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Annotating the druggable genome estimates the potential maximum size of the playing field for current small-molecule drug design but It does not consider biologicals or future breakthroughs in medicinal chemistry or biology.
SUMMARY: The druggable genome encompasses the genes that are known or predicted to interact with drugs. The Drug-Gene Interaction Database (DGIdb) provides an integrated resource for discovering and contextualizing these interactions, supporting a broad range of research and clinical applications. DGIdb is currently accessed through structured web interfaces and API calls, requiring users to translate natural-language questions into database-specific query patterns. To allow for the use of DGIdb through natural language, we developed the DGIdb Model Context Protocol (MCP) server, which allows large language models (LLMs) access to up-to-date information through the DGIdb API. We demonstrate that the MCP server improves an LLM's ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. AVAILABILITY AND IMPLEMENTATION: The DGIdb MCP server is detailed at https://github.com/dgidb/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.
SUMMARY: The druggable genome encompasses the genes that are known or predicted to interact with drugs. The Drug-Gene Interaction Database (DGIdb) provides an integrated resource for discovering and contextualizing these interactions, supporting a broad range of research and clinical applications. DGIdb is currently accessed through structured web interfaces and API calls, requiring users to translate natural-language questions into database-specific query patterns. To allow for the use of DGIdb through natural language, we developed the DGIdb Model Context Protocol (MCP) server, which allows large language models (LLMs) access to up-to-date information through the DGIdb API. We demonstrate that the MCP server greatly enhances an LLM's ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. AVAILABILITY AND IMPLEMENTATION: The DGIdb MCP server is detailed at https://github.com/griffithlab/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.
To identify therapeutic targets limiting glioblastoma invasion, we applied druggable genome CRISPRi screens and multiomic analysis to patient-derived glioblastoma cells in micro-dissectible biomimetic 3D hydrogels that permitted separation and analysis of core versus invasive fractions. Of 2,550 genes screened, 12 encoded druggable targets whose suppression limited invasion, of which AURKB (encoding aurora kinase B) and ACP1 (encoding low molecular weight protein tyrosine phosphatase, LMW-PTP) were validated in neurosphere assays and in vivo. Proximity labeling identified cortactin as a link between LMW-PTP and aurora B, and we observed that cortactin underwent serine phosphorylation by aurora B and tyrosine dephosphorylation by LMW-PTP. Targeting ACP1 or AURKB via CRISPRi or inhibitors in culture and in vivo shifted the cortactin phosphorylation balance in glioblastoma, reducing levels of cortactin and the actin-related protein 2/3 (Arp2/3) complex that mediates cortactin-induced actin stabilization, thereby reducing actin-cortactin-Arp2/3 colocalization and subsequent actin polymerization. AURKB or ACP1 targeting shifted actin from cytoplasm to the nucleus, reducing mesenchymal gene expression. Biophysical analysis implicated AURKB in glioblastoma cell adhesion and stiffness needed for initial migration and ACP1 in mechanical stress resistance required for later migration. These findings revealed a targetable axis balancing kinase and phosphatase activities to regulate actin polymerization during glioblastoma invasion.
To identify new therapeutic targets that limit glioblastoma (GBM) invasion, we applied druggable-genome CRISPR screens to patient-derived GBM cells in micro-dissectible biomimetic 3D hydrogel platforms that permit separation and independent analysis of core vs. invasive fractions. We identified 12 targets whose suppression limited invasion, of which ACP1 (LMW-PTP) and Aurora Kinase B (AURKB) were validated in neurosphere assays. Proximity labeling analysis identified cortactin as an ACP1-AURKB link, as cortactin undergoes serine phosphorylation by AURKB and tyrosine dephosphorylation by ACP1. Suppression of ACP1 or AURKB in culture and in vivo shifted the balance of cortactin phosphorylation in GBM and reduced actin polymerization and actin-cortactin co-localization. Additional biophysical analysis implicated AURKB in GBM cell adhesion and cortical stiffness, and ACP1 in resistance to mechanical stress and shape plasticity needed for 3D migration. These findings reveal a novel targetable axis that balances kinase and phosphatase activities to regulate actin polymerization during GBM invasion.
Structural genomics projects are producing protein structure data at an unprecedented rate. In this paper, we present the Target Informatics Platform (TIP), a novel structural informatics approach for amplifying the rapidly expanding body of experimental protein structure information to enhance the discovery and optimization of small molecule protein modulators on a genomic scale. In TIP, existing experimental structure information is augmented using a homology modeling approach, and binding sites across multiple target families are compared using a clique detection algorithm. We report here a detailed analysis of the structural coverage for the set of druggable human targets, highlighting drug target families where the level of structural knowledge is currently quite high, as well as those areas where structural knowledge is sparse. Furthermore, we demonstrate the utility of TIP's intra- and inter-family binding site similarity analysis using a series of retrospective case studies. Our analysis underscores the utility of a structural informatics infrastructure for extracting drug discovery-relevant information from structural data, aiding researchers in the identification of lead discovery and optimization opportunities as well as potential "off-target" liabilities.
An assessment of the number of molecular targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many molecular targets this opportunity represents. We start from the position that we understand the properties that are required for a good drug, and therefore must be able to understand what makes a good drug target.
BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.
OBJECTIVE: To identify circulating proteins associated with type 2 diabetes (T2D) risk through pathways not fully explained by body mass index (BMI), and to assess therapeutic actionability. RESEARCH DESIGN AND METHODS: We applied GWAS-by-subtraction within a genomic structural equation model to European ancestry summary statistics for T2D (74,124 cases, 824,006 controls) and BMI (n = 681,275), partitioning T2D liability into BMI-related and BMI-subtracted components. We then performed proteome-wide Mendelian randomization (MR) using cis-protein quantitative trait loci from four plasma proteomics cohorts: ARIC, deCODE, Fenland, and the UK Biobank Pharma Proteomics Project. Prioritized proteins passed sensitivity analyses with alternative MR methods and were supported by colocalization evidence. Tissue-resolution regulatory support was assessed using cis-eQTL colocalization across GTEx and pancreatic islet, subcutaneous adipose, and whole-blood resources. Actionability was evaluated using the druggable genome and Open Targets. RESULTS: GWAS-by-subtraction attenuated the genetic correlation between BMI and BMI-subtracted T2D from 0.54 (SE 0.02) to 0.35 (SE 0.02). Proteome-wide MR prioritized 29 proteins for BMI-subtracted T2D. Thirteen showed eQTL colocalization in at least one tissue, implicating liver and intermediary metabolism (GCDH, NOTCH2), pancreatic islet biology (CTRB2, MANBA), adipose and Wnt signaling (RSPO3, GALNT3), and whole blood regulatory signals (PAM, SNUPN). Sixteen proteins were classified within druggable-genome Tiers 1-3, and five had existing Open Targets compounds. CONCLUSIONS: Integrating GWAS-by-subtraction, proteome-wide MR, and colocalization nominated 29 proteins associated with T2D liability not fully explained by BMI. These findings highlight genetically supported targets for follow-up studies of T2D therapies that complement weight-centered approaches.
The cataloguing of the human genome has provided an unprecedented prospectus for target identification and drug discovery. A current analysis indicates that slightly more than 3000 unique protein encoding loci are potentially amenable to pharmacological intervention (the 'druggable genome', which can be queried at http://function.gnf.org/druggable). However, the assessment of genome sequence data has not resulted in the anticipated acceleration of novel therapeutic developments. The basis for this shortfall lies in the significant attrition rates endemic to preclinical/clinical development, as well as the often underestimated complexity of gene function in higher order biological systems. To address the latter issue, a number of strategies have emerged to facilitate genomics-driven target identification and validation, including cellular profiling of gene function, in silico modelling of gene networks, and systematic analyses of protein complexes. The expectation is that the integration of these and other systems-based technologies may enable the conversion of potential genomic targets into functionally validated molecules, and result in practicable gene-based drug discovery pipelines.
The biopharmaceutical industry is currently faced with a tremendous number of potential drug targets identified through the sequencing of the human genome. The challenge ahead is to delineate those targets with the greatest value for therapeutic intervention. Here, we critically evaluate mouse-knockout technology for target discovery and validation. A retrospective evaluation of the knockout phenotypes for the targets of the 100 best-selling drugs indicates that these phenotypes correlate well with known drug efficacy, illuminating a productive path forward for discovering future drug targets. Prospective mining of the druggable genome is being catalysed by large-scale mouse knockout programs combined with phenotypic screens focused on identifying targets that modulate mammalian physiology in a therapeutically relevant manner.
Biallelic loss-of-function variants in adaptor protein complex 4 (AP-4) disrupt trafficking of transmembrane proteins at the trans-Golgi network, including autophagy-related protein 9A (ATG9A), leading to childhood-onset hereditary spastic paraplegia (AP-4-HSP). AP-4-HSP is characterized by features of both a neurodevelopmental and a degenerative neurological disease. To investigate the molecular mechanisms underlying AP-4-HSP and identify potential therapeutic targets, we conducted an arrayed CRISPR/Cas9 loss-of-function screen of 8,478 genes, targeting the "druggable genome," in a human neuronal model of AP-4 deficiency. Through this phenotypic screen and subsequent experiments, key modulators of ATG9A trafficking were identified, and complementary pathway analyses provided insights into the regulatory landscape of ATG9A transport. Knockdown of ANPEP and NPM1 enhanced ATG9A availability outside the trans-Golgi network, suggesting that they regulate ATG9A localization. These findings deepen our understanding of ATG9A trafficking in the context of AP-4 deficiency and offer a framework for the development of targeted interventions for AP-4-HSP.
Primary Sclerosing Cholangitis (PSC) is a progressive cholestatic liver disease with no licensed therapies. Previous Genome Wide Association Studies (GWAS) have identified genes that correlate significantly with PSC, and these were identified by systematic review. Here we use novel Network Proximity Analysis (NPA) methods to identify already licensed candidate drugs that may have an effect on the genetically coded aspects of PSC pathophysiology.Over 2000 agents were identified as significantly linked to genes implicated in PSC by this method. The most significant results include previously researched agents such as metronidazole, as well as biological agents such as basiliximab, abatacept and belatacept. This in silico analysis could potentially serve as a basis for developing novel clinical trials in this rare disease.
Conventional antimicrobial discovery relies on targeting essential enzymes in pathogenic organisms, contributing to a paucity of new antibiotics to address resistant strains. Here, by targeting a non-essential enzyme, Borrelia burgdorferi HtpG, to deliver lethal payloads, we expand what can be considered druggable within any pathogen. We synthesized HS-291, an HtpG inhibitor tethered to the photoactive toxin verteporfin. Reactive oxygen species, generated by light, enables HS-291 to sterilize Borrelia cultures by causing oxidation of HtpG, and a discrete subset of proteins in proximity to the chaperone. This caused irreversible nucleoid collapse and membrane blebbing. Tethering verteporfin to the HtpG inhibitor was essential, since free verteporfin was not retained by Borrelia in contrast to HS-291. For this reason, we liken HS-291 to a berserker, wreaking havoc upon the pathogen's biology once selectively absorbed and activated. This strategy expands the druggable pathogenic genome and offsets antibiotic resistance by targeting non-essential proteins.
BACKGROUND: Hypothermia is a life-threatening condition lacking specific pharmacological treatments. This study aimed to prioritize genetically supported molecular loci associated with hypothermia and to explore their pharmacological tractability using multi-omics data. METHODS: Initially, 2532 druggable genes were curated from the Drug-Gene Interaction Database and established literature. These were cross-referenced with cis-eQTL and cis-pQTL datasets, encompassing 870,655 and 114,281 SNPs for blood, respectively, alongside 2379 shared SNPs across adipose, skeletal muscle, and heart tissues. Matched instrumental variables were integrated with hypothermia GWAS summary statistics for two-sample Mendelian randomization (MR) and Bayesian colocalization. Transcriptomic differential expression analysis (DEA) was subsequently conducted as an exploratory analysis of cold-exposure-associated expression changes. Database-derived compound annotations were systematically re-evaluated according to target specificity, established pharmacological mechanism, and concordance with the direction of the MR estimates. RESULTS: Among 671 gene-level MR tests, 36 genes reached nominal significance, whereas only ABCC8 remained significant after FDR correction. Colocalization was evaluable for 8 of these 36 genes, and 4 loci (COL18A1, SLC1A7, ADIPOQ, and MERTK) met the prespecified PP.H4>0.90 threshold. The remaining 28 loci were not evaluable because sufficient overlapping regional variants were unavailable after harmonization. Transcriptomic analysis identified altered expression of SLC1A3 and SLCO4A1 under cold exposure, although these findings did not directly validate the colocalization-supported loci. Re-evaluation of database-derived compound annotations did not identify any direct, selective, and directionally concordant drug-repurposing candidate for hypothermia. CONCLUSIONS: COL18A1, SLC1A7, ADIPOQ, and MERTK showed colocalization support among the 8 evaluable nominal MR-associated loci. Because colocalization coverage was limited, these genes should be regarded as preliminary candidate loci rather than established therapeutic targets. The pharmacological annotations were indirect, non-selective, unsupported, or directionally inconsistent and should be interpreted solely as hypothesis-generating information.
We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-alpha, which is a known target of fenofibrate.
The ability to predict whether a particular protein can bind with high affinity and specificity to small, drug-like compounds based solely on its 3D structure has been a longstanding goal of structural biologists and computational scientists. The promise is that an accurate prediction of protein druggability can capitalize on the huge investments already made in structural genomics initiatives by identifying highly druggable proteins and using this information in target identification and validation campaigns. Here we discuss the potential utility of tools that characterize protein targets and describe strategies for the optimal integration of protein druggability data with bioinformatic approaches to target selection.
BACKGROUND & AIMS: Hepatocellular carcinoma (HCC), the dominant form of liver cancer, is a leading cause of cancer death worldwide. Sorafenib and lenvatinib have long been the 2 limited options of first-line treatments for patients with unresectable advanced HCC. However, the single-drug treatment strategy only shows modest survival benefit, mostly because of the survival ability of cancer cells to activate alternative pathways for compensation. In this study, we aim to identify druggable targets contributing to lenvatinib resistance and evaluate the efficacy of combining respective inhibitors and lenvatinib on HCC. METHODS: Genome-scale clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 knockout library screening was applied on the vehicle group and lenvatinib treatment group. Identified druggable candidates were validated individually on HCC cell models. Therapeutic effects of the combined treatment of inhibitors of candidate genes and lenvatinib were evaluated in vitro and in vivo. RESULTS: We successfully identified NFKB1 and MET as critical drivers for the development of lenvatinib resistance in HCC cells. By perturbing the 2 genes with either CRISPR knockout or RNA interference approaches, lenvatinib treatments were significantly sensitized. Moreover, using small molecules QNZ and cabozantinib to target NFKB1 and MET, respectively, this together with lenvatinib could synergistically induce apoptosis and suppress HCC growth in vitro and in vivo. CONCLUSION: Our results demonstrated that genome-wide CRISPR/Cas9 screening is a powerful tool for the design of rational combinational cancer therapy and provided candidate genes possible for combined treatments with lenvatinib to improve therapy efficacy.