1
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Waser P, Faghtmann J, Gil-Ordóñez M, Kristensen A, Svenningsen EB, Poulsen TB, Jørgensen KA. Enantioselective Synthesis of α-Quaternary Isochromanes by Oxidative Aminocatalysis and Gold Catalysis. Chemistry 2024; 30:e202401354. [PMID: 38629389 DOI: 10.1002/chem.202401354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Indexed: 05/24/2024]
Abstract
A novel strategy that combines oxidative aminocatalysis and gold catalysis allows the preparation of chiral α-quaternary isochromanes, a motif that is prevalent in natural products and synthetic bioactive compounds. In the first step, α-branched aldehydes and propargylic alcohols are transformed into α-quaternary ethers with excellent optical purities (>90 % ee) via oxidative umpolung with DDQ and an amino acid-derived primary amine catalyst. Subsequent gold(I)-catalyzed intramolecular hydroarylation affords the isochromane products with retention of the quaternary stereocenter. A second approach explores the use of allylic alcohols as reaction partners for the oxidative coupling to furnish α-quaternary ethers with generally lower enantiopurities. Stereoretentive cyclization to isochromane products is achieved via intramolecular Friedel-Crafts type alkylation with allylic acetates as a reactive handle. A number of synthetic elaborations and a biological study on these α-quaternary isochromanes highlight the potential applicability of the presented method.
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Affiliation(s)
- Philipp Waser
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark
| | - Jonas Faghtmann
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark
| | - Marta Gil-Ordóñez
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark
| | - Anne Kristensen
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark
| | - Esben B Svenningsen
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark
| | - Thomas B Poulsen
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark
| | - Karl Anker Jørgensen
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark
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2
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Oba GM, Nakato R. Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach. Genes Cells 2024; 29:456-470. [PMID: 38602264 PMCID: PMC11163938 DOI: 10.1111/gtc.13119] [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: 01/05/2024] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024]
Abstract
Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.
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Affiliation(s)
- Gina Miku Oba
- Laboratory of Computational Genomics, Institute for Quantitative BiosciencesUniversity of TokyoTokyoJapan
- Department of Computational Biology and Medical Science, Graduate School of Frontier ScienceUniversity of TokyoTokyoJapan
| | - Ryuichiro Nakato
- Laboratory of Computational Genomics, Institute for Quantitative BiosciencesUniversity of TokyoTokyoJapan
- Department of Computational Biology and Medical Science, Graduate School of Frontier ScienceUniversity of TokyoTokyoJapan
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3
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Mariella E, Grasso G, Miotto M, Buzo K, Reilly NM, Andrei P, Vitiello PP, Crisafulli G, Arena S, Rospo G, Corti G, Lorenzato A, Cancelliere C, Barault L, Gionfriddo G, Linnebacher M, Russo M, Di Nicolantonio F, Bardelli A. Transcriptome-wide gene expression outlier analysis pinpoints therapeutic vulnerabilities in colorectal cancer. Mol Oncol 2024; 18:1460-1485. [PMID: 38468448 PMCID: PMC11161737 DOI: 10.1002/1878-0261.13622] [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: 01/25/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
Multiple strategies are continuously being explored to expand the drug target repertoire in solid tumors. We devised a novel computational workflow for transcriptome-wide gene expression outlier analysis that allows the systematic identification of both overexpression and underexpression events in cancer cells. Here, it was applied to expression values obtained through RNA sequencing in 226 colorectal cancer (CRC) cell lines that were also characterized by whole-exome sequencing and microarray-based DNA methylation profiling. We found cell models displaying an abnormally high or low expression level for 3533 and 965 genes, respectively. Gene expression abnormalities that have been previously associated with clinically relevant features of CRC cell lines were confirmed. Moreover, by integrating multi-omics data, we identified both genetic and epigenetic alternations underlying outlier expression values. Importantly, our atlas of CRC gene expression outliers can guide the discovery of novel drug targets and biomarkers. As a proof of concept, we found that CRC cell lines lacking expression of the MTAP gene are sensitive to treatment with a PRMT5-MTA inhibitor (MRTX1719). Finally, other tumor types may also benefit from this approach.
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Affiliation(s)
- Elisa Mariella
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
- IFOM ETS, The AIRC Institute of Molecular OncologyMilanItaly
| | - Gaia Grasso
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
- IFOM ETS, The AIRC Institute of Molecular OncologyMilanItaly
| | - Martina Miotto
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
- IFOM ETS, The AIRC Institute of Molecular OncologyMilanItaly
| | - Kristi Buzo
- Department of OncologyUniversity of TorinoCandiolo (TO)Italy
- Candiolo Cancer InstituteFPO‐IRCCSCandiolo (TO)Italy
| | | | - Pietro Andrei
- Department of OncologyUniversity of TorinoCandiolo (TO)Italy
| | - Pietro Paolo Vitiello
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
- IFOM ETS, The AIRC Institute of Molecular OncologyMilanItaly
| | | | - Sabrina Arena
- Department of OncologyUniversity of TorinoCandiolo (TO)Italy
- Candiolo Cancer InstituteFPO‐IRCCSCandiolo (TO)Italy
| | - Giuseppe Rospo
- Department of OncologyUniversity of TorinoCandiolo (TO)Italy
- Present address:
Boehringer Ingelheim RCV GmbH & Co KGViennaAustria
| | - Giorgio Corti
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
| | - Annalisa Lorenzato
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
| | | | - Ludovic Barault
- Department of OncologyUniversity of TorinoCandiolo (TO)Italy
| | | | - Michael Linnebacher
- Clinic of General Surgery, Molecular Oncology and ImmunotherapyUniversity of RostockGermany
| | - Mariangela Russo
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
- IFOM ETS, The AIRC Institute of Molecular OncologyMilanItaly
| | - Federica Di Nicolantonio
- Department of OncologyUniversity of TorinoCandiolo (TO)Italy
- Candiolo Cancer InstituteFPO‐IRCCSCandiolo (TO)Italy
| | - Alberto Bardelli
- Department of Oncology, Molecular Biotechnology CenterUniversity of TorinoItaly
- IFOM ETS, The AIRC Institute of Molecular OncologyMilanItaly
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4
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie ME, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D’Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. Database (Oxford) 2024; 2024:baae031. [PMID: 38713862 PMCID: PMC11184451 DOI: 10.1093/database/baae031] [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: 11/08/2023] [Revised: 02/23/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024]
Abstract
Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.
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Affiliation(s)
- Marija Orlic-Milacic
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Lisa Matthews
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Adam Wright
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Veronica Shamovsky
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Quang Trinh
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Marc E Gillespie
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- College of Pharmacy and Health Sciences, St. John’s University, 8000 Utopia Parkway, Queens, NY 11439, USA
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ralf Stephan
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Bruce May
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Robin Haw
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Deidre Beavers
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Patrick Conley
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Lincoln D Stein
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Room 4386, Toronto, ON M5S 1A8, Canada
| | - Peter D’Eustachio
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Guanming Wu
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
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5
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Offensperger F, Tin G, Duran-Frigola M, Hahn E, Dobner S, Ende CWA, Strohbach JW, Rukavina A, Brennsteiner V, Ogilvie K, Marella N, Kladnik K, Ciuffa R, Majmudar JD, Field SD, Bensimon A, Ferrari L, Ferrada E, Ng A, Zhang Z, Degliesposti G, Boeszoermenyi A, Martens S, Stanton R, Müller AC, Hannich JT, Hepworth D, Superti-Furga G, Kubicek S, Schenone M, Winter GE. Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells. Science 2024; 384:eadk5864. [PMID: 38662832 DOI: 10.1126/science.adk5864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 03/22/2024] [Indexed: 05/04/2024]
Abstract
Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.
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Affiliation(s)
- Fabian Offensperger
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Gary Tin
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Miquel Duran-Frigola
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
- Ersilia Open Source Initiative, Cambridge CB1 3DE, UK
| | - Elisa Hahn
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Sarah Dobner
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | | | - Andrea Rukavina
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Vincenth Brennsteiner
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Kevin Ogilvie
- Medicine Design, Pfizer Worldwide Research and Development, Groton, CT 06340, USA
| | - Nara Marella
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Katharina Kladnik
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Rodolfo Ciuffa
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | | | - Ariel Bensimon
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Luca Ferrari
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna Biocenter 5, 1030 Vienna, Austria
- University of Vienna, Max Perutz Labs, Vienna Biocenter 5, 1030 Vienna, Austria
| | - Evandro Ferrada
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Amanda Ng
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Zhechun Zhang
- Molecular Informatics, Machine Learning and Computational Sciences, Early Clinical Development, Pfizer, Cambridge, MA 02139, USA
| | - Gianluca Degliesposti
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Andras Boeszoermenyi
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Sascha Martens
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna Biocenter 5, 1030 Vienna, Austria
- University of Vienna, Max Perutz Labs, Vienna Biocenter 5, 1030 Vienna, Austria
| | - Robert Stanton
- Molecular Informatics, Machine Learning and Computational Sciences, Early Clinical Development, Pfizer, Cambridge, MA 02139, USA
| | - André C Müller
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | - J Thomas Hannich
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | - Giulio Superti-Furga
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
- Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Stefan Kubicek
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
| | | | - Georg E Winter
- CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
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6
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Muscò A, Martini D, Digregorio M, Broccoli V, Andreazzoli M. Shedding a Light on Dark Genes: A Comparative Expression Study of PRR12 Orthologues during Zebrafish Development. Genes (Basel) 2024; 15:492. [PMID: 38674426 PMCID: PMC11050278 DOI: 10.3390/genes15040492] [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: 03/16/2024] [Revised: 04/06/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Haploinsufficiency of the PRR12 gene is implicated in a human neuro-ocular syndrome. Although identified as a nuclear protein highly expressed in the embryonic mouse brain, PRR12 molecular function remains elusive. This study explores the spatio-temporal expression of zebrafish PRR12 co-orthologs, prr12a and prr12b, as a first step to elucidate their function. In silico analysis reveals high evolutionary conservation in the DNA-interacting domains for both orthologs, with significant syntenic conservation observed for the prr12b locus. In situ hybridization and RT-qPCR analyses on zebrafish embryos and larvae reveal distinct expression patterns: prr12a is expressed early in zygotic development, mainly in the central nervous system, while prr12b expression initiates during gastrulation, localizing later to dopaminergic telencephalic and diencephalic cell clusters. Both transcripts are enriched in the ganglion cell and inner neural layers of the 72 hpf retina, with prr12b widely distributed in the ciliary marginal zone. In the adult brain, prr12a and prr12b are found in the cerebellum, amygdala and ventral telencephalon, which represent the main areas affected in autistic patients. Overall, this study suggests PRR12's potential involvement in eye and brain development, laying the groundwork for further investigations into PRR12-related neurobehavioral disorders.
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Affiliation(s)
- Alessia Muscò
- Cell and Developmental Biology Unit, University of Pisa, 56126 Pisa, Italy (D.M.)
| | - Davide Martini
- Cell and Developmental Biology Unit, University of Pisa, 56126 Pisa, Italy (D.M.)
| | - Matteo Digregorio
- Cell and Developmental Biology Unit, University of Pisa, 56126 Pisa, Italy (D.M.)
| | - Vania Broccoli
- Stem Cell and Neurogenesis Unit, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy
- CNR Institute of Neuroscience, 20132 Milan, Italy
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7
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Richardson R, Tejedor Navarro H, Amaral LAN, Stoeger T. Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results. eLife 2024; 12:RP93429. [PMID: 38546716 PMCID: PMC10977968 DOI: 10.7554/elife.93429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024] Open
Abstract
Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.
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Affiliation(s)
- Reese Richardson
- Interdisciplinary Biological Sciences, Northwestern UniversityEvanstonUnited States
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
| | - Heliodoro Tejedor Navarro
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
- Northwestern Institute on Complex Systems, Northwestern UniversityEvanstonUnited States
| | - Luis A Nunes Amaral
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
- Northwestern Institute on Complex Systems, Northwestern UniversityEvanstonUnited States
- Department of Molecular Biosciences, Northwestern UniversityEvanstonUnited States
- Department of Physics and Astronomy, Northwestern UniversityEvanstonUnited States
| | - Thomas Stoeger
- Department of Chemical and Biological Engineering, Northwestern UniversityEvanstonUnited States
- The Potocsnak Longevity Institute, Northwestern UniversityChicagoUnited States
- Simpson Querrey Lung Institute for Translational Science, Northwestern UniversityChicagoUnited States
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8
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Ren F, Aliper A, Chen J, Zhao H, Rao S, Kuppe C, Ozerov IV, Zhang M, Witte K, Kruse C, Aladinskiy V, Ivanenkov Y, Polykovskiy D, Fu Y, Babin E, Qiao J, Liang X, Mou Z, Wang H, Pun FW, Ayuso PT, Veviorskiy A, Song D, Liu S, Zhang B, Naumov V, Ding X, Kukharenko A, Izumchenko E, Zhavoronkov A. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat Biotechnol 2024:10.1038/s41587-024-02143-0. [PMID: 38459338 DOI: 10.1038/s41587-024-02143-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/16/2024] [Indexed: 03/10/2024]
Abstract
Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.
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Affiliation(s)
- Feng Ren
- Insilico Medicine Shanghai Ltd., Shanghai, China
- Insilico Medicine AI Limited, Abu Dhabi, UAE
| | - Alex Aliper
- Insilico Medicine AI Limited, Abu Dhabi, UAE
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Jian Chen
- Department of Clinical Pharmacology, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, China
| | - Heng Zhao
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Sujata Rao
- Insilico Medicine US Inc., New York, NY, USA
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
- Department of Nephrology, University Clinic RWTH Aachen, Aachen, Germany
| | - Ivan V Ozerov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Man Zhang
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Klaus Witte
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Chris Kruse
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | | | - Yan Ivanenkov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | | | - Yanyun Fu
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | | | - Junwen Qiao
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Xing Liang
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Zhenzhen Mou
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Hui Wang
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Frank W Pun
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Pedro Torres Ayuso
- Department of Cancer and Cellular Biology, Lewis Katz School of Medicine, Temple University, PA, USA
| | | | - Dandan Song
- Department of Clinical Pharmacology, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, China
| | - Sang Liu
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Bei Zhang
- Insilico Medicine Shanghai Ltd., Shanghai, China
| | - Vladimir Naumov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Xiaoqiang Ding
- Division of Nephrology, Zhongshan Hospital Shanghai Medical College, Fudan University, Shanghai, China
| | - Andrey Kukharenko
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Evgeny Izumchenko
- Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Alex Zhavoronkov
- Insilico Medicine AI Limited, Abu Dhabi, UAE.
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China.
- Insilico Medicine US Inc., New York, NY, USA.
- Insilico Medicine Canada Inc, Montreal, Quebec, Canada.
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9
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Oprea TI, Bologa C, Holmes J, Mathias S, Metzger VT, Waller A, Yang JJ, Leach AR, Jensen LJ, Kelleher KJ, Sheils TK, Mathé E, Avram S, Edwards JS. Overview of the Knowledge Management Center for Illuminating the Druggable Genome. Drug Discov Today 2024; 29:103882. [PMID: 38218214 PMCID: PMC10939799 DOI: 10.1016/j.drudis.2024.103882] [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: 10/18/2023] [Revised: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024]
Abstract
The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.
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Affiliation(s)
- Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian Bologa
- 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
| | - Stephen Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Anna Waller
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Keith J Kelleher
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Ewy Mathé
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Sorin Avram
- Coriolan Dragulescu Institute of Chemistry, Timisoara, Romania
| | - Jeremy S Edwards
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA; Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM, USA.
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10
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Veth TS, Kannegieter NM, de Graaf EL, Ruijtenbeek R, Joore J, Ressa A, Altelaar M. Innovative strategies for measuring kinase activity to accelerate the next wave of novel kinase inhibitors. Drug Discov Today 2024; 29:103907. [PMID: 38301799 DOI: 10.1016/j.drudis.2024.103907] [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: 06/23/2023] [Revised: 01/18/2024] [Accepted: 01/25/2024] [Indexed: 02/03/2024]
Abstract
The development of protein kinase inhibitors (PKIs) has gained significance owing to their therapeutic potential for diseases like cancer. In addition, there has been a rise in refining kinase activity assays, each possessing unique biological and analytical characteristics crucial for PKI development. However, the PKI development pipeline experiences high attrition rates and approved PKIs exhibit unexploited potential because of variable patient responses. Enhancing PKI development efficiency involves addressing challenges related to understanding the PKI mechanism of action and employing biomarkers for precision medicine. Selecting appropriate kinase activity assays for these challenges can overcome these attrition rate issues. This review delves into the current obstacles in kinase inhibitor development and elucidates kinase activity assays that can provide solutions.
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Affiliation(s)
- Tim S Veth
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | | | - Erik L de Graaf
- Pepscope, Nieuwe Kanaal 7, 6709 PA Wageningen, The Netherlands
| | | | - Jos Joore
- Pepscope, Nieuwe Kanaal 7, 6709 PA Wageningen, The Netherlands
| | - Anna Ressa
- Pepscope, Nieuwe Kanaal 7, 6709 PA Wageningen, The Netherlands
| | - Maarten Altelaar
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands.
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11
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Sharma KR, Colvis CM, Rodgers GP, Sheeley DM. Illuminating the druggable genome: Pathways to progress. Drug Discov Today 2024; 29:103805. [PMID: 37890715 PMCID: PMC10939933 DOI: 10.1016/j.drudis.2023.103805] [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: 09/07/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
There are ∼4500 genes within the 'druggable genome', the subset of the human genome that expresses proteins able to bind drug-like molecules, yet existing drugs only target a few hundred. A substantial subset of druggable proteins are largely uncharacterized or understudied, with many falling within G protein-coupled receptor (GPCR), ion channel, and kinase protein families. To improve scientific understanding of these three understudied protein families, the US National Institutes of Health launched the Illuminating the Druggable Genome Program. Now, as the program draws to a close, this review will lay out resources developed by the program that are intended to equip the scientific community with the tools necessary to explore previously understudied biology with the potential to rapidly impact human health.
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Affiliation(s)
- Karlie R Sharma
- National Center for Advancing Translational Sciences, National Institutes of Health, 6701 Democracy Blvd, Bethesda, MD 20892, USA.
| | - Christine M Colvis
- National Center for Advancing Translational Sciences, National Institutes of Health, 6701 Democracy Blvd, Bethesda, MD 20892, USA
| | - Griffin P Rodgers
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Douglas M Sheeley
- Office of Strategic Coordination, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
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12
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Ogasawara D, Konrad DB, Tan ZY, Carey KL, Luo J, Won SJ, Li H, Carter T, DeMeester KE, Njomen E, Schreiber SL, Xavier RJ, Melillo B, Cravatt BF. Chemical tools to expand the ligandable proteome: diversity-oriented synthesis-based photoreactive stereoprobes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582206. [PMID: 38464067 PMCID: PMC10925180 DOI: 10.1101/2024.02.27.582206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Chemical proteomics enables the global assessment of small molecule-protein interactions in native biological systems and has emerged as a versatile approach for ligand discovery. The range of small molecules explored by chemical proteomics has, however, been limited. Here, we describe a diversity-oriented synthesis (DOS)-inspired library of stereochemically-defined compounds bearing diazirine and alkyne units for UV light-induced covalent modification and click chemistry enrichment of interacting proteins, respectively. We find that these 'photo-stereoprobes' interact in a stereoselective manner with hundreds of proteins from various structural and functional classes in human cells and demonstrate that these interactions can form the basis for high-throughput screening-compatible nanoBRET assays. Integrated phenotypic analysis and chemical proteomics identified photo-stereoprobes that modulate autophagy by engaging the mitochondrial serine protease CLPP. Our findings show the utility of photo-stereoprobes for expanding the ligandable proteome, furnishing target engagement assays, and discovering and characterizing bioactive small molecules by cell-based screening.
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13
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Kim S, Mollaei P, Antony A, Magar R, Barati Farimani A. GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models. J Chem Inf Model 2024; 64:1134-1144. [PMID: 38340054 PMCID: PMC10900288 DOI: 10.1021/acs.jcim.3c01706] [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: 10/22/2023] [Revised: 01/29/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024]
Abstract
With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.
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Affiliation(s)
- Seongwon Kim
- Department
of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Parisa Mollaei
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Akshay Antony
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Rishikesh Magar
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Amir Barati Farimani
- Department
of Mechanical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
- Department
of Biomedical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
- Machine
Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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14
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Huang M, Rueda-Garcia M, Harthorn A, Hackel BJ, Van Deventer JA. Systematic Evaluation of Protein-Small Molecule Hybrids on the Yeast Surface. ACS Chem Biol 2024; 19:325-335. [PMID: 38230650 PMCID: PMC11146673 DOI: 10.1021/acschembio.3c00524] [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] [Indexed: 01/18/2024]
Abstract
Protein-small molecule hybrids are structures that have the potential to combine the inhibitory properties of small molecules and the specificities of binding proteins. However, achieving such synergies is a substantial engineering challenge with fundamental principles yet to be elucidated. Recent work has demonstrated the power of the yeast display-based discovery of hybrids using a combination of fibronectin-binding domains and thiol-mediated conjugations to introduce small-molecule warheads. Here, we systematically study the effects of expanding the chemical diversity of these hybrids on the yeast surface by investigating a combinatorial set of fibronectins, noncanonical amino acid (ncAA) substitutions, and small-molecule pharmacophores. Our results show that previously discovered thiol-fibronectin hybrids are generally tolerant of a range of ncAA substitutions and retain binding functions to carbonic anhydrases following click chemistry-mediated assembly of hybrids with diverse linker structures. Most surprisingly, we identified several cases where replacement of a potent acetazolamide warhead with a substantially weaker benzenesulfonamide warhead still resulted in the assembly of multiple functional hybrids. In addition to these unexpected findings, we expanded the throughput of our system by validating a 96-well plate-based format to produce yeast-displayed hybrid conjugates in parallel. These efficient explorations of hybrid chemical diversity demonstrate that there are abundant opportunities to expand the functions of protein-small molecule hybrids and elucidate principles that dictate their efficient discovery and design.
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Affiliation(s)
- Manjie Huang
- Chemical and Biological Engineering Department, Tufts University, Medford, Massachusetts 02155, United States
| | - Marina Rueda-Garcia
- Chemical and Biological Engineering Department, Tufts University, Medford, Massachusetts 02155, United States
| | - Abbigael Harthorn
- Department of Biomedical Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Benjamin J. Hackel
- Department of Biomedical Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
- Chemical Engineering and Materials Science Department, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, United States
| | - James A. Van Deventer
- Chemical and Biological Engineering Department, Tufts University, Medford, Massachusetts 02155, United States
- Biomedical Engineering Department, Tufts University, Medford, Massachusetts 02155, United States
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15
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Richardson RAK, Tejedor Navarro H, Amaral LAN, Stoeger T. Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.28.530483. [PMID: 36909550 PMCID: PMC10002660 DOI: 10.1101/2023.02.28.530483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of - omics studies. To promote the investigation of understudied genes we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.
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Affiliation(s)
- Reese AK Richardson
- Interdisciplinary Biological Sciences, Northwestern University
- Department of Chemical and Biological Engineering, Northwestern University
| | - Heliodoro Tejedor Navarro
- Department of Chemical and Biological Engineering, Northwestern University
- Northwestern Institute on Complex Systems, Northwestern University
| | - Luis A Nunes Amaral
- Department of Chemical and Biological Engineering, Northwestern University
- Northwestern Institute on Complex Systems, Northwestern University
- Department of Physics and Astronomy, Northwestern University
- Department of Molecular Biosciences, Northwestern University
| | - Thomas Stoeger
- Department of Chemical and Biological Engineering, Northwestern University
- The Potocsnak Longevity Institute, Northwestern University
- Simpson Querrey Lung Institute for Translational Science, Northwestern University
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16
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Hasselgren C, Oprea TI. Artificial Intelligence for Drug Discovery: Are We There Yet? Annu Rev Pharmacol Toxicol 2024; 64:527-550. [PMID: 37738505 DOI: 10.1146/annurev-pharmtox-040323-040828] [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: 09/24/2023]
Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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17
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Zdrazil B, Felix E, Hunter F, Manners EJ, Blackshaw J, Corbett S, de Veij M, Ioannidis H, Lopez DM, Mosquera J, Magarinos M, Bosc N, Arcila R, Kizilören T, Gaulton A, Bento A, Adasme M, Monecke P, Landrum G, Leach A. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res 2024; 52:D1180-D1192. [PMID: 37933841 PMCID: PMC10767899 DOI: 10.1093/nar/gkad1004] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 11/08/2023] Open
Abstract
ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.
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Affiliation(s)
- Barbara Zdrazil
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eloy Felix
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Fiona Hunter
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Emma J Manners
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - James Blackshaw
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Sybilla Corbett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Marleen de Veij
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Harris Ioannidis
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - David Mendez Lopez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Juan F Mosquera
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Maria Paula Magarinos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Nicolas Bosc
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ricardo Arcila
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Tevfik Kizilören
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - A Patrícia Bento
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Melissa F Adasme
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Peter Monecke
- Sanofi, R&D, Preclinical Safety, Industriepark Höchst, 65926 Frankfurt am Main, Germany
| | - Gregory A Landrum
- Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
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18
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Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, Haw R, Jassal B, Matthews L, May B, Petryszak R, Ragueneau E, Rothfels K, Sevilla C, Shamovsky V, Stephan R, Tiwari K, Varusai T, Weiser J, Wright A, Wu G, Stein L, Hermjakob H, D’Eustachio P. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res 2024; 52:D672-D678. [PMID: 37941124 PMCID: PMC10767911 DOI: 10.1093/nar/gkad1025] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/14/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a broad range of normal and disease-related biological processes. Processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Here we review progress towards annotation of the entire human proteome, targeted annotation of disease-causing genetic variants of proteins and of small-molecule drugs in a pathway context, and towards supporting explicit annotation of cell- and tissue-specific pathways. Finally, we briefly discuss issues involved in making Reactome more fully interoperable with other related resources such as the Gene Ontology and maintaining the resulting community resource network.
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Affiliation(s)
- Marija Milacic
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Deidre Beavers
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Patrick Conley
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Marc Gillespie
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Johannes Griss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Department of Dermatology, Medical University of Vienna, 1090 Vienna, Austria
| | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Bijay Jassal
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Lisa Matthews
- NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Bruce May
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | | | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | | | - Ralf Stephan
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Thawfeek Varusai
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Joel Weiser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Adam Wright
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Guanming Wu
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
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19
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Kong X, Liu C, Zhang Z, Cheng M, Mei Z, Li X, Liu P, Diao L, Ma Y, Jiang P, Kong X, Nie S, Guo Y, Wang Z, Zhang X, Wang Y, Tang L, Guo S, Liu Z, Li D. BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Res 2024; 52:D1110-D1120. [PMID: 37904598 PMCID: PMC10767940 DOI: 10.1093/nar/gkad926] [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: 08/15/2023] [Revised: 09/24/2023] [Accepted: 10/09/2023] [Indexed: 11/01/2023] Open
Abstract
Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient-target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.
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Affiliation(s)
- Xiangren Kong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Chao Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Zuzhen Zhang
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
| | - Meiqi Cheng
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Zhijun Mei
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Xiangdong Li
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Peng Liu
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Lihong Diao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yajie Ma
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Peng Jiang
- Beijing Geneworks Technology Co., Ltd, Beijing 100101, China
| | - Xiangya Kong
- Beijing Geneworks Technology Co., Ltd, Beijing 100101, China
| | - Shiyan Nie
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yingzi Guo
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Ze Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xinlei Zhang
- Beijing Geneworks Technology Co., Ltd, Beijing 100101, China
| | - Yan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Liujun Tang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shuzhen Guo
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhongyang Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- College of Life Sciences, Hebei University, Baoding 071002, China
| | - Dong Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- College of Life Sciences, Hebei University, Baoding 071002, China
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20
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Rappsilber J. A dive into the unknome. Trends Genet 2024; 40:15-16. [PMID: 37968205 DOI: 10.1016/j.tig.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023]
Abstract
We may never understand the function of all genes, findings by Freeman, Munro and colleagues suggest, unless we rethink our approaches. They make a thorough attempt at quantifying the unknownness of protein-coding genes and experimentally prove that many neglected genes hold the seed of important discoveries.
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Affiliation(s)
- Juri Rappsilber
- Technische Universität Berlin, Chair of Bioanalytics, 10623 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, EH9 3BF, UK; Si-M/'Der Simulierte Mensch', a Science Framework of Technische Universität Berlin and Charité - Universitätsmedizin Berlin, Berlin, Germany.
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21
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Heebkaew N, Promjantuek W, Chaicharoenaudomrung N, Phonchai R, Kunhorm P, Soraksa N, Noisa P. Encapsulation of HaCaT Secretome for Enhanced Wound Healing Capacity on Human Dermal Fibroblasts. Mol Biotechnol 2024; 66:44-55. [PMID: 37016178 DOI: 10.1007/s12033-023-00732-z] [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: 12/17/2022] [Accepted: 03/23/2023] [Indexed: 04/06/2023]
Abstract
In the epidermal and dermal layers of the skin, diverse cell types are reconstituted during the wound healing process. Delays or failures in wound healing are a major issue in skin therapy because they prevent the normal structure and function of wounded tissue from being restored, resulting in ulceration or other skin abnormalities. Human immortalized keratinocytes (HaCAT) cells are a spontaneously immortalized human keratinocyte cell line capable of secreting many bioactive chemicals (a secretome) that stimulate skin cell proliferation, rejuvenation, and regeneration. In this study, the HaCaT secretome was encapsulated with polyesters such as poly (lactic-co-glycolic acid) (PLGA) and cassava starch in an effort to maximize its potential. According to the estimated mechanism of the HaCaT secretome, all treatments were conducted on immortalized dermal fibroblast cell lines, a model of wound healing. Encapsulation of HaCaT secretome and cassava starch enhanced the effectiveness of cell proliferation, migration, and anti-aging. On the other hand, the levels of reactive oxygen species (ROS) were lowered, activating antioxidants in immortalized dermal fibroblast cells. The HaCaT secretome induced in a dose-dependent manner the expression of antioxidant-associated genes, including SOD, CAT, and GPX. Six cytokines, including CCL2 and MCP-1, influenced immunoregulatory and inflammatory processes in cultured HaCAT cells. HaCaT secretome encapsulated in cassava starch can reduce ROS buildup by boosting antioxidant to stimulate wound healing. Hence, the HaCaT secretome may have a new chance in the cosmetics business to develop components for wound prevention and healing.
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Affiliation(s)
- Nudjanad Heebkaew
- Laboratory of Cell-Based Assays and Innovations, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima, 30000, Thailand
| | - Wilasinee Promjantuek
- Laboratory of Cell-Based Assays and Innovations, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima, 30000, Thailand
| | - Nipha Chaicharoenaudomrung
- Laboratory of Cell-Based Assays and Innovations, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima, 30000, Thailand
| | - Ruchee Phonchai
- Laboratory of Cell-Based Assays and Innovations, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima, 30000, Thailand
| | - Phongsakorn Kunhorm
- Laboratory of Cell-Based Assays and Innovations, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima, 30000, Thailand
| | - Natchadaporn Soraksa
- Laboratory of Cell-Based Assays and Innovations, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima, 30000, Thailand
| | - Parinya Noisa
- Laboratory of Cell-Based Assays and Innovations, School of Biotechnology, Institute of Agricultural Technology, Suranaree University of Technology, 111 University Avenue, Nakhon Ratchasima, 30000, Thailand.
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22
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Kafita D, Nkhoma P, Dzobo K, Sinkala M. Shedding light on the dark genome: Insights into the genetic, CRISPR-based, and pharmacological dependencies of human cancers and disease aggressiveness. PLoS One 2023; 18:e0296029. [PMID: 38117798 PMCID: PMC10732413 DOI: 10.1371/journal.pone.0296029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/05/2023] [Indexed: 12/22/2023] Open
Abstract
Investigating the human genome is vital for identifying risk factors and devising effective therapies to combat genetic disorders and cancer. Despite the extensive knowledge of the "light genome", the poorly understood "dark genome" remains understudied. In this study, we integrated data from 20,412 protein-coding genes in Pharos and 8,395 patient-derived tumours from The Cancer Genome Atlas (TCGA) to examine the genetic and pharmacological dependencies in human cancers and their treatment implications. We discovered that dark genes exhibited high mutation rates in certain cancers, similar to light genes. By combining the drug response profiles of cancer cells with cell fitness post-CRISPR-mediated gene knockout, we identified the crucial vulnerabilities associated with both dark and light genes. Our analysis also revealed that tumours harbouring dark gene mutations displayed worse overall and disease-free survival rates than those without such mutations. Furthermore, dark gene expression levels significantly influenced patient survival outcomes. Our findings demonstrated a similar distribution of genetic and pharmacological dependencies across the light and dark genomes, suggesting that targeting the dark genome holds promise for cancer treatment. This study underscores the need for ongoing research on the dark genome to better comprehend the underlying mechanisms of cancer and develop more effective therapies.
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Affiliation(s)
- Doris Kafita
- Department of Biomedical Sciences, University of Zambia, School of Health Sciences, Lusaka, Zambia
| | - Panji Nkhoma
- Department of Biomedical Sciences, University of Zambia, School of Health Sciences, Lusaka, Zambia
| | - Kevin Dzobo
- Department of Medicine, Division of Dermatology, Hair and Skin Research Laboratory, Wound and Keloid Scarring Research Unit, The South African Medical Research Council, University of Cape Town, Cape Town, South Africa
| | - Musalula Sinkala
- Department of Biomedical Sciences, University of Zambia, School of Health Sciences, Lusaka, Zambia
- Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine and Department of Integrative Biomedical Sciences, University of Cape Town, Computational Biology Division, Cape Town, South Africa
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23
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Stephenson EH, Higgins JMG. Pharmacological approaches to understanding protein kinase signaling networks. Front Pharmacol 2023; 14:1310135. [PMID: 38164473 PMCID: PMC10757940 DOI: 10.3389/fphar.2023.1310135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.
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Affiliation(s)
| | - Jonathan M. G. Higgins
- Faculty of Medical Sciences, Biosciences Institute, Newcastle University, Newcastle uponTyne, United Kingdom
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24
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Luo Y, Liu Y, Peng J. Calibrated geometric deep learning improves kinase-drug binding predictions. NAT MACH INTELL 2023; 5:1390-1401. [PMID: 38962391 PMCID: PMC11221792 DOI: 10.1038/s42256-023-00751-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 09/29/2023] [Indexed: 07/05/2024]
Abstract
Protein kinases regulate various cellular functions and hold significant pharmacological promise in cancer and other diseases. Although kinase inhibitors are one of the largest groups of approved drugs, much of the human kinome remains unexplored but potentially druggable. Computational approaches, such as machine learning, offer efficient solutions for exploring kinase-compound interactions and uncovering novel binding activities. Despite the increasing availability of three-dimensional (3D) protein and compound structures, existing methods predominantly focus on exploiting local features from one-dimensional protein sequences and two-dimensional molecular graphs to predict binding affinities, overlooking the 3D nature of the binding process. Here we present KDBNet, a deep learning algorithm that incorporates 3D protein and molecule structure data to predict binding affinities. KDBNet uses graph neural networks to learn structure representations of protein binding pockets and drug molecules, capturing the geometric and spatial characteristics of binding activity. In addition, we introduce an algorithm to quantify and calibrate the uncertainties of KDBNet's predictions, enhancing its utility in model-guided discovery in chemical or protein space. Experiments demonstrated that KDBNet outperforms existing deep learning models in predicting kinase-drug binding affinities. The uncertainties estimated by KDBNet are informative and well-calibrated with respect to prediction errors. When integrated with a Bayesian optimization framework, KDBNet enables data-efficient active learning and accelerates the exploration and exploitation of diverse high-binding kinase-drug pairs.
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Affiliation(s)
- Yunan Luo
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- These authors contributed equally: Yunan Luo, Yang Liu
| | - Yang Liu
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
- These authors contributed equally: Yunan Luo, Yang Liu
| | - Jian Peng
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
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25
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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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26
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Joisa CU, Chen KA, Berginski ME, Golitz BT, Jenner MR, Herrera Loeza G, Yeh JJ, Gomez SM. Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors. PeerJ 2023; 11:e16342. [PMID: 38025707 PMCID: PMC10657565 DOI: 10.7717/peerj.16342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.
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Affiliation(s)
- Chinmaya U. Joisa
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States of America
| | - Kevin A. Chen
- Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Matthew E. Berginski
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Brian T. Golitz
- Eshelman Institute for Innovation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Madison R. Jenner
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Gabriela Herrera Loeza
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Jen Jen Yeh
- Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Shawn M. Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States of America
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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27
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Mazidi M, Wright N, Yao P, Kartsonaki C, Millwood IY, Fry H, Said S, Pozarickij A, Pei P, Chen Y, Avery D, Du H, Schmidt DV, Yang L, Lv J, Yu C, Chen J, Hill M, Holmes MV, Howson JMM, Peto R, Collins R, Bennett DA, Walters RG, Li L, Clarke R, Chen Z. Plasma Proteomics to Identify Drug Targets for Ischemic Heart Disease. J Am Coll Cardiol 2023; 82:1906-1920. [PMID: 37940228 PMCID: PMC10641761 DOI: 10.1016/j.jacc.2023.09.804] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/11/2023] [Accepted: 09/05/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Integrated analyses of plasma proteomic and genetic markers in prospective studies can clarify the causal relevance of proteins and discover novel targets for ischemic heart disease (IHD) and other diseases. OBJECTIVES The purpose of this study was to examine associations of proteomics and genetics data with IHD in population studies to discover novel preventive treatments. METHODS We conducted a nested case-cohort study in the China Kadoorie Biobank (CKB) involving 1,971 incident IHD cases and 2,001 subcohort participants who were genotyped and free of prior cardiovascular disease. We measured 1,463 proteins in the stored baseline samples using the OLINK EXPLORE panel. Cox regression yielded adjusted HRs for IHD associated with individual proteins after accounting for multiple testing. Moreover, cis-protein quantitative loci (pQTLs) identified for proteins in genome-wide association studies of CKB and of UK Biobank were used as instrumental variables in separate 2-sample Mendelian randomization (MR) studies involving global CARDIOGRAM+C4D consortium (210,842 IHD cases and 1,378,170 controls). RESULTS Overall 361 proteins were significantly associated at false discovery rate <0.05 with risk of IHD (349 positively, 12 inversely) in CKB, including N-terminal prohormone of brain natriuretic peptide and proprotein convertase subtilisin/kexin type 9. Of these 361 proteins, 212 had cis-pQTLs in CKB, and MR analyses of 198 variants in CARDIOGRAM+C4D identified 13 proteins that showed potentially causal associations with IHD. Independent MR analyses of 307 cis-pQTLs identified in Europeans replicated associations for 4 proteins (FURIN, proteinase-activated receptor-1, Asialoglycoprotein receptor-1, and matrix metalloproteinase-3). Further downstream analyses showed that FURIN, which is highly expressed in endothelial cells, is a potential novel target and matrix metalloproteinase-3 a potential repurposing target for IHD. CONCLUSIONS Integrated analyses of proteomic and genetic data in Chinese and European adults provided causal support for FURIN and multiple other proteins as potential novel drug targets for treatment of IHD.
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Affiliation(s)
- Mohsen Mazidi
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Neil Wright
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Pang Yao
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Christiana Kartsonaki
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iona Y Millwood
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Hannah Fry
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Saredo Said
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Alfred Pozarickij
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Dan Valle Schmidt
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Junshi Chen
- China National Center for Food Risk Assessment, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Michael V Holmes
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Richard Peto
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Rory Collins
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Derrick A Bennett
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin G Walters
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
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28
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Xie Z, Chen C, Ma’ayan A. Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis. PeerJ 2023; 11:e16351. [PMID: 37953774 PMCID: PMC10638921 DOI: 10.7717/peerj.16351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/04/2023] [Indexed: 11/14/2023] Open
Abstract
Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.
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Affiliation(s)
- Zhuorui Xie
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clara Chen
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Avi Ma’ayan
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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29
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie M, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D'Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.18.562964. [PMID: 37904913 PMCID: PMC10614924 DOI: 10.1101/2023.10.18.562964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.
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30
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Salcedo MV, Gravel N, Keshavarzi A, Huang LC, Kochut KJ, Kannan N. Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding. PeerJ 2023; 11:e15815. [PMID: 37868056 PMCID: PMC10590106 DOI: 10.7717/peerj.15815] [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: 01/24/2023] [Accepted: 07/10/2023] [Indexed: 10/24/2023] Open
Abstract
The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.
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Affiliation(s)
- Mariah V. Salcedo
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
| | - Abbas Keshavarzi
- School of Computing, University of Georgia, Athens, GA, United States of America
| | - Liang-Chin Huang
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
| | - Krzysztof J. Kochut
- School of Computing, University of Georgia, Athens, GA, United States of America
| | - Natarajan Kannan
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America
- Institute of Bioinformatics, University of Georgia, Athens, GA, United States of America
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31
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Aatkar A, Vuorinen A, Longfield OE, Gilbert K, Peltier-Heap R, Wagner CD, Zappacosta F, Rittinger K, Chung CW, House D, Tomkinson NCO, Bush JT. Efficient Ligand Discovery Using Sulfur(VI) Fluoride Reactive Fragments. ACS Chem Biol 2023; 18:1926-1937. [PMID: 37084287 PMCID: PMC10510102 DOI: 10.1021/acschembio.3c00034] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/03/2023] [Indexed: 04/23/2023]
Abstract
Sulfur(VI) fluorides (SFs) have emerged as valuable electrophiles for the design of "beyond-cysteine" covalent inhibitors and offer potential for expansion of the liganded proteome. Since SFs target a broad range of nucleophilic amino acids, they deliver an approach for the covalent modification of proteins without requirement for a proximal cysteine residue. Further to this, libraries of reactive fragments present an innovative approach for the discovery of ligands and tools for proteins of interest by leveraging a breadth of mass spectrometry analytical approaches. Herein, we report a screening approach that exploits the unique properties of SFs for this purpose. Libraries of SF-containing reactive fragments were synthesized, and a direct-to-biology workflow was taken to efficiently identify hit compounds for CAII and BCL6. The most promising hits were further characterized to establish the site(s) of covalent modification, modification kinetics, and target engagement in cells. Crystallography was used to gain a detailed molecular understanding of how these reactive fragments bind to their target. It is anticipated that this screening protocol can be used for the accelerated discovery of "beyond-cysteine" covalent inhibitors.
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Affiliation(s)
- Arron Aatkar
- GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
- Department
of Pure and Applied Chemistry, University
of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, U.K.
| | - Aini Vuorinen
- GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
- The
Francis Crick Institute, London NW1 1AT, U.K.
| | - Oliver E. Longfield
- GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
- Department
of Pure and Applied Chemistry, University
of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, U.K.
| | - Katharine Gilbert
- GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
- Department
of Pure and Applied Chemistry, University
of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, U.K.
| | - Rachel Peltier-Heap
- GSK, South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | - Craig D. Wagner
- GSK, South Collegeville Road, Collegeville, Pennsylvania 19426, United States
| | | | | | - Chun-wa Chung
- GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
| | - David House
- GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
- The
Francis Crick Institute, London NW1 1AT, U.K.
| | - Nicholas C. O. Tomkinson
- Department
of Pure and Applied Chemistry, University
of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, U.K.
| | - Jacob T. Bush
- GSK, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
- The
Francis Crick Institute, London NW1 1AT, U.K.
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32
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Hommel U, Hurth K, Rondeau JM, Vulpetti A, Ostermeier D, Boettcher A, Brady JP, Hediger M, Lehmann S, Koch E, Blechschmidt A, Yamamoto R, Tundo Dottorello V, Haenni-Holzinger S, Kaiser C, Lehr P, Lingel A, Mureddu L, Schleberger C, Blank J, Ramage P, Freuler F, Eder J, Bornancin F. Discovery of a selective and biologically active low-molecular weight antagonist of human interleukin-1β. Nat Commun 2023; 14:5497. [PMID: 37679328 PMCID: PMC10484922 DOI: 10.1038/s41467-023-41190-0] [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: 12/12/2022] [Accepted: 08/25/2023] [Indexed: 09/09/2023] Open
Abstract
Human interleukin-1β (hIL-1β) is a pro-inflammatory cytokine involved in many diseases. While hIL-1β directed antibodies have shown clinical benefit, an orally available low-molecular weight antagonist is still elusive, limiting the applications of hIL-1β-directed therapies. Here we describe the discovery of a low-molecular weight hIL-1β antagonist that blocks the interaction with the IL-1R1 receptor. Starting from a low affinity fragment-based screening hit 1, structure-based optimization resulted in a compound (S)-2 that binds and antagonizes hIL-1β with single-digit micromolar activity in biophysical, biochemical, and cellular assays. X-ray analysis reveals an allosteric mode of action that involves a hitherto unknown binding site in hIL-1β encompassing two loops involved in hIL-1R1/hIL-1β interactions. We show that residues of this binding site are part of a conformationally excited state of the mature cytokine. The compound antagonizes hIL-1β function in cells, including primary human fibroblasts, demonstrating the relevance of this discovery for future development of hIL-1β directed therapeutics.
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Affiliation(s)
- Ulrich Hommel
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
| | - Konstanze Hurth
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
| | - Jean-Michel Rondeau
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Anna Vulpetti
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Daniela Ostermeier
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Andreas Boettcher
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Jacob Peter Brady
- Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Michael Hediger
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Sylvie Lehmann
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Elke Koch
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Anke Blechschmidt
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Rina Yamamoto
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | | | | | - Christian Kaiser
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Philipp Lehr
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Andreas Lingel
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Luca Mureddu
- Leicester Institute of Structural and Chemical Biology, Department of Molecular and Cell Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Christian Schleberger
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Jutta Blank
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Paul Ramage
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Felix Freuler
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Joerg Eder
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland
| | - Frédéric Bornancin
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
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33
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Anderson B, Rosston P, Ong HW, Hossain MA, Davis-Gilbert ZW, Drewry DH. How many kinases are druggable? A review of our current understanding. Biochem J 2023; 480:1331-1363. [PMID: 37642371 PMCID: PMC10586788 DOI: 10.1042/bcj20220217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/11/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023]
Abstract
There are over 500 human kinases ranging from very well-studied to almost completely ignored. Kinases are tractable and implicated in many diseases, making them ideal targets for medicinal chemistry campaigns, but is it possible to discover a drug for each individual kinase? For every human kinase, we gathered data on their citation count, availability of chemical probes, approved and investigational drugs, PDB structures, and biochemical and cellular assays. Analysis of these factors highlights which kinase groups have a wealth of information available, and which groups still have room for progress. The data suggest a disproportionate focus on the more well characterized kinases while much of the kinome remains comparatively understudied. It is noteworthy that tool compounds for understudied kinases have already been developed, and there is still untapped potential for further development in this chemical space. Finally, this review discusses many of the different strategies employed to generate selectivity between kinases. Given the large volume of information available and the progress made over the past 20 years when it comes to drugging kinases, we believe it is possible to develop a tool compound for every human kinase. We hope this review will prove to be both a useful resource as well as inspire the discovery of a tool for every kinase.
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Affiliation(s)
- Brian Anderson
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
| | - Peter Rosston
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
| | - Han Wee Ong
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
| | - Mohammad Anwar Hossain
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
| | - Zachary W. Davis-Gilbert
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
| | - David H. Drewry
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
- UNC Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A
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34
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Rocha JJ, Jayaram SA, Stevens TJ, Muschalik N, Shah RD, Emran S, Robles C, Freeman M, Munro S. Functional unknomics: Systematic screening of conserved genes of unknown function. PLoS Biol 2023; 21:e3002222. [PMID: 37552676 PMCID: PMC10409296 DOI: 10.1371/journal.pbio.3002222] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/27/2023] [Indexed: 08/10/2023] Open
Abstract
The human genome encodes approximately 20,000 proteins, many still uncharacterised. It has become clear that scientific research tends to focus on well-studied proteins, leading to a concern that poorly understood genes are unjustifiably neglected. To address this, we have developed a publicly available and customisable "Unknome database" that ranks proteins based on how little is known about them. We applied RNA interference (RNAi) in Drosophila to 260 unknown genes that are conserved between flies and humans. Knockdown of some genes resulted in loss of viability, and functional screening of the rest revealed hits for fertility, development, locomotion, protein quality control, and resilience to stress. CRISPR/Cas9 gene disruption validated a component of Notch signalling and 2 genes contributing to male fertility. Our work illustrates the importance of poorly understood genes, provides a resource to accelerate future research, and highlights a need to support database curation to ensure that misannotation does not erode our awareness of our own ignorance.
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Affiliation(s)
- João J. Rocha
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Tim J. Stevens
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | | | - Rajen D. Shah
- Centre for Mathematical Sciences, University of Cambridge, Cambridge, United Kingdom
| | - Sahar Emran
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Cristina Robles
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Matthew Freeman
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
- Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
| | - Sean Munro
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
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35
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Sutherland JJ, Yonchev D, Fekete A, Urban L. A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs. Nat Commun 2023; 14:4323. [PMID: 37468498 DOI: 10.1038/s41467-023-40064-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023] Open
Abstract
In vitro secondary pharmacology assays are an important tool for predicting clinical adverse drug reactions (ADRs) of investigational drugs. We created the Secondary Pharmacology Database (SPD) by testing 1958 drugs using 200 assays to validate target-ADR associations. Compared to public and subscription resources, 95% of all and 36% of active (AC50 < 1 µM) results are unique to SPD, with bias towards higher activity in public resources. Annotating drugs with free maximal plasma concentrations, we find 684 physiologically relevant unpublished off-target activities. Furthermore, 64% of putative ADRs linked to target activity in key literature reviews are not statistically significant in SPD. Systematic analysis of all target-ADR pairs identifies several putative associations supported by publications. Finally, candidate mechanisms for known ADRs are proposed based on SPD off-target activities. Here we present a freely-available resource for benchmarking ADR predictions, explaining phenotypic activity and investigating clinical properties of marketed drugs.
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Affiliation(s)
| | - Dimitar Yonchev
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Laszlo Urban
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
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36
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Beavers D, Brunson T, Sanati N, Matthews L, Haw R, Shorser S, Sevilla C, Viteri G, Conley P, Rothfels K, Hermjakob H, Stein L, D’Eustachio P, Wu G. Illuminate the Functions of Dark Proteins Using the Reactome-IDG Web Portal. Curr Protoc 2023; 3:e845. [PMID: 37467006 PMCID: PMC10399304 DOI: 10.1002/cpz1.845] [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: 07/20/2023]
Abstract
Understudied or dark proteins have the potential to shed light on as-yet undiscovered molecular mechanisms that underlie phenotypes and suggest innovative therapeutic approaches for many diseases. The Reactome-IDG (Illuminating the Druggable Genome) project aims to place dark proteins in the context of manually curated, highly reliable pathways in Reactome, the most comprehensive, open-source biological pathway knowledgebase, facilitating the understanding functions and predicting therapeutic potentials of dark proteins. The Reactome-IDG web portal, deployed at https://idg.reactome.org, provides a simple, interactive web page for users to search pathways that may functionally interact with dark proteins, enabling the prediction of functions of dark proteins in the context of Reactome pathways. Enhanced visualization features implemented at the portal allow users to investigate the functional contexts for dark proteins based on tissue-specific gene or protein expression, drug-target interactions, or protein or gene pairwise relationships in the original Reactome's systems biology graph notation (SBGN) diagrams or the new simplified functional interaction (FI) network view of pathways. The protocols in this chapter describe step-by-step procedures to use the web portal to learn biological functions of dark proteins in the context of Reactome pathways. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Search for interacting pathways of a protein Support Protocol: Interacting pathway results for an annotated protein Alternate Protocol: Use individual pairwise relationships to predict interacting pathways of a protein Basic Protocol 2: Using the IDG pathway browser to study interacting pathways Basic Protocol 3: Overlaying tissue-specific expression data Basic Protocol 4: Overlaying protein/gene pairwise relationships in the pathway context Basic Protocol 5: Visualizing drug/target interactions.
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Affiliation(s)
- Deidre Beavers
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Nasim Sanati
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Solomon Shorser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Patrick Conley
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A1, Canada
| | | | - Guanming Wu
- Oregon Health & Science University, Portland, OR 97239, USA
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37
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Brunson T, Sanati N, Matthews L, Haw R, Beavers D, Shorser S, Sevilla C, Viteri G, Conley P, Rothfels K, Hermjakob H, Stein L, D’Eustachio P, Wu G. Illuminating Dark Proteins using Reactome Pathways. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543335. [PMID: 37333417 PMCID: PMC10274615 DOI: 10.1101/2023.06.05.543335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg.reactome.org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.
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Affiliation(s)
| | - Nasim Sanati
- Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Deidre Beavers
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Solomon Shorser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Patrick Conley
- Oregon Health & Science University, Portland, OR 97239, USA
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S1A1, Canada
| | | | - Guanming Wu
- Oregon Health & Science University, Portland, OR 97239, USA
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38
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Essegian DJ, Chavez V, Khurshid R, Merchan JR, Schürer SC. AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK. PLoS Comput Biol 2023; 19:e1010263. [PMID: 37235579 PMCID: PMC10249896 DOI: 10.1371/journal.pcbi.1010263] [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: 05/31/2022] [Revised: 06/08/2023] [Accepted: 04/13/2023] [Indexed: 05/28/2023] Open
Abstract
PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes.
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Affiliation(s)
- Derek J. Essegian
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Valery Chavez
- Department of Medicine, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Rabia Khurshid
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Jaime R. Merchan
- Department of Medicine, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida, United States of America
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, United States of America
- Department of Medicine, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida, United States of America
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39
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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40
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Diluvio G, Kelley TT, Lahiry M, Alvarez-Trotta A, Kolb EM, Shersher E, Astudillo L, Kovall RA, Schürer SC, Capobianco AJ. A novel chemical attack on Notch-mediated transcription by targeting the NACK ATPase. Mol Ther Oncolytics 2023; 28:307-320. [PMID: 36938545 PMCID: PMC10015116 DOI: 10.1016/j.omto.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
Notch activation complex kinase (NACK) is a component of the Notch transcriptional machinery critical for the Notch-mediated tumorigenesis. However, the mechanism through which NACK regulates Notch-mediated transcription is not well understood. Here, we demonstrate that NACK binds and hydrolyzes ATP and that only ATP-bound NACK can bind to the Notch ternary complex (NTC). Considering this, we sought to identify inhibitors of this ATP-dependent function and, using computational pipelines, discovered the first small-molecule inhibitor of NACK, Z271-0326, that directly blocks the activity of Notch-mediated transcription and shows potent antineoplastic activity in PDX mouse models. In conclusion, we have discovered the first inhibitor that holds promise for the efficacious treatment of Notch-driven cancers by blocking the Notch activity downstream of the NTC.
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Affiliation(s)
- Giulia Diluvio
- Molecular Oncology Program, The DeWitt Daughtry Family Department of Surgery, 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
| | - Tanya T. Kelley
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mohini Lahiry
- Molecular Oncology Program, The DeWitt Daughtry Family Department of Surgery, 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
| | - Annamil Alvarez-Trotta
- Molecular Oncology Program, The DeWitt Daughtry Family Department of Surgery, 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
| | - Ellen M. Kolb
- Department of Molecular Genetics, Biochemistry, and Microbiology, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Elena Shersher
- Molecular Oncology Program, The DeWitt Daughtry Family Department of Surgery, 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
- Cancer Epigenetics Program, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Luisana Astudillo
- Molecular Oncology Program, The DeWitt Daughtry Family Department of Surgery, 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
| | - Rhett A. Kovall
- Department of Molecular Genetics, Biochemistry, and Microbiology, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Stephan C. Schürer
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
- Corresponding author: Stephan C. Schürer, Miller School of Medicine, University of Miami, 1600 North West 10th Avenue, Miami, FL 33136, USA.
| | - Anthony J. Capobianco
- Molecular Oncology Program, The DeWitt Daughtry Family Department of Surgery, 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
- Corresponding author: Anthony J. Capobianco, Miller School of Medicine, University of Miami, 1600 North West 10th Avenue, Miami, FL 33136, USA.
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41
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Kim G, Lee D. Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets. Comput Biol Med 2023; 158:106881. [PMID: 37028141 DOI: 10.1016/j.compbiomed.2023.106881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.
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42
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Tantoso E, Eisenhaber B, Sinha S, Jensen LJ, Eisenhaber F. About the dark corners in the gene function space of Escherichia coli remaining without illumination by scientific literature. Biol Direct 2023; 18:7. [PMID: 36855185 PMCID: PMC9976479 DOI: 10.1186/s13062-023-00362-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 02/21/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Although Escherichia coli (E. coli) is the most studied prokaryote organism in the history of life sciences, many molecular mechanisms and gene functions encoded in its genome remain to be discovered. This work aims at quantifying the illumination of the E. coli gene function space by the scientific literature and how close we are towards the goal of a complete list of E. coli gene functions. RESULTS The scientific literature about E. coli protein-coding genes has been mapped onto the genome via the mentioning of names for genomic regions in scientific articles both for the case of the strain K-12 MG1655 as well as for the 95%-threshold softcore genome of 1324 E. coli strains with known complete genome. The article match was quantified with the ratio of a given gene name's occurrence to the mentioning of any gene names in the paper. The various genome regions have an extremely uneven literature coverage. A group of elite genes with ≥ 100 full publication equivalents (FPEs, FPE = 1 is an idealized publication devoted to just a single gene) attracts the lion share of the papers. For K-12, ~ 65% of the literature covers just 342 elite genes; for the softcore genome, ~ 68% of the FPEs is about only 342 elite gene families (GFs). We also find that most genes/GFs have at least one mentioning in a dedicated scientific article (with the exception of at least 137 protein-coding transcripts for K-12 and 26 GFs from the softcore genome). Whereas the literature growth rates were highest for uncharacterized or understudied genes until 2005-2010 compared with other groups of genes, they became negative thereafter. At the same time, literature for anyhow well-studied genes started to grow explosively with threshold T10 (≥ 10 FPEs). Typically, a body of ~ 20 actual articles generated over ~ 15 years of research effort was necessary to reach T10. Lineage-specific co-occurrence analysis of genes belonging to the accessory genome of E. coli together with genomic co-localization and sequence-analytic exploration hints previously completely uncharacterized genes yahV and yddL being associated with osmotic stress response/motility mechanisms. CONCLUSION If the numbers of scientific articles about uncharacterized and understudied genes remain at least at present levels, full gene function lists for the strain K-12 MG1655 and the E. coli softcore genome are in reach within the next 25-30 years. Once the literature body for a gene crosses 10 FPEs, most of the critical fundamental research risk appears overcome and steady incremental research becomes possible.
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Affiliation(s)
- Erwin Tantoso
- Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore (GIS), 60 Biopolis Street, Singapore, 138672, Republic of Singapore.,Agency for Science, Technology and Research (A*STAR), Bioinformatics Institute (BII), 30 Biopolis Street #07-01, Matrix Building, Singapore, 138671, Republic of Singapore
| | - Birgit Eisenhaber
- Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore (GIS), 60 Biopolis Street, Singapore, 138672, Republic of Singapore.,Agency for Science, Technology and Research (A*STAR), Bioinformatics Institute (BII), 30 Biopolis Street #07-01, Matrix Building, Singapore, 138671, Republic of Singapore
| | - Swati Sinha
- Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore (GIS), 60 Biopolis Street, Singapore, 138672, Republic of Singapore.,Agency for Science, Technology and Research (A*STAR), Bioinformatics Institute (BII), 30 Biopolis Street #07-01, Matrix Building, Singapore, 138671, Republic of Singapore.,European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Frank Eisenhaber
- Agency for Science, Technology and Research (A*STAR), Genome Institute of Singapore (GIS), 60 Biopolis Street, Singapore, 138672, Republic of Singapore. .,Agency for Science, Technology and Research (A*STAR), Bioinformatics Institute (BII), 30 Biopolis Street #07-01, Matrix Building, Singapore, 138671, Republic of Singapore. .,School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Republic of Singapore.
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43
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Jagodnik KM, Shvili Y, Bartal A. HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression. PLoS One 2023; 18:e0280839. [PMID: 36791052 PMCID: PMC9931161 DOI: 10.1371/journal.pone.0280839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 01/10/2023] [Indexed: 02/16/2023] Open
Abstract
Graph analytical approaches permit identifying novel genes involved in complex diseases, but are limited by (i) inferring structural network similarity of connected gene nodes, ignoring potentially relevant unconnected nodes; (ii) using homogeneous graphs, missing gene-disease associations' complexity; (iii) relying on disease/gene-phenotype associations' similarities, involving highly incomplete data; (iv) using binary classification, with gene-disease edges as positive training samples, and non-associated gene and disease nodes as negative samples that may include currently unknown disease genes; or (v) reporting predicted novel associations without systematically evaluating their accuracy. Addressing these limitations, we develop the Heterogeneous Integrated Graph for Predicting Disease Genes (HetIG-PreDiG) model that includes gene-gene, gene-disease, and gene-tissue associations. We predict novel disease genes using low-dimensional representation of nodes accounting for network structure, and extending beyond network structure using the developed Gene-Disease Prioritization Score (GDPS) reflecting the degree of gene-disease association via gene co-expression data. For negative training samples, we select non-associated gene and disease nodes with lower GDPS that are less likely to be affiliated. We evaluate the developed model's success in predicting novel disease genes by analyzing the prediction probabilities of gene-disease associations. HetIG-PreDiG successfully predicts (Micro-F1 = 0.95) gene-disease associations, outperforming baseline models, and is validated using published literature, thus advancing our understanding of complex genetic diseases.
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Affiliation(s)
- Kathleen M. Jagodnik
- The School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States of America
| | - Yael Shvili
- Department of Surgery A, Meir Medical Center, Kfar Sava, Israel
| | - Alon Bartal
- The School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
- * E-mail:
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Small-molecule inhibition of the archetypal UbiB protein COQ8. Nat Chem Biol 2023; 19:230-238. [PMID: 36302899 PMCID: PMC9898131 DOI: 10.1038/s41589-022-01168-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/08/2022] [Indexed: 02/06/2023]
Abstract
Small-molecule tools have enabled mechanistic investigations and therapeutic targeting of the protein kinase-like (PKL) superfamily. However, such tools are still lacking for many PKL members, including the highly conserved and disease-related UbiB family. Here, we sought to develop and characterize an inhibitor for the archetypal UbiB member COQ8, whose function is essential for coenzyme Q (CoQ) biosynthesis. Guided by crystallography, activity assays and cellular CoQ measurements, we repurposed the 4-anilinoquinoline scaffold to selectively inhibit human COQ8A in cells. Our chemical tool promises to lend mechanistic insights into the activities of these widespread and understudied proteins and to offer potential therapeutic strategies for human diseases connected to their dysfunction.
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45
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Franchini L, Orlandi C. Probing the orphan receptors: Tools and directions. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2023; 195:47-76. [PMID: 36707155 DOI: 10.1016/bs.pmbts.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The endogenous ligands activating a large fraction of the G Protein Coupled Receptor (GPCR) family members have yet to be identified. These receptors are commonly labeled as orphans (oGPCRs), and because of the absence of available pharmacological tools they are currently understudied. Nonetheless, genome wide association studies, together with research using animal models identified many physiological functions regulated by oGPCRs. Similarly, mutations in some oGPCRs have been associated with rare genetic disorders or with an increased risk of developing pathologies. The once underestimated pharmacological potential of targeting oGPCRs is increasingly being exploited by the development of novel tools to understand their biology and by drug discovery endeavors aimed at identifying new modulators of their activity. Here, we summarize recent advancements in the field of oGPCRs and future directions.
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Affiliation(s)
- Luca Franchini
- Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, NY, United States
| | - Cesare Orlandi
- Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, NY, United States.
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46
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom,
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,
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47
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Gilbert K, Vuorinen A, Aatkar A, Pogány P, Pettinger J, Grant EK, Kirkpatrick JM, Rittinger K, House D, Burley GA, Bush JT. Profiling Sulfur(VI) Fluorides as Reactive Functionalities for Chemical Biology Tools and Expansion of the Ligandable Proteome. ACS Chem Biol 2023; 18:285-295. [PMID: 36649130 PMCID: PMC9942091 DOI: 10.1021/acschembio.2c00633] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Here, we report a comprehensive profiling of sulfur(VI) fluorides (SVI-Fs) as reactive groups for chemical biology applications. SVI-Fs are reactive functionalities that modify lysine, tyrosine, histidine, and serine sidechains. A panel of SVI-Fs were studied with respect to hydrolytic stability and reactivity with nucleophilic amino acid sidechains. The use of SVI-Fs to covalently modify carbonic anhydrase II (CAII) and a range of kinases was then investigated. Finally, the SVI-F panel was used in live cell chemoproteomic workflows, identifying novel protein targets based on the type of SVI-F used. This work highlights how SVI-F reactivity can be used as a tool to expand the liganded proteome.
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Affiliation(s)
- Katharine
E. Gilbert
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, HertfordshireSG1 2NY, United Kingdom,University
of Strathclyde, 295 Cathedral Street, GlasgowG11XL, United Kingdom
| | - Aini Vuorinen
- Crick-GSK
Biomedical LinkLabs, GlaxoSmithKline, Gunnels Wood Road, StevenageSG1 2NY, United Kingdom
| | - Arron Aatkar
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, HertfordshireSG1 2NY, United Kingdom,University
of Strathclyde, 295 Cathedral Street, GlasgowG11XL, United Kingdom
| | - Peter Pogány
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, HertfordshireSG1 2NY, United Kingdom
| | - Jonathan Pettinger
- Crick-GSK
Biomedical LinkLabs, GlaxoSmithKline, Gunnels Wood Road, StevenageSG1 2NY, United Kingdom
| | - Emma K. Grant
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, HertfordshireSG1 2NY, United Kingdom
| | | | - Katrin Rittinger
- The
Francis Crick Institute, 1 Midland Road, LondonNW1 1AT, United Kingdom
| | - David House
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, HertfordshireSG1 2NY, United Kingdom,Crick-GSK
Biomedical LinkLabs, GlaxoSmithKline, Gunnels Wood Road, StevenageSG1 2NY, United Kingdom
| | - Glenn A. Burley
- University
of Strathclyde, 295 Cathedral Street, GlasgowG11XL, United Kingdom,
| | - Jacob T. Bush
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, HertfordshireSG1 2NY, United Kingdom,Crick-GSK
Biomedical LinkLabs, GlaxoSmithKline, Gunnels Wood Road, StevenageSG1 2NY, United Kingdom,
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48
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Magariños MP, Gaulton A, Félix E, Kiziloren T, Arcila R, Oprea TI, Leach AR. Illuminating the druggable genome through patent bioactivity data. PeerJ 2023; 11:e15153. [PMID: 37151295 PMCID: PMC10162037 DOI: 10.7717/peerj.15153] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/10/2023] [Indexed: 05/09/2023] Open
Abstract
The patent literature is a potentially valuable source of bioactivity data. In this article we describe a process to prioritise 3.7 million life science relevant patents obtained from the SureChEMBL database (https://www.surechembl.org/), according to how likely they were to contain bioactivity data for potent small molecules on less-studied targets, based on the classification developed by the Illuminating the Druggable Genome (IDG) project. The overall goal was to select a smaller number of patents that could be manually curated and incorporated into the ChEMBL database. Using relatively simple annotation and filtering pipelines, we have been able to identify a substantial number of patents containing quantitative bioactivity data for understudied targets that had not previously been reported in the peer-reviewed medicinal chemistry literature. We quantify the added value of such methods in terms of the numbers of targets that are so identified, and provide some specific illustrative examples. Our work underlines the potential value in searching the patent corpus in addition to the more traditional peer-reviewed literature. The small molecules found in these patents, together with their measured activity against the targets, are now accessible via the ChEMBL database.
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Affiliation(s)
| | - Anna Gaulton
- EMBL-EBI, Hinxton, United Kingdom
- Exscientia, Oxford, United Kingdom
| | | | | | | | - Tudor I. Oprea
- Translational informatics Division, Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, United States
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TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine. J Cheminform 2022; 14:89. [PMID: 36587232 PMCID: PMC9805110 DOI: 10.1186/s13321-022-00670-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 12/14/2022] [Indexed: 01/01/2023] Open
Abstract
Traditional Chinese Medicine (TCM) has been widely used in the treatment of various diseases for millennia. In the modernization process of TCM, TCM ingredient databases are playing more and more important roles. However, most of the existing TCM ingredient databases do not provide simplification function for extracting key ingredients in each herb or formula, which hinders the research on the mechanism of actions of the ingredients in TCM databases. The lack of quality control and standardization of the data in most of these existing databases is also a prominent disadvantage. Therefore, we developed a Traditional Chinese Medicine Simplified Integrated Database (TCMSID) with high storage, high quality and standardization. The database includes 499 herbs registered in the Chinese pharmacopeia with 20,015 ingredients, 3270 targets as well as corresponding detailed information. TCMSID is not only a database of herbal ingredients, but also a TCM simplification platform. Key ingredients from TCM herbs are available to be screened out and regarded as representatives to explore the mechanism of TCM herbs by implementing multi-tool target prediction and multilevel network construction. TCMSID provides abundant data sources and analysis platforms for TCM simplification and drug discovery, which is expected to promote modernization and internationalization of TCM and enhance its international status in the future. TCMSID is freely available at https://tcm.scbdd.com .
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50
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Avram S, Wilson TB, Curpan R, Halip L, Borota A, Bora A, Bologa C, Holmes J, Knockel J, Yang J, Oprea T. DrugCentral 2023 extends human clinical data and integrates veterinary drugs. Nucleic Acids Res 2022; 51:D1276-D1287. [PMID: 36484092 PMCID: PMC9825566 DOI: 10.1093/nar/gkac1085] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/20/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs (154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance data for pediatric and geriatric patients. Additional enhancements include chemical substructure searching using SMILES and 'Target Cards' based on UniProt accession codes. Statistics of interests include the following: (i) 60% of the covered drugs are on-market drugs with expired patent and exclusivity coverage, 17% are off-market, and 23% are on-market drugs with active patents and exclusivity coverage; (ii) 59% of the drugs are oral, 33% are parenteral and 18% topical, at the level of the active ingredients; (iii) only 3% of all drugs are for animal use only; however, 61% of the veterinary drugs are also approved for human use; (iv) dogs, cats and horses are by far the most represented target species for veterinary drugs; (v) the physicochemical property profile of animal drugs is very similar to that of human drugs. Use cases include azaperone, the only sedative approved for swine, and ruxolitinib, a Janus kinase inhibitor.
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Affiliation(s)
| | | | - Ramona Curpan
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Liliana Halip
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Ana Borota
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Alina Bora
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Jeffrey Knockel
- Department of Computer Science, University of New Mexico, 1901 Redondo S Dr, Albuquerque, NM 87106, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Tudor I Oprea
- To whom correspondence should be addressed. Tel: +1 505 925 7529; Fax: +1 505 925 7625;
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