeepEmbedding-DTI, MolTrans and HyperAttentionDTI, and is on a par with the best baseline for transductive DTI prediction. The source codes and datasets are publicly accessible at https://github.com/mengmeng34/GraphormerDTI.

Insight into telomere regulation: road to discovery and intervention in plasma drug-protein targets

BackgroundTelomere length is a critical metric linked to aging, health, and disease. Currently, the exploration of target proteins related to telomere length is usually limited to the context of aging and specific diseases, which limits the discovery of more relevant drug targets. This study integrated large-scale plasma cis-pQTLs data and telomere length GWAS datasets. We used Mendelian randomization(MR) to identify drug target proteins for telomere length, providing essential clues for future precision therapy and targeted drug development.MethodsUsing plasma cis-pQTLs data from a previous GWAS study (3,606 Pqtls associated with 2,656 proteins) and a GWAS dataset of telomere length (sample size: 472,174; GWAS ID: ieu-b-4879) from UK Biobank, using MR, external validation, and reverse causality testing, we identified essential drug target proteins for telomere length. We also performed co-localization, Phenome-wide association studies and enrichment analysis, protein-protein interaction network construction, search for existing intervening drugs, and potential drug/compound prediction for these critical targets to strengthen and expand our findings.ResultsAfter Bonferron correction (p < 0.05/734), RPN1 (OR: 0.96; 95%CI: (0.95, 0.97)), GDI2 (OR: 0.94; 95%CI: (0.92, 0.96)), NT5C (OR: 0.97; 95%CI: (0.95, 0.98)) had a significant negative causal association with telomere length; TYRO3 (OR: 1.11; 95%CI: (1.09, 1.15)) had a significant positive causal association with telomere length. GDI2 shared the same genetic variants with telomere length (coloc.abf-PPH 4 > 0.8).ConclusionGenetically determined plasma RPN1, GDI2, NT5C, and TYRO3 have significant causal effects on telomere length and can potentially be drug targets. Further exploration of the role and mechanism of these proteins/genes in regulating telomere length is needed.