PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization.

Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure-activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.

Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.

With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1ß-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

Identification of new GLUT2-selective inhibitors through in silico ligand screening and validation in eukaryotic expression systems.

Glucose is an essential energy source for cells. In humans, its passive diffusion through the cell membrane is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT2 transports both glucose and fructose with low affinity and plays a critical role in glucose sensing mechanisms. Alterations in the function or expression of GLUT2 are involved in the Fanconi-Bickel syndrome, diabetes, and cancer. Distinguishing GLUT2 transport in tissues where other GLUTs coexist is challenging due to the low affinity of GLUT2 for glucose and fructose and the scarcity of GLUT-specific modulators. By combining in silico ligand screening of an inward-facing conformation model of GLUT2 and glucose uptake assays in a hexose transporter-deficient yeast strain, in which the GLUT1-5 can be expressed individually, we identified eleven new GLUT2 inhibitors (IC50 ranging from 0.61 to 19.3 µM). Among them, nine were GLUT2-selective, one inhibited GLUT1-4 (pan-Class I GLUT inhibitor), and another inhibited GLUT5 only. All these inhibitors dock to the substrate cavity periphery, close to the large cytosolic loop connecting the two transporter halves, outside the substrate-binding site. The GLUT2 inhibitors described here have various applications; GLUT2-specific inhibitors can serve as tools to examine the pathophysiological role of GLUT2 relative to other GLUTs, the pan-Class I GLUT inhibitor can block glucose entry in cancer cells, and the GLUT2/GLUT5 inhibitor can reduce the intestinal absorption of fructose to combat the harmful effects of a high-fructose diet.

DrugCentral 2021 supports drug discovery and repositioning.

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

Publisher Correction: A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

How to Prepare a Compound Collection Prior to Virtual Screening.

Virtual screening is a well-established technique that has proven to be successful in the identification of novel biologically active molecules, including drug repurposing. Whether for ligand-based or for structure-based virtual screening, a chemical collection needs to be properly processed prior to in silico evaluation. Here we describe our step-by-step procedure for handling very large collections (up to billions) of compounds prior to virtual screening.

DrugCentral 2018: an update.

DrugCentral is a drug information resource (http://drugcentral.org) open to the public since 2016 and previously described in the 2017 Nucleic Acids Research Database issue. Since the 2016 release, 103 new approved drugs were updated. The following new data sources have been included: Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), FDA Orange Book information, L1000 gene perturbation profile distance/similarity matrices and estimated protonation constants. New and existing entries have been updated with the latest information from scientific literature, drug labels and external databases. The web interface has been updated to display and query new data. The full database dump and data files are available for download from the DrugCentral website.

Activation of Rho Family GTPases by Small Molecules.

Ras and Ras-related small GTPases are key regulators of diverse cellular functions that impact cell growth, survival, motility, morphogenesis, and differentiation. They are important targets for studies of disease mechanisms as well as drug discovery. Here, we report the characterization of small molecule agonists of one or more of six Rho, Rab, and Ras family GTPases that were first identified through flow cytometry-based, multiplexed high-throughput screening of 200000 compounds. The activators were categorized into three distinct chemical families that are represented by three lead compounds having the highest activity. Virtual screening predicted additional compounds with potential GTPase activating properties. Secondary dose-response assays performed on compounds identified through these screens confirmed agonist activity of 43 compounds. While the lead and second most active small molecules acted as pan activators of multiple GTPase subfamilies, others showed partial selectivity for Ras and Rab proteins. The compounds did not stimulate nucleotide exchange by guanine nucleotide exchange factors and did not protect against GAP-stimulated GTP hydrolysis. The activating properties were caused by a reversible stabilization of the GTP-bound state and prolonged effector protein interactions. Notably, these compounds were active both in vitro and in cell-based assays, and small molecule-mediated changes in Rho GTPase activities were directly coupled to measurable changes in cytoskeletal rearrangements that dictate cell morphology.

Unexplored therapeutic opportunities in the human genome.

This corrects the article DOI: 10.1038/nrd.2018.14.

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.

Chronic obstructive pulmonary disease phenotypes using cluster analysis of electronic medical records.

Chronic obstructive pulmonary disease is a heterogeneous disease. In this retrospective study, we hypothesize that it is possible to identify clinically relevant phenotypes by applying clustering methods to electronic medical records. We included all the patients >40 years with a diagnosis of chronic obstructive pulmonary disease admitted to the University of New Mexico Hospital between 1 January 2011 and 1 May 2014. We collected admissions, demographics, comorbidities, severity markers and treatments. A total of 3144 patients met the inclusion criteria: 46 percent were >65 years and 52 percent were males. The median Charlson score was 2 (interquartile range: 1-4) and the most frequent comorbidities were depression (36%), congestive heart failure (25%), obesity (19%), cancer (19%) and mild liver disease (18%). Using the sphere exclusion method, nine clusters were obtained: depression-chronic obstructive pulmonary disease, coronary artery disease-chronic obstructive pulmonary disease, cerebrovascular disease-chronic obstructive pulmonary disease, malignancy-chronic obstructive pulmonary disease, advanced malignancy-chronic obstructive pulmonary disease, diabetes mellitus-chronic kidney disease-chronic obstructive pulmonary disease, young age-few comorbidities-high readmission rates-chronic obstructive pulmonary disease, atopy-chronic obstructive pulmonary disease, and advanced disease-chronic obstructive pulmonary disease. These clusters will need to be validated prospectively.

High-Throughput Flow Cytometry Screening of Multidrug Efflux Systems.

The resistance nodulation cell division (RND) family of proteins are inner membrane transporters that associate with periplasmic adaptor proteins and outer membrane porins to affect substrate transport from the cytosol and periplasm in Gram-negative bacteria. Various structurally diverse compounds are substrates of RND transporters. Along with their notable role in antibiotic resistance, these transporters are essential for niche colonization, quorum sensing, and virulence as well as for the removal of fatty acids and bile salts. As such, RNDs are an attractive target for antimicrobial development. However, while enhancing the utility of antibiotics with an RND inhibitor is an appealing concept, only a small core of chemotypes has been identified as efflux pump inhibitors (EPIs). Thus, our key objective is the development and validation of an efflux profiling and discovery strategy for RND model systems. Here we describe a flow cytometric dye accumulation assay that uses fluorescein diacetate (FDA) to interrogate the model Gram-negative pathogens Escherichia coli, Franscisella tularensis, and Burkholderia pseudomallei. Fluorochrome retention is increased in the presence of known efflux inhibitors and in RND deletion strains. The assay can be used in a high-throughput format to evaluate efflux of dye-substrate candidates and to screen chemical libraries for novel EPIs. Triaged compounds that inhibit efflux in pathogenic strains are tested for growth inhibition and antibiotic potentiation using microdilution culture plates in a select agent Biosafety Level-3 (BSL3) environment. This combined approach demonstrates the utility of flow cytometric analysis for efflux activity and provides a useful platform in which to characterize efflux in pathogenic Gram-negative bacteria. Screening small molecule libraries for novel EPI candidates offers the potential for the discovery of new classes of antibacterial compounds.

High-Throughput Screening Approach for Identifying Compounds That Inhibit Nonhomologous End Joining.

DNA double-strand breaks (DSBs) are repaired primarily by homologous recombination (HR) or nonhomologous end joining (NHEJ). Compounds that modulate HR have shown promise as cancer therapeutics. The V(D)J recombination reaction, which assembles antigen receptor genes in lymphocytes, is initiated by the introduction of DNA DSBs at two recombining gene segments by the RAG endonuclease, followed by the NHEJ-mediated repair of these DSBs. Here, using HyperCyt automated flow cytometry, we develop a robust high-throughput screening (HTS) assay for NHEJ that utilizes engineered pre-B-cell lines where the V(D)J recombination reaction can be induced and monitored at a single-cell level. This approach, novel in processing four 384-well plates at a time in parallel, was used to screen the National Cancer Institute NeXT library to identify compounds that inhibit V(D)J recombination and NHEJ. Assessment of cell light scattering characteristics at the primary HTS stage (83,536 compounds) enabled elimination of 60% of apparent hits as false positives. Although all the active compounds that we identified had an inhibitory effect on RAG cleavage, we have established this as an approach that could identify compounds that inhibit RAG cleavage or NHEJ using new chemical libraries.

Drug target ontology to classify and integrate drug discovery data.

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.

Formalizing drug indications on the road to therapeutic intent.

Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.

TIN-X: target importance and novelty explorer.

MOTIVATION: The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. RESULTS: We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. AVAILABILITY AND IMPLEMENTATION: http://www.newdrugtargets.org. CONTACT: cbologa@salud.unm.edu.

Protein biomarker druggability profiling.

Developing automated and interactive methods for building a model by incorporating mechanistic and potentially causal annotations of ranked biomarkers of a disease or clinical condition followed by a mapping into a contextual framework in disease-linked biochemical pathways can be used for potential drug-target evaluation and for proposing new drug targets. We demonstrate the potential of this approach using ranked protein biomarkers obtained in neonatal sepsis by enrolling 127 infants (39 infants with late onset neonatal sepsis and 88 control infants) and by performing a focused proteomic profile of the sera and by applying the interactive druggability profiling algorithm (DPA) developed by us.

DrugCentral: online drug compendium.

DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format.