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Faria D, Eugénio P, Contreiras Silva M, Balbi L, Bedran G, Kallor AA, Nunes S, Palkowski A, Waleron M, Alfaro JA, Pesquita C. The Immunopeptidomics Ontology (ImPO). Database (Oxford) 2024; 2024:baae014. [PMID: 38857186 PMCID: PMC11164101 DOI: 10.1093/database/baae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 11/30/2023] [Accepted: 02/22/2024] [Indexed: 06/12/2024]
Abstract
The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of short peptides by major histocompatibility complex Class I molecules on the cell surface. Immunopeptidomics, the study of peptides displayed on cells, delves into the wide variety of these peptides. Understanding the mechanisms behind antigen processing and presentation is crucial for effectively evaluating cancer immunotherapies. As an emerging domain, immunopeptidomics currently lacks standardization-there is neither an established terminology nor formally defined semantics-a critical concern considering the complexity, heterogeneity, and growing volume of data involved in immunopeptidomics studies. Additionally, there is a disconnection between how the proteomics community delivers the information about antigen presentation and its uptake by the clinical genomics community. Considering the significant relevance of immunopeptidomics in cancer, this shortcoming must be addressed to bridge the gap between research and clinical practice. In this work, we detail the development of the ImmunoPeptidomics Ontology, ImPO, the first effort at standardizing the terminology and semantics in the domain. ImPO aims to encapsulate and systematize data generated by immunopeptidomics experimental processes and bioinformatics analysis. ImPO establishes cross-references to 24 relevant ontologies, including the National Cancer Institute Thesaurus, Mondo Disease Ontology, Logical Observation Identifier Names and Codes and Experimental Factor Ontology. Although ImPO was developed using expert knowledge to characterize a large and representative data collection, it may be readily used to encode other datasets within the domain. Ultimately, ImPO facilitates data integration and analysis, enabling querying, inference and knowledge generation and importantly bridging the gap between the clinical proteomics and genomics communities. As the field of immunogenomics uses protein-level immunopeptidomics data, we expect ImPO to play a key role in supporting a rich and standardized description of the large-scale data that emerging high-throughput technologies are expected to bring in the near future. Ontology URL: https://zenodo.org/record/10237571 Project GitHub: https://github.com/liseda-lab/ImPO/blob/main/ImPO.owl.
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Affiliation(s)
- Daniel Faria
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol, 9, Lisboa 1000-029, Portugal
| | - Patrícia Eugénio
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Marta Contreiras Silva
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Laura Balbi
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Georges Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Ashwin Adrian Kallor
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Susana Nunes
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
| | - Aleksander Palkowski
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Michal Waleron
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
| | - Javier A Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, ul. Kładki 24, Gdańsk 80-822, Poland
- Department of Biochemistry and Microbiology, University of Victoria, 3800 Finnerty Rd, Victoria, British Columbia, BC V8P 5C2, Canada
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, UK
- The Canadian Association for Responsible AI in Medicine, Victoria, Canada
| | - Catia Pesquita
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa 1749-016, Portugal
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Hu J, Allen BK, Stathias V, Ayad NG, Schürer SC. Kinome-Wide Virtual Screening by Multi-Task Deep Learning. Int J Mol Sci 2024; 25:2538. [PMID: 38473785 PMCID: PMC10932040 DOI: 10.3390/ijms25052538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/04/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome-a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data.
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Affiliation(s)
- Jiaming Hu
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
| | - Bryce K. Allen
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
- Institute for Data Science & Computing, University of Miami, Miami, FL 33136, USA
| | - Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
| | - Nagi G. Ayad
- Center for Therapeutic Innovation Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
- Miami Project to Cure Paralysis, Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (B.K.A.); (V.S.)
- Institute for Data Science & Computing, University of Miami, Miami, FL 33136, USA
- Center for Therapeutic Innovation Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
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Koutrouli M, Nastou K, Piera Líndez P, Bouwmeester R, Rasmussen S, Martens L, Jensen LJ. FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data. Bioinformatics 2024; 40:btae010. [PMID: 38192003 PMCID: PMC10868155 DOI: 10.1093/bioinformatics/btae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 12/07/2023] [Accepted: 01/05/2024] [Indexed: 01/10/2024] Open
Abstract
MOTIVATION Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex. RESULTS To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source. AVAILABILITY AND IMPLEMENTATION Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.
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Affiliation(s)
- Mikaela Koutrouli
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Katerina Nastou
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Pau Piera Líndez
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
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Soleymani S, Gravel N, Huang LC, Yeung W, Bozorgi E, Bendzunas NG, Kochut KJ, Kannan N. Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology. PeerJ 2023; 11:e16087. [PMID: 38077442 PMCID: PMC10704995 DOI: 10.7717/peerj.16087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/22/2023] [Indexed: 12/18/2023] Open
Abstract
The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.
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Affiliation(s)
- Saber Soleymani
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Liang-Chin Huang
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Wayland Yeung
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
| | - Elika Bozorgi
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Nathaniel G. Bendzunas
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States
| | - Krzysztof J. Kochut
- Department of Computer Science, University of Georgia, Athens, GA, United States
| | - Natarajan Kannan
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States
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5
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Halip L, Avram S, Curpan R, Borota A, Bora A, Bologa C, Oprea TI. Exploring DrugCentral: from molecular structures to clinical effects. J Comput Aided Mol Des 2023; 37:681-694. [PMID: 37707619 PMCID: PMC10692006 DOI: 10.1007/s10822-023-00529-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023]
Abstract
DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.
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Affiliation(s)
- Liliana Halip
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Sorin Avram
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ramona Curpan
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ana Borota
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Alina Bora
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
- Expert Systems Inc, San Diego, CA, USA.
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6
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Novikova SE, Tolstova TV, Soloveva NA, Farafonova TE, Tikhonova OV, Kurbatov LK, Rusanov AL, Zgoda VG. Proteomic Approach to Investigating Expression, Localization, and Functions of the SOWAHD Gene Protein Product during Granulocytic Differentiation. BIOCHEMISTRY. BIOKHIMIIA 2023; 88:1668-1682. [PMID: 38105032 DOI: 10.1134/s000629792310019x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/23/2023] [Accepted: 08/26/2023] [Indexed: 12/19/2023]
Abstract
Cataloging human proteins and evaluation of their expression, cellular localization, functions, and potential medical significance are important tasks for the global proteomic community. At present, localization and functions of protein products for almost half of protein-coding genes remain unknown or poorly understood. Investigation of organelle proteomes is a promising approach to uncovering localization and functions of human proteins. Nuclear proteome is of particular interest because many nuclear proteins, e.g., transcription factors, regulate functions that determine cell fate. Meta-analysis of the nuclear proteome, or nucleome, of HL-60 cells treated with all-trans-retinoic acid (ATRA) has shown that the functions and localization of a protein product of the SOWAHD gene are poorly understood. Also, there is no comprehensive information on the SOWAHD gene expression at the protein level. In HL-60 cells, the number of mRNA transcripts of the SOWAHD gene was determined as 6.4 ± 0.7 transcripts per million molecules. Using targeted mass spectrometry, the content of the SOWAHD protein was measured as 0.27 to 1.25 fmol/μg total protein. The half-life for the protein product of the SOWAHD gene determined using stable isotope pulse-chase labeling was ~19 h. Proteomic profiling of the nuclear fraction of HL-60 cells showed that the content of the SOWAHD protein increased during the ATRA-induced granulocytic differentiation, reached the peak value at 9 h after ATRA addition, and then decreased. Nuclear location and involvement of the SOWAHD protein in the ATRA-induced granulocytic differentiation have been demonstrated for the first time.
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Affiliation(s)
| | | | | | | | | | | | | | - Victor G Zgoda
- Institute of Biomedical Chemistry, Moscow, 119121, Russia.
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7
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Ocker M, Neureiter D. Gene expression inhibitors for the treatment of liver fibrosis: drugs under preclinical and early clinical investigation. Expert Opin Investig Drugs 2023; 32:1133-1141. [PMID: 37997755 DOI: 10.1080/13543784.2023.2288075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/22/2023] [Indexed: 11/25/2023]
Abstract
INTRODUCTION Liver fibrosis represents an unmet medical condition with growing incidence and only limited therapeutic options. Interfering with dysregulated gene expression was considered a specific treatment approach, and we are here reviewing the current options to modulate transcription and translation with small molecule inhibitors of involved enzymes, transcription factors or by using non-coding RNA molecules (RNA interference) or DNA antisense oligonucleotides. Despite promising results in preclinical models, only limited data are available from studies in humans. AREAS COVERED This expert opinion provides a general overview of how to interfere with gene expression (transcription and translation) and highlighting recent achievements in liver fibrosis. EXPERT OPINION Many compounds that were explored to modulate gene expression in liver fibrosis (models) were developed as anti-cancer agents. Their use in humans with impaired liver function is often impaired by the lack of specificity to inhibit only fibrosis-related genes in the liver and by associated general toxicity and narrow therapeutic windows. RNAi approaches show a higher degree of specificity and potentially less systemic toxicity. Clinical development in liver fibrosis requires close interaction between pharmaceutical companies and regulatory authorities to address topics like relevant (surrogate) endpoints to achieve meaningful readouts faster.
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Affiliation(s)
- Matthias Ocker
- Medical Department, Division of Hematology, Oncology, and Cancer Immunology, Campus Charité Mitte, Charité University Medicine Berlin, Berlin, Germany
- EO Translational Insights Consulting GmbH, Berlin, Germany
- Tacalyx GmbH, Berlin, Germany
| | - Daniel Neureiter
- Institute of Pathology, Paracelsus Medical University/University Hospital Salzburg (SALK), Salzburg, Austria
- Cancer Cluster Salzburg, Salzburg, Austria
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Kreutzfeldt S, Horak P, Hübschmann D, Knurr A, Fröhling S. National Center for Tumor Diseases Precision Oncology Thesaurus for Drugs: A Curated Database for Drugs, Drug Classes, and Drug Targets in Precision Cancer Medicine. JCO Clin Cancer Inform 2023; 7:e2200147. [PMID: 36888935 DOI: 10.1200/cci.22.00147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023] Open
Affiliation(s)
- Simon Kreutzfeldt
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Peter Horak
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Daniel Hübschmann
- German Cancer Consortium (DKTK), Heidelberg, Germany.,Computational Oncology Group, Molecular Precision Oncology Program, NCT Heidelberg and DKFZ, Heidelberg, Germany.,Heidelberg Institute for Stem Cell Technology and Experimental Medicine, Heidelberg, Germany
| | - Alexander Knurr
- German Cancer Consortium (DKTK), Heidelberg, Germany.,Secondary Use of Data in Oncology Group, Clinical Trial Office, DKFZ, Heidelberg, Germany
| | - Stefan Fröhling
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany
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9
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Piñero J, Rodriguez Fraga PS, Valls-Margarit J, Ronzano F, Accuosto P, Jane RL, Sanz F, Furlong LI. Genomic and proteomic biomarker landscape in Clinical Trials. Comput Struct Biotechnol J 2023; 21:2110-2118. [PMID: 36968019 PMCID: PMC10036891 DOI: 10.1016/j.csbj.2023.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 03/10/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.
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Binder JL, Berendzen J, Stevens AO, He Y, Wang J, Dokholyan NV, Oprea TI. AlphaFold illuminates half of the dark human proteins. Curr Opin Struct Biol 2022; 74:102372. [PMID: 35439658 PMCID: PMC10669925 DOI: 10.1016/j.sbi.2022.102372] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/02/2022] [Accepted: 03/13/2022] [Indexed: 01/05/2023]
Abstract
We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.
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Affiliation(s)
- Jessica L Binder
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA. https://twitter.com/@jessicamaine
| | - Joel Berendzen
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA
| | - Amy O Stevens
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Yi He
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Jian Wang
- Department of Pharmacology, Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA
| | - Nikolay V Dokholyan
- Department of Pharmacology, Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA; Department of Chemistry and Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, United States
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; UNM Comprehensive Cancer Center, Albuquerque, NM, USA; Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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11
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Gurbuz O, Alanis-Lobato G, Picart-Armada S, Sun M, Haslinger C, Lawless N, Fernandez-Albert F. Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method. Front Genet 2022; 13:814093. [PMID: 35360842 PMCID: PMC8963915 DOI: 10.3389/fgene.2022.814093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/28/2022] [Indexed: 11/19/2022] Open
Abstract
Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG’s tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.
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Affiliation(s)
- Ozge Gurbuz
- Discovery Research Coordination Germany, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
| | - Gregorio Alanis-Lobato
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Sergio Picart-Armada
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Miao Sun
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Christian Haslinger
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Nathan Lawless
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Francesc Fernandez-Albert
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
- *Correspondence: Ozge Gurbuz, ; Francesc Fernandez-Albert,
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12
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Systematic illumination of druggable genes in cancer genomes. Cell Rep 2022; 38:110400. [PMID: 35196490 PMCID: PMC8919705 DOI: 10.1016/j.celrep.2022.110400] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 09/12/2021] [Accepted: 01/26/2022] [Indexed: 01/15/2023] Open
Abstract
By combining 6 druggable genome resources, we identify 6,083 genes as potential druggable genes (PDGs). We characterize their expression, recurrent genomic alterations, cancer dependencies, and therapeutic potentials by integrating genome, functionome, and druggome profiles across cancers. 81.5% of PDGs are reliably expressed in major adult cancers, 46.9% show selective expression patterns, and 39.1% exhibit at least one recurrent genomic alteration. We annotate a total of 784 PDGs as dependent genes for cancer cell growth. We further quantify 16 cancer-related features and estimate a PDG cancer drug target score (PCDT score). PDGs with higher PCDT scores are significantly enriched for genes encoding kinases and histone modification enzymes. Importantly, we find that a considerable portion of high PCDT score PDGs are understudied genes, providing unexplored opportunities for drug development in oncology. By integrating the druggable genome and the cancer genome, our study thus generates a comprehensive blueprint of potential druggable genes across cancers. Jiang et al. generate a comprehensive blueprint of potential druggable genes (PDGs) across cancers by a systematic integration of the druggable genome and the cancer genome. This resource is publicly available to the cancer research community in The Cancer Druggable Gene Atlas (TCDA) through the Functional Cancer Genome data portal.
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13
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Kropiwnicki E, Binder J, Yang J, Holmes J, Lachmann A, Clarke DJB, Sheils T, Kelleher K, Metzger V, Bologa CG, Oprea TI, Ma’ayan A. Getting Started with the IDG KMC Datasets and Tools. Curr Protoc 2022; 2:e355. [PMID: 35085427 PMCID: PMC10789444 DOI: 10.1002/cpz1.355] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
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.
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Affiliation(s)
- Eryk Kropiwnicki
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Jessica Binder
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J. B. Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Timothy Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Keith Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Vincent Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian G. Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Tudor I. Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
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14
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Manoharan S, Iyyappan OR. A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining. Methods Mol Biol 2022; 2496:41-70. [PMID: 35713858 DOI: 10.1007/978-1-0716-2305-3_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The advancement in technology for various scientific experiments and the amount of raw data produced from that is enormous, thus giving rise to various subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are expression data, analysis of which results in a set of up/downregulated lists of genes that are functionally annotated to ascertain the biological meaning of genes. These genes are represented as vocabularies and/or Gene Ontology terms when associated with pathway enrichment analysis need relational and conceptual understanding to a disease. The chapter deals with a hybrid approach we designed for identifying novel drug-disease targets. Microarray data for muscular dystrophy is explored here as an example and text mining approaches are utilized with an aim to identify promisingly novel drug targets. Our main objective is to give a basic overview from a biologist's perspective for whom text mining approaches of data mining and information retrieval is fairly a new concept. The chapter aims to bridge the gap between biologist and computational text miners and bring about unison for a more informative research in a fast and time efficient manner.
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Affiliation(s)
- Sharanya Manoharan
- Department of Bioinformatics, Stella Maris College (Autonomous), Chennai, Tamilnadu, India.
| | - Oviya Ramalakshmi Iyyappan
- Department of Sciences, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, Tamilnadu, India
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15
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Yang JJ, Grissa D, Lambert CG, Bologa CG, Mathias SL, Waller A, Wild DJ, Jensen LJ, Oprea TI. TIGA: target illumination GWAS analytics. Bioinformatics 2021; 37:3865-3873. [PMID: 34086846 PMCID: PMC11025677 DOI: 10.1093/bioinformatics/btab427] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 05/12/2021] [Accepted: 06/03/2021] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study. RESULTS Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists. AVAILABILITY AND IMPLEMENTATION Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jeremy J Yang
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Integrative Data Science Laboratory, School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Dhouha Grissa
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Christophe G Lambert
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Cristian G Bologa
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Stephen L Mathias
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Anna Waller
- Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - David J Wild
- Integrative Data Science Laboratory, School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Tudor I Oprea
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
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16
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Sheils T, Mathias SL, Siramshetty VB, Bocci G, Bologa CG, Yang JJ, Waller A, Southall N, Nguyen DT, Oprea TI. How to Illuminate the Druggable Genome Using Pharos. ACTA ACUST UNITED AC 2021; 69:e92. [PMID: 31898878 DOI: 10.1002/cpbi.92] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
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.
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Affiliation(s)
- Timothy Sheils
- National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Stephen L Mathias
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | | | - Giovanni Bocci
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Cristian G Bologa
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Jeremy J Yang
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Anna Waller
- Department of Pathology, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Noel Southall
- National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico.,UNM Comprehensive Cancer Center, Albuquerque, New Mexico.,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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17
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Artificial intelligence in drug design: algorithms, applications, challenges and ethics. FUTURE DRUG DISCOVERY 2021. [DOI: 10.4155/fdd-2020-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
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18
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Chakraborty C, Bhattacharya M, Mallick B, Sharma AR, Lee SS, Agoramoorthy G. SARS-CoV-2 protein drug targets landscape: a potential pharmacological insight view for the new drug development. Expert Rev Clin Pharmacol 2021; 14:225-238. [PMID: 33423554 DOI: 10.1080/17512433.2021.1874348] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Protein drug targets play a significant choice in different stages of the drug discovery process. There is an urgent need to understand the drug discovery approaches and protein drug targets (PDT) of SARS-CoV-2, with structural insights for the development of SARS-CoV-2 drugs through targeted therapeutic approach.Areas covered: We have described the protein as a drug target class and also discussed various drug discovery approaches for SARS-CoV-2 involving the protein drug targets such as drug repurposing study, designing of viral entry inhibitors, viral replication inhibitors, and different enzymes of the virus. We have performed comprehensive literature search from the popular databases such as PubMed Google scholar, Web of Science, and Scopus. Finally, we have illustrated the structural landscape of different significant viral proteins (3 CLpro or Mpro, PLpro, RdRp, helicase, S protein) and host proteins as drug targets (cathepsin L, furin, TMPRSS2, ACE2).Expert opinion: The structural landscape of PDT with their binding pockets, and significant residues involved in binding has been discussed further to better understand the PDT and the structure-based drug discovery for SARS-CoV-2. This attempt will increase more therapeutic options, and combination therapies with a multi-target strategy.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal India.,Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Gangwon-do, Republic of Korea
| | | | - Bidyut Mallick
- Department of Applied Science, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Gangwon-do, Republic of Korea
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Gangwon-do, Republic of Korea
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19
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Kanza S, Graham Frey J. Semantic Technologies in Drug Discovery. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11520-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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20
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Issa NT, Stathias V, Schürer S, Dakshanamurthy S. Machine and deep learning approaches for cancer drug repurposing. Semin Cancer Biol 2021; 68:132-142. [PMID: 31904426 PMCID: PMC7723306 DOI: 10.1016/j.semcancer.2019.12.011] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/31/2019] [Accepted: 12/15/2019] [Indexed: 02/07/2023]
Abstract
Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.
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Affiliation(s)
- Naiem T Issa
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami School of Medicine, Miami, FL, USA
| | - Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, University of Miami School of Medicine, Miami, FL, USA
| | - Stephan Schürer
- Department of Molecular and Cellular Pharmacology, University of Miami School of Medicine, Miami, FL, USA
| | - Sivanesan Dakshanamurthy
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.
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21
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Mendu SK, Stremska ME, Schappe MS, Moser EK, Krupa JK, Rogers JS, Stipes EJ, Parker CA, Braciale TJ, Perry JSA, Desai BN. Targeting the ion channel TRPM7 promotes the thymic development of regulatory T cells by promoting IL-2 signaling. Sci Signal 2020; 13:13/661/eabb0619. [PMID: 33293462 DOI: 10.1126/scisignal.abb0619] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The thymic development of regulatory T (Treg) cells, crucial suppressors of the responses of effector T (Teff) cells, is governed by the transcription factor FOXP3. Despite the clinical importance of Treg cells, there is a dearth of druggable molecular targets capable of increasing their numbers in vivo. We found that inhibiting the function of the TRPM7 chanzyme (ion channel and enzyme) potentiated the thymic development of Treg cells in mice and led to a substantially higher frequency of functional Treg cells in the periphery. In addition, TRPM7-deficient mice were resistant to T cell-driven hepatitis. Deletion of Trpm7 and inhibition of TRPM7 channel activity by the FDA-approved drug FTY720 increased the sensitivity of T cells to the cytokine interleukin-2 (IL-2) through a positive feed-forward loop involving increased expression of the IL-2 receptor α-subunit and activation of the transcriptional regulator STAT5. Enhanced IL-2 signaling increased the expression of Foxp3 in thymocytes and promoted thymic Treg (tTreg) cell development. Thus, these data indicate that inhibiting TRPM7 activity increases Treg cell numbers, suggesting that it may be a therapeutic target to promote immune tolerance.
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Affiliation(s)
- Suresh K Mendu
- Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA
| | - Marta E Stremska
- Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA.,Department of Microbiology, Immunology and Cancer Biology, University of Virginia, Charlottesville, VA 22908, USA
| | - Michael S Schappe
- Howard Hughes Medical Institute, Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Julia K Krupa
- Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA
| | - Jason S Rogers
- Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA
| | - Eric J Stipes
- Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA
| | - Clare A Parker
- Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA
| | - Thomas J Braciale
- Carter Immunology Center, University of Virginia, Charlottesville, VA 22908, USA
| | - Justin S A Perry
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.,Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.,Department of Immunology and Microbial Pathogenesis, Weill Cornell Medical College, New York, NY 10065, USA
| | - Bimal N Desai
- Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA. .,Carter Immunology Center, University of Virginia, Charlottesville, VA 22908, USA.,Robert M. Berne Cardiovascular Research Center, Charlottesville, VA 22908, USA
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22
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Kang H, Li J, Wu M, Shen L, Hou L. Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma. JMIR Med Inform 2020; 8:e20291. [PMID: 33084582 PMCID: PMC7641779 DOI: 10.2196/20291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/11/2020] [Accepted: 09/13/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Many drugs do not work the same way for everyone owing to distinctions in their genes. Pharmacogenomics (PGx) aims to understand how genetic variants influence drug efficacy and toxicity. It is often considered one of the most actionable areas of the personalized medicine paradigm. However, little prior work has included in-depth explorations and descriptions of drug usage, dosage adjustment, and so on. OBJECTIVE We present a pharmacogenomics knowledge model to discover the hidden relationships between PGx entities such as drugs, genes, and diseases, especially details in precise medication. METHODS PGx open data such as DrugBank and RxNorm were integrated in this study, as well as drug labels published by the US Food and Drug Administration. We annotated 190 drug labels manually for entities and relationships. Based on the annotation results, we trained 3 different natural language processing models to complete entity recognition. Finally, the pharmacogenomics knowledge model was described in detail. RESULTS In entity recognition tasks, the Bidirectional Encoder Representations from Transformers-conditional random field model achieved better performance with micro-F1 score of 85.12%. The pharmacogenomics knowledge model in our study included 5 semantic types: drug, gene, disease, precise medication (population, daily dose, dose form, frequency, etc), and adverse reaction. Meanwhile, 26 semantic relationships were defined in detail. Taking melanoma caused by a BRAF gene mutation into consideration, the pharmacogenomics knowledge model covered 7 related drugs and 4846 triples were established in this case. All the corpora, relationship definitions, and triples were made publically available. CONCLUSIONS We highlighted the pharmacogenomics knowledge model as a scalable framework for clinicians and clinical pharmacists to adjust drug dosage according to patient-specific genetic variation, and for pharmaceutical researchers to develop new drugs. In the future, a series of other antitumor drugs and automatic relation extractions will be taken into consideration to further enhance our framework with more PGx linked data.
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Affiliation(s)
- Hongyu Kang
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jiao Li
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Meng Wu
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Liu Shen
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Li Hou
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
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23
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Essegian D, Khurana R, Stathias V, Schürer SC. The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer. CELL REPORTS MEDICINE 2020; 1:100128. [PMID: 33205077 PMCID: PMC7659504 DOI: 10.1016/j.xcrm.2020.100128] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 04/25/2020] [Accepted: 09/24/2020] [Indexed: 02/07/2023]
Abstract
The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment-the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively "drugged." Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app (http://cki.ccs.miami.edu) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.
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Affiliation(s)
- Derek Essegian
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA
| | - Rimpi Khurana
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA
| | - Vasileios Stathias
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA.,Sylvester Comprehensive Cancer Center, University of Miami, Miami, USA
| | - Stephan C Schürer
- Department of Pharmacology, Miller School of Medicine, University of Miami, Miami, USA.,Sylvester Comprehensive Cancer Center, University of Miami, Miami, USA.,Institute for Data Science & Computing, University of Miami, Miami, USA
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24
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Recent progress on cheminformatics approaches to epigenetic drug discovery. Drug Discov Today 2020; 25:2268-2276. [PMID: 33010481 DOI: 10.1016/j.drudis.2020.09.021] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 08/31/2020] [Accepted: 09/17/2020] [Indexed: 12/16/2022]
Abstract
The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.
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25
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Tamir TY, Drewry DH, Wells C, Major MB, Axtman AD. PKIS deep dive yields a chemical starting point for dark kinases and a cell active BRSK2 inhibitor. Sci Rep 2020; 10:15826. [PMID: 32985588 PMCID: PMC7522982 DOI: 10.1038/s41598-020-72869-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 09/08/2020] [Indexed: 01/14/2023] Open
Abstract
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.
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Affiliation(s)
- Tigist Y Tamir
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David H Drewry
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Carrow Wells
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - M Ben Major
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Alison D Axtman
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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26
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Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186553] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.
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27
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Yamagata Y, Yamada H. Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management. Sci Rep 2020; 10:14581. [PMID: 32883995 PMCID: PMC7471325 DOI: 10.1038/s41598-020-71370-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 08/07/2020] [Indexed: 11/09/2022] Open
Abstract
Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. Clarifying the mechanisms of toxicity to evaluate and manage drug safety during drug development is extremely important. However, toxicity mechanisms, especially hepatotoxic mechanisms, are very complex. The significant exposure of liver cells to drugs can cause dysfunction, cell injury, and organ failure in the liver. To clarify potential risks in drug safety management, it is necessary to systematize knowledge from a consistent viewpoint. In this study, we adopt an ontological approach. Ontology provides a controlled vocabulary for sharing and reusing of various data with a computer-friendly manner. We focus on toxic processes, especially hepatotoxic processes, and construct the toxic process ontology (TXPO). The TXPO systematizes knowledge concerning hepatotoxic courses with consistency and no ambiguity. In our application study, we developed a toxic process interpretable knowledge system (TOXPILOT) to bridge the gaps between basic science and medicine for drug safety management. Using semantic web technology, TOXPILOT supports the interpretation of toxicity mechanisms and provides visualizations of toxic courses with useful information based on ontology. Our system will contribute to various applications for drug safety evaluation and management.
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Affiliation(s)
- Yuki Yamagata
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka, 567-0085, Japan. .,Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, Japan.
| | - Hiroshi Yamada
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka, 567-0085, Japan.
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28
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Gong J, Chen Y, Pu F, Sun P, He F, Zhang L, Li Y, Ma Z, Wang H. Understanding Membrane Protein Drug Targets in Computational Perspective. Curr Drug Targets 2020; 20:551-564. [PMID: 30516106 DOI: 10.2174/1389450120666181204164721] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 01/16/2023]
Abstract
Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.
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Affiliation(s)
- Jianting Gong
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Yongbing Chen
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
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29
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Murali V, Königs C, Deekshitula S, Nukala S, Santhi MD, Athri P. CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases. Mol Inform 2020; 39:e2000013. [DOI: 10.1002/minf.202000013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/09/2020] [Indexed: 01/13/2023]
Affiliation(s)
- Vidhya Murali
- Dept. of Computer Science & Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India 2518 3700
| | - Cassandra Königs
- Bio informatics and Medical Informatics Bielefeld University Northrhine-Westphalia Germany
| | - Sarvani Deekshitula
- Dept. of Computer Science & Engineering Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham India
| | - Saranya Nukala
- Dept. of Computer Science & Engineering Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham India
| | - Maddala Divya Santhi
- Dept. of Computer Science & Engineering Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham India
| | - Prashanth Athri
- Dept. of Computer Science & Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India 2518 3700
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30
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Stathias V, Turner J, Koleti A, Vidovic D, Cooper D, Fazel-Najafabadi M, Pilarczyk M, Terryn R, Chung C, Umeano A, Clarke DJB, Lachmann A, Evangelista JE, Ma’ayan A, Medvedovic M, Schürer SC. LINCS Data Portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res 2020; 48:D431-D439. [PMID: 31701147 PMCID: PMC7145650 DOI: 10.1093/nar/gkz1023] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/17/2019] [Accepted: 11/04/2019] [Indexed: 12/21/2022] Open
Abstract
The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.
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Affiliation(s)
- Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - John Turner
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Amar Koleti
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Dusica Vidovic
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Daniel Cooper
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
| | - Mehdi Fazel-Najafabadi
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Marcin Pilarczyk
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Raymond Terryn
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
| | - Caty Chung
- BD2K-LINCS Data Coordination and Integration Center, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, USA
| | - Afoma Umeano
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
| | - Daniel J B Clarke
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alexander Lachmann
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - John Erol Evangelista
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Avi Ma’ayan
- BD2K-LINCS Data Coordination and Integration Center, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Mario Medvedovic
- BD2K-LINCS Data Coordination and Integration Center, USA
- Laboratory for Statistical Genomics and Systems Biology, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati College of Medicine, USA
| | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, USA
- Center for Computational Science, University of Miami, USA
- BD2K-LINCS Data Coordination and Integration Center, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, USA
- To whom correspondence should be addressed. Tel: +1 305 243 6552;
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31
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Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019. [DOI: 78495111110.1038/s41573-019-0050-3' target='_blank'>'"<>78495111110.1038/s41573-019-0050-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [78495111110.1038/s41573-019-0050-3','', '10.1186/s13326-017-0161-x')">Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
78495111110.1038/s41573-019-0050-3" />
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32
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Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019; 19:353-364. [DOI: 10.1038/s41573-019-0050-3] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2019] [Indexed: 12/17/2022]
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33
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Oprea TI. Exploring the dark genome: implications for precision medicine. Mamm Genome 2019; 30:192-200. [PMID: 31270560 DOI: 10.1007/s00335-019-09809-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 06/15/2019] [Indexed: 01/08/2023]
Abstract
The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called "digital natives") is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The "Illuminating the Druggable Genome Knowledge Management Center" (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the "dark genome"), and their potential contribution to specific pathologies. Using the "Target Importance and Novelty Explorer" (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.
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Affiliation(s)
- Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA. .,UNM Comprehensive Cancer Center, Albuquerque, NM, USA. .,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden. .,Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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34
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Küçük McGinty H, Visser U, Schürer S. How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM). Methods Mol Biol 2019; 1939:49-69. [PMID: 30848456 PMCID: PMC7257161 DOI: 10.1007/978-1-4939-9089-4_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.
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Affiliation(s)
- Hande Küçük McGinty
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
- Collaborative Drug Discovery, Inc., Burlingame, CA, USA
| | - Ubbo Visser
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Stephan Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.
- Center for Computational Science, University of Miami, Coral Gables, FL, USA.
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35
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Piñero J, Gonzalez-Perez A, Guney E, Aguirre-Plans J, Sanz F, Oliva B, Furlong LI. Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response. Front Genet 2018; 9:412. [PMID: 30319692 PMCID: PMC6168038 DOI: 10.3389/fgene.2018.00412] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 09/05/2018] [Indexed: 11/13/2022] Open
Abstract
Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.
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Affiliation(s)
- Janet Piñero
- Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Abel Gonzalez-Perez
- Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Emre Guney
- Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Joaquim Aguirre-Plans
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ferran Sanz
- Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Laura I Furlong
- Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
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36
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Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A, Gomez SM, Guha R, Hersey A, Holmes J, Jadhav A, Jensen LJ, Johnson GL, Karlson A, Leach AR, Ma’ayan A, Malovannaya A, Mani S, Mathias SL, McManus MT, Meehan TF, von Mering C, Muthas D, Nguyen DT, Overington JP, Papadatos G, Qin J, Reich C, Roth BL, Schürer SC, Simeonov A, Sklar LA, Southall N, Tomita S, Tudose I, Ursu O, Vidovic D, Waller A, Westergaard D, Yang JJ, Zahoránszky-Köhalmi G. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 2018; 17:317-332. [PMID: 29472638 PMCID: PMC6339563 DOI: 10.1038/nrd.2018.14] [Citation(s) in RCA: 209] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
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.
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Affiliation(s)
- Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- UNM Comprehensive Cancer Center, Albuquerque, NM, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cristian G. Bologa
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Shawn M. Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Rajarshi Guha
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Anne Hersey
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Jayme Holmes
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Ajit Jadhav
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gary L. Johnson
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Anneli Karlson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Present addresses: SciBite Limited, BioData Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Andrew R. Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Avi Ma’ayan
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Subramani Mani
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Stephen L. Mathias
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | | | - Terrence F. Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Daniel Muthas
- Respiratory, Inflammation and Autoimmunity Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, Mölndal, Sweden
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - John P. Overington
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Medicines Discovery Catapult, Alderley Edge, UK
| | - George Papadatos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- GlaxoSmithKline, Stevenage, UK
| | - Jun Qin
- Baylor College of Medicine, Houston, TX, USA
| | | | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Larry A. Sklar
- UNM Comprehensive Cancer Center, Albuquerque, NM, USA
- Center for Molecular Discovery, University of New Mexico Cancer Center, University of New Mexico, Albuquerque, NM, USA
- Department of Pathology, University of New Mexico, Albuquerque, NM, USA
| | - Noel Southall
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Susumu Tomita
- Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Ilinca Tudose
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Google Germany GmbH, München, Germany
| | - Oleg Ursu
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Dušica Vidovic
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anna Waller
- Center for Molecular Discovery, University of New Mexico Cancer Center, University of New Mexico, Albuquerque, NM, USA
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeremy J. Yang
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Gergely Zahoránszky-Köhalmi
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- NIH-NCATS, Rockville, MD, USA
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37
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Ong E, Xie J, Ni Z, Liu Q, Sarntivijai S, Lin Y, Cooper D, Terryn R, Stathias V, Chung C, Schürer S, He Y. Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses. BMC Bioinformatics 2017; 18:556. [PMID: 29322930 PMCID: PMC5763302 DOI: 10.1186/s12859-017-1981-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration. Results CLO has consistently represented all 1097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as an example, we demonstrated how to ontologically model LINCS cellular signatures such as their non-tumorigenic epithelial cell type, three-dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was generated to support systematic LINCS cell line analysis and queries. In summary, LINCS cell lines are currently associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLO view information can be queried using SPARQL scripts. Conclusions CLO was used to support ontological representation, integration, and analysis of over a thousand LINCS cell line cells and their cellular responses.
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Affiliation(s)
- Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jiangan Xie
- Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Zhaohui Ni
- Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Qingping Liu
- Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Sirarat Sarntivijai
- Samples, Phenotypes and Ontologies Team, European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Hinxton, Cambridge, UK
| | - Yu Lin
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA
| | - Daniel Cooper
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA
| | - Raymond Terryn
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA
| | - Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA.,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA
| | - Caty Chung
- BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA.,Center for Computational Science, University of Miami, Miami, FL, USA
| | - Stephan Schürer
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL, USA. .,BD2K LINCS Data Coordination and Integration Center, University of Miami, Miami, FL, USA. .,Center for Computational Science, University of Miami, Miami, FL, USA.
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. .,Unit of Laboratory Animal Medicine and Department of Micro biology and Immunology, University of Michigan, Ann Arbor, MI, USA.
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