Illuminating The Druggable Genome Research Articles

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Illuminating the druggable genome through patent bioactivity data.

The patent literature is a potentially valuable source of bioactivity data. In this article we describe a process to prioritise 3.7 million life science relevant patents obtained from the SureChEMBL database (https://www.surechembl.org/), according to how likely they were to contain bioactivity data for potent small molecules on less-studied targets, based on the classification developed by the Illuminating the Druggable Genome (IDG) project. The overall goal was to select a smaller number of patents that could be manually curated and incorporated into the ChEMBL database. Using relatively simple annotation and filtering pipelines, we have been able to identify a substantial number of patents containing quantitative bioactivity data for understudied targets that had not previously been reported in the peer-reviewed medicinal chemistry literature. We quantify the added value of such methods in terms of the numbers of targets that are so identified, and provide some specific illustrative examples. Our work underlines the potential value in searching the patent corpus in addition to the more traditional peer-reviewed literature. The small molecules found in these patents, together with their measured activity against the targets, are now accessible via the ChEMBL database.

Illuminating the Chemical Space of Untargeted Proteins

According to the Illuminating the Druggable Genome (IDG) initiative, 90% of the proteins encoded by the human genome still lack an identified active ligand, that is, a small molecule with biologically relevant binding potency or functional activity in an in vitro assay. Under this scenario, there is an urgent need for new approaches to chemically address these yet untargeted proteins. It is widely recognized that the best starting point for generating novel small molecules for proteins is to exploit the expected polypharmacology of known active ligands across phylogenetically related proteins following the paradigm that similar proteins are likely to interact with similar ligands. Here, we introduce a computational strategy to identify privileged structures that, when chemically expanded, are highly probable to contain active small molecules for untargeted proteins. The protocol was first tested on a set of 576 currently targeted proteins having at least one protein family sibling the year before their first active ligand was reported. A privileged structure contained in active ligands that were identified in the following years was correctly anticipated for 214 (37%) of those targeted proteins, a lower-bound recall estimate when considering data completeness issues. When applied to a set of 1184 untargeted potential druggable genes in cancer, the identification of privileged structures from known bioactive ligands of protein family siblings allowed for extracting a priority list of diverse commercially available small molecules for 960 of them. Assuming a minimum success rate of 37%, the chemical library selections should be able to deliver active ligands for at least 355 currently untargeted proteins associated with cancer.

Pharos 2023: an integrated resource for the understudied human proteome.

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.

Getting Started with the IDG KMC Datasets and Tools.

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.

Identifying New Drug Targets by Illuminating the Druggable Genome

Results from the Human Genome Project have revealed that the human genome contains ~20,000 genes. Of these, ~4,500 genes are considered part of the “druggable genome,” defined as the subset of the human genome that expresses proteins that are able to bind drug-like molecules. However, existing clinical drugs only target a few hundred of these proteins, leaving a subset of ~4000 proteins within this druggable genome that still remain largely understudied. Three protein families, the ion channels, G-protein-coupled receptors, and protein kinases, have been identified to contain adequate numbers of understudied members and are well-established druggable families with high potential to impact human health once disease associations are made. Further investigation is warranted to discover crucial knowledge about the function of understudied members of these protein families and to elucidate their roles in health and disease. To improve our scientific understanding of understudied members of these three protein families, the National Institutes of Health (NIH) Common Fund launched the Illuminating the Druggable Genome (IDG) Program in 2014. The overall goal of the IDG Program is to catalyze research in areas of biology that are currently understudied but that have high potential to impact human health by (1) identifying biochemical, cellular, or animal model phenotypes for understudied proteins from druggable gene families, (2) enabling further investigation of those proteins by providing reagents and tools, and (3) generating, maintaining, and facilitating the use of a minable knowledge base. A key resource from this program, Pharos (https://pharos.nih.gov/idg/index), aggregates protein information from several sources, allowing researchers easy access to in depth protein data, enabling them to enhance their research and help identify new targets of interest. In addition, the IDG program works to develop molecular probes (small molecules), molecular tools and assays, transgenic mice, recombinant cell lines, data, and digital resources for 100+ orphan GPCRs, ion channels and kinases. These resources are now available for use by the scientific community, with the IDG Program continuing to build on the knowledge and tools developed and generate, aggregate, analyze, and disseminate knowledge and tools around understudied proteins. These new knowledge and tool sets are intended to equip the scientific community, including small businesses and the pharmaceutical industry, with the ability to explore previously understudied biology with the potential to rapidly impact human health. It is hoped that the resources developed, and the discoveries made by the IDG Program, will enable the advancement of new treatments and diagnostics in order to get more treatments to more patients more quickly.

TCRD and Pharos 2021: mining the human proteome for disease biology.

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein–protein interactions, protein–disease and protein–phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases.

Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH’s Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or ‘dark’ kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets.

PKIS deep dive yields a chemical starting point for dark kinases and a cell active BRSK2 inhibitor

The Published Kinase Inhibitor Set (PKIS) is a publicly-available chemogenomic library distributed to more than 300 laboratories by GlaxoSmithKline (GSK) between 2011 and 2015 and by SGC-UNC from 2015 to 2017. Screening this library of well-annotated, published kinase inhibitors has yielded a plethora of data in diverse therapeutic and scientific areas, funded applications, publications, and provided impactful pre-clinical results. GW296115 is a compound that was included in PKIS based on its promising selectivity following profiling against 260 human kinases. Herein we present more comprehensive profiling data for 403 wild type human kinases and follow-up enzymatic screening results for GW296115. This more thorough investigation of GW296115 has confirmed it as a potent inhibitor of kinases including BRSK1 and BRSK2 that were identified in the original panel of 260 kinases as well as surfaced other kinases that it potently inhibits. Based on these new kinome-wide screening results, we report that GW296115 is an inhibitor of several members of the Illuminating the Druggable Genome (IDG) list of understudied dark kinases. Specifically, our results establish GW296115 as a potent lead chemical tool that inhibits six IDG kinases with IC50 values less than 100 nM. Focused studies establish that GW296115 is cell active, and directly engages BRSK2. Further evaluation showed that GW296115 downregulates BRSK2-driven phosphorylation and downstream signaling. Therefore, we present GW296115 as a cell-active chemical tool that can be used to interrogate the poorly characterized function(s) of BRSK2.

How to Illuminate the Druggable Genome Using Pharos.

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

Unexplored therapeutic opportunities in the human genome.

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

Drug target ontology to classify and integrate drug discovery data

BackgroundOne 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.ResultsAs 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.ConclusionsDTO 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.

Pharos: Collating protein information to shed light on the druggable genome.

The ‘druggable genome’ encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage ‘serendipitous browsing’, whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

Abstract B1-25: Data integration for illuminating the druggable genome

Abstract Introduction: The druggable genome is defined as a set of roughly 3000 genes encoding proteins for which drugs can be readily designed to modify their activity. Nearly half of the genes of the druggable genome are G protein-coupled receptors (GPCRs), ion channels, protein kinases, or nuclear receptors. To date, the majority of the genes/proteins from these four families have been scarcely investigated and therefore little is known about their molecular interactions, biological functions, and roles in diseases. Illuminating the Druggable Genome (IDG) is an NIH Common Fund project that is supporting experimental and computational efforts to investigate under-studied druggable genome targets. Our objective is to perform computational analyses on existing biological data to prioritize understudied targets based on their likely disease relevance. Methods: We collected and organized information from a diverse set of 23 resources about GPCRs, ion channels, protein kinases, and nuclear receptors. These resources cover four categories of information: curated physical interactions (e.g. kinase-substrate phosphorylations), literature-derived annotations (e.g. Gene Ontology terms), physical properties (e.g. structural domains), and data from large scale profiling studies (e.g. gene expression profiles of cancer cell lines and other disease models). We distilled the information from each resource into attribute tables where the rows are targets and the columns their attributes. This enabled us to build target and attribute networks for unsupervised learning, and cross validate and combine evidence from diverse resources for supervised learning. We integrated kinase similarity matrices using regularized logistic regression to predict phosphorylation reactions between kinases. We also used gene expression data within The Cancer Genome Atlas (TCGA) to perform enrichment analysis against several gene signatures for druggable genome targets, including: signatures of differentially expressed genes (DEG) following target knockdown, signatures of DEG following target over-expression, and signatures of proteins reported to interact with targets. We used an unsupervised approach to obtain consensus scores for the relative likelihood of a target to be dysregulated in a tumor sample, and then performed hierarchical clustering to match groups of cancer patients with potential novel targets. Results: We computed receiver operating characteristic (ROC) curves to assess the performance of kinase similarity matrices as predictors of kinase-kinase phosphorylation reactions. We investigated the performance of the following similarity matrices individually and after integration using cross-validated, regularized logistic regression: similarity of DEG after kinase knockdown, phylogenetic similarity, similarity of kinase knockout phenotypes, similarity of curated annotations, and similarity of curated interactions. We found that although the predictive performance of each individual similarity matrix varied, with areas under the ROC curve ranging from 0.57 to 0.82, integrating similarity matrices improved the predictive performance up to 0.86. The predicted kinome network can be used to prioritize under-studied kinases by searching for connections between kinases known to play a role in cancer or other diseases. Using an unsupervised approach to identify potentially dysregulated targets in patient tumor samples, we found that within a cancer type (e.g. Acute Myeloid Leukemia), clusters of patients emerged with distinct sets of enriched targets. Under-studied genes within these sets are potentially novel targets that may be investigated further to confirm disease relevance and therapeutic potential. Conclusions: The attribute tables, similarity matrices, and data integration pipelines we developed will enable prioritization of targets for cancer and other diseases. Citation Format: Andrew D. Rouillard, Avi Ma'ayan. Data integration for illuminating the druggable genome. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-25.

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