201
|
Affiliation(s)
- Jie Wang
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| | - Shiliang Li
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and Technology Shanghai 200237 China
| |
Collapse
|
202
|
Fernández-Torras A, Duran-Frigola M, Aloy P. Encircling the regions of the pharmacogenomic landscape that determine drug response. Genome Med 2019; 11:17. [PMID: 30914058 PMCID: PMC6436215 DOI: 10.1186/s13073-019-0626-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 03/05/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics. METHODS To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome. RESULTS We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses. CONCLUSIONS Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.
Collapse
Affiliation(s)
- Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
| |
Collapse
|
203
|
Vasudevan A, Argiriadi MA, Baranczak A, Friedman MM, Gavrilyuk J, Hobson AD, Hulce JJ, Osman S, Wilson NS. Covalent binders in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2019; 58:1-62. [PMID: 30879472 DOI: 10.1016/bs.pmch.2018.12.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Covalent modulation of protein function can have multiple utilities including therapeutics, and probes to interrogate biology. While this field is still viewed with scepticism due to the potential for (idiosyncratic) toxicities, significant strides have been made in terms of understanding how to tune electrophilicity to selectively target specific residues. Progress has also been made in harnessing the potential of covalent binders to uncover novel biology and to provide an enhanced utility as payloads for Antibody Drug Conjugates. This perspective covers the tenets and applications of covalent binders.
Collapse
Affiliation(s)
| | | | | | | | - Julia Gavrilyuk
- AbbVie Stemcentrx, LLC, South San Francisco, CA, United States
| | | | | | - Sami Osman
- AbbVie Bioresearch Center, Worcester, MA, United States
| | | |
Collapse
|
204
|
Liaud N, Horlbeck MA, Gilbert LA, Gjoni K, Weissman JS, Cate JHD. Cellular response to small molecules that selectively stall protein synthesis by the ribosome. PLoS Genet 2019; 15:e1008057. [PMID: 30875366 PMCID: PMC6436758 DOI: 10.1371/journal.pgen.1008057] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 03/27/2019] [Accepted: 02/28/2019] [Indexed: 11/25/2022] Open
Abstract
Identifying small molecules that inhibit protein synthesis by selectively stalling the ribosome constitutes a new strategy for therapeutic development. Compounds that inhibit the translation of PCSK9, a major regulator of low-density lipoprotein cholesterol, have been identified that reduce LDL cholesterol in preclinical models and that affect the translation of only a few off-target proteins. Although some of these compounds hold potential for future therapeutic development, it is not known how they impact the physiology of cells or ribosome quality control pathways. Here we used a genome-wide CRISPRi screen to identify proteins and pathways that modulate cell growth in the presence of high doses of a selective PCSK9 translational inhibitor, PF-06378503 (PF8503). The two most potent genetic modifiers of cell fitness in the presence of PF8503, the ubiquitin binding protein ASCC2 and helicase ASCC3, bind to the ribosome and protect cells from toxic effects of high concentrations of the compound. Surprisingly, translation quality control proteins Pelota (PELO) and HBS1L sensitize cells to PF8503 treatment. In genetic interaction experiments, ASCC3 acts together with ASCC2, and functions downstream of HBS1L. Taken together, these results identify new connections between ribosome quality control pathways, and provide new insights into the selectivity of compounds that stall human translation that will aid the development of next-generation selective translation stalling compounds to treat disease.
Collapse
Affiliation(s)
- Nadège Liaud
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Max A. Horlbeck
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, United States of America
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, United States of America
- California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, CA, United States of America
| | - Luke A. Gilbert
- Department of Urology, University of California, San Francisco, San Francisco, CA, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States of America
| | - Ketrin Gjoni
- Department of Chemistry, University of California, Berkeley, Berkeley, California, United States of America
| | - Jonathan S. Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, United States of America
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, United States of America
- California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, CA, United States of America
| | - Jamie H. D. Cate
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
- Department of Chemistry, University of California, Berkeley, Berkeley, California, United States of America
- QB3 Institute, University of California, Berkeley, Berkeley, California, United States of America
- Molecular Biophysics and Integrated Bio-imaging, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| |
Collapse
|
205
|
|
206
|
Uhl GR, Martinez MJ. PTPRD: neurobiology, genetics, and initial pharmacology of a pleiotropic contributor to brain phenotypes. Ann N Y Acad Sci 2019; 1451:112-129. [PMID: 30648269 PMCID: PMC6629525 DOI: 10.1111/nyas.14002] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 11/12/2018] [Accepted: 12/19/2018] [Indexed: 12/12/2022]
Abstract
Receptor-type protein tyrosine phosphatase, receptor type D (PTPRD) has likely roles as a neuronal cell adhesion molecule and synaptic specifier. Interest in its neurobiology and genomics has been stimulated by results from human genetics and mouse models for phenotypes related to addiction, restless leg syndrome, neurofibrillary pathology in Alzheimer's disease, cognitive impairment/intellectual disability, mood lability, and obsessive-compulsive disorder. We review PTPRD's discovery, gene family, candidate homomeric and heteromeric binding partners, phosphatase activities, brain distribution, human genetic associations with nervous system phenotypes, and mouse model data relevant to these phenotypes. We discuss the recently reported discovery of the first small molecule inhibitor of PTPRD phosphatase, the identification of its addiction-related effects, and the implications of these findings for the PTPRD-associated brain phenotypes. In assembling PTPRD neurobiology, human genetics, and mouse genetic and pharmacological datasets, we provide a compelling picture of the roles played by PTPRD, its variation, and its potential as a target for novel therapeutics.
Collapse
Affiliation(s)
- George R Uhl
- Neurology and Research Services, New Mexico VA Healthcare System, Albuquerque, New Mexico.,Departments of Neurology, Neuroscience, Molecular Genetics and Microbiology, University of New Mexico, Albuquerque, New Mexico.,Biomedical Research Institute of New Mexico, Albuquerque, New Mexico.,Departments of Neurology, Neuroscience and Mental Health, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Maria J Martinez
- Neurology and Research Services, New Mexico VA Healthcare System, Albuquerque, New Mexico.,Biomedical Research Institute of New Mexico, Albuquerque, New Mexico
| |
Collapse
|
207
|
Bosc N, Atkinson F, Felix E, Gaulton A, Hersey A, Leach AR. Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery. J Cheminform 2019; 11:4. [PMID: 30631996 PMCID: PMC6690068 DOI: 10.1186/s13321-018-0325-4] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 12/24/2018] [Indexed: 12/22/2022] Open
Abstract
Structure–activity relationship modelling is frequently used in the early stage of drug discovery to assess the activity of a compound on one or several targets, and can also be used to assess the interaction of compounds with liability targets. QSAR models have been used for these and related applications over many years, with good success. Conformal prediction is a relatively new QSAR approach that provides information on the certainty of a prediction, and so helps in decision-making. However, it is not always clear how best to make use of this additional information. In this article, we describe a case study that directly compares conformal prediction with traditional QSAR methods for large-scale predictions of target-ligand binding. The ChEMBL database was used to extract a data set comprising data from 550 human protein targets with different bioactivity profiles. For each target, a QSAR model and a conformal predictor were trained and their results compared. The models were then evaluated on new data published since the original models were built to simulate a “real world” application. The comparative study highlights the similarities between the two techniques but also some differences that it is important to bear in mind when the methods are used in practical drug discovery applications.
Collapse
Affiliation(s)
- Nicolas Bosc
- Chemogenomics Team, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Francis Atkinson
- Chemogenomics Team, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Eloy Felix
- Chemogenomics Team, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Anna Gaulton
- Chemogenomics Team, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Anne Hersey
- Chemogenomics Team, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Andrew R Leach
- Chemogenomics Team, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| |
Collapse
|
208
|
Ursu O, Holmes J, Bologa CG, Yang JJ, Mathias SL, Stathias V, Nguyen DT, Schürer S, Oprea T. DrugCentral 2018: an update. Nucleic Acids Res 2019; 47:D963-D970. [PMID: 30371892 PMCID: PMC6323925 DOI: 10.1093/nar/gky963] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 10/02/2018] [Accepted: 10/26/2018] [Indexed: 01/21/2023] Open
Abstract
DrugCentral is a drug information resource (http://drugcentral.org) open to the public since 2016 and previously described in the 2017 Nucleic Acids Research Database issue. Since the 2016 release, 103 new approved drugs were updated. The following new data sources have been included: Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), FDA Orange Book information, L1000 gene perturbation profile distance/similarity matrices and estimated protonation constants. New and existing entries have been updated with the latest information from scientific literature, drug labels and external databases. The web interface has been updated to display and query new data. The full database dump and data files are available for download from the DrugCentral website.
Collapse
Affiliation(s)
- Oleg Ursu
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Vasileios Stathias
- Center for Computational Science, Miller School of Medicine, University of Miami, Coral Gables, FL 33146, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Stephan Schürer
- Center for Computational Science, Miller School of Medicine, University of Miami, Coral Gables, FL 33146, USA
| | - Tudor Oprea
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| |
Collapse
|
209
|
Brommage R, Ohlsson C. High Fidelity of Mouse Models Mimicking Human Genetic Skeletal Disorders. Front Endocrinol (Lausanne) 2019; 10:934. [PMID: 32117046 PMCID: PMC7010808 DOI: 10.3389/fendo.2019.00934] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/23/2019] [Indexed: 12/24/2022] Open
Abstract
UNLABELLED The 2019 International Skeletal Dysplasia Society nosology update lists 441 genes for which mutations result in rare human skeletal disorders. These genes code for enzymes (33%), scaffolding proteins (18%), signal transduction proteins (16%), transcription factors (14%), cilia proteins (8%), extracellular matrix proteins (5%), and membrane transporters (4%). Skeletal disorders include aggrecanopathies, channelopathies, ciliopathies, cohesinopathies, laminopathies, linkeropathies, lysosomal storage diseases, protein-folding and RNA splicing defects, and ribosomopathies. With the goal of evaluating the ability of mouse models to mimic these human genetic skeletal disorders, a PubMed literature search identified 260 genes for which mutant mice were examined for skeletal phenotypes. These mouse models included spontaneous and ENU-induced mutants, global and conditional gene knockouts, and transgenic mice with gene over-expression or specific base-pair substitutions. The human X-linked gene ARSE and small nuclear RNA U4ATAC, a component of the minor spliceosome, do not have mouse homologs. Mouse skeletal phenotypes mimicking human skeletal disorders were observed in 249 of the 260 genes (96%) for which comparisons are possible. A supplemental table in spreadsheet format provides PubMed weblinks to representative publications of mutant mouse skeletal phenotypes. Mutations in 11 mouse genes (Ccn6, Cyp2r1, Flna, Galns, Gna13, Lemd3, Manba, Mnx1, Nsd1, Plod1, Smarcal1) do not result in similar skeletal phenotypes observed with mutations of the homologous human genes. These discrepancies can result from failure of mouse models to mimic the exact human gene mutations. There are no obvious commonalities among these 11 genes. Body BMD and/or radiologic dysmorphology phenotypes were successfully identified for 28 genes by the International Mouse Phenotyping Consortium (IMPC). Forward genetics using ENU mouse mutagenesis successfully identified 37 nosology gene phenotypes. Since many human genetic disorders involve hypomorphic, gain-of-function, dominant-negative and intronic mutations, future studies will undoubtedly utilize CRISPR/Cas9 technology to examine transgenic mice having genes modified to exactly mimic variant human sequences. Mutant mice will increasingly be employed for drug development studies designed to treat human genetic skeletal disorders. SIGNIFICANCE Great progress is being made identifying mutant genes responsible for human rare genetic skeletal disorders and mouse models for genes affecting bone mass, architecture, mineralization and strength. This review organizes data for 441 human genetic bone disorders with regard to heredity, gene function, molecular pathways, and fidelity of relevant mouse models to mimic the human skeletal disorders. PubMed weblinks to citations of 249 successful mouse models are provided.
Collapse
Affiliation(s)
- Robert Brommage
- Department of Internal Medicine and Clinical Nutrition, Centre for Bone and Arthritis Research, Institute of Medicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- *Correspondence: Robert Brommage
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Centre for Bone and Arthritis Research, Institute of Medicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
| |
Collapse
|
210
|
Structural biology and structure–function relationships of membrane proteins. Biochem Soc Trans 2018; 47:47-61. [DOI: 10.1042/bst20180269] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 10/22/2018] [Accepted: 10/29/2018] [Indexed: 01/02/2023]
Abstract
Abstract
The study of structure–function relationships of membrane proteins (MPs) has been one of the major goals in the field of structural biology. Many Noble Prizes regarding remarkable accomplishments in MP structure determination and biochemistry have been awarded over the last few decades. Mutations or improper folding of these proteins are associated with numerous serious illnesses. Therefore, as important drug targets, the study of their primary sequence and three-dimensional fold, combined with cell-based assays, provides vital information about their structure–function relationships. Today, this information is vital to drug discovery and medicine. In the last two decades, many have been the technical advances and breakthroughs in the field of MP structural biology that have contributed to an exponential growth in the number of unique MP structures in the Protein Data Bank. Nevertheless, given the medical importance and many unanswered questions, it will never be an excess of MP structures, regardless of the method used. Owing to the extension of the field, in this brief review, we will only focus on structure–function relationships of the three most significant pharmaceutical classes: G protein-coupled receptors, ion channels and transporters.
Collapse
|
211
|
Dueva E, Singh K, Kalyta A, LeBlanc E, Rennie PS, Cherkasov A. Computer-Aided Discovery of Small Molecule Inhibitors of Transcriptional Activity of TLX (NR2E1) Nuclear Receptor. Molecules 2018; 23:molecules23112967. [PMID: 30441799 PMCID: PMC6278398 DOI: 10.3390/molecules23112967] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 11/01/2018] [Accepted: 11/09/2018] [Indexed: 12/17/2022] Open
Abstract
Orphan nuclear receptor TLX (NR2E1) plays a critical role in the regulation of neural stem cells (NSC) as well as in the development of NSC-derived brain tumors. In the last years, new data have emerged implicating TLX in prostate and breast cancer. Therefore, inhibitors of TLX transcriptional activity may have a significant impact on the treatment of several critical malignancies. However, the TLX protein possesses a non-canonical ligand-binding domain (LBD), which lacks a ligand-binding pocket (conventionally targeted in case of nuclear receptors) that complicates the development of small molecule inhibitors of TLX. Herein, we utilized a rational structure-based design approach to identify small molecules targeting the Atro-box binding site of human TLX LBD. As a result of virtual screening of ~7 million molecular structures, 97 compounds were identified and evaluated in the TLX-responsive luciferase reporter assay. Among those, three chemicals demonstrated 40–50% inhibition of luciferase-detected transcriptional activity of the TLX orphan nuclear receptor at a dose of 35 µM. The identified compounds represent the first class of small molecule inhibitors of TLX transcriptional activity identified via methods of computer-aided drug discovery.
Collapse
Affiliation(s)
- Evgenia Dueva
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada.
| | - Kriti Singh
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada.
| | - Anastasia Kalyta
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada.
| | - Eric LeBlanc
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada.
| | - Paul S Rennie
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada.
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada.
| |
Collapse
|
212
|
Sinha S, Eisenhaber B, Jensen LJ, Kalbuaji B, Eisenhaber F. Darkness in the Human Gene and Protein Function Space: Widely Modest or Absent Illumination by the Life Science Literature and the Trend for Fewer Protein Function Discoveries Since 2000. Proteomics 2018; 18:e1800093. [PMID: 30265449 PMCID: PMC6282819 DOI: 10.1002/pmic.201800093] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 09/07/2018] [Indexed: 12/15/2022]
Abstract
The mentioning of gene names in the body of the scientific literature 1901-2017 and their fractional counting is used as a proxy to assess the level of biological function discovery. A literature score of one has been defined as full publication equivalent (FPE), the amount of literature necessary to achieve one publication solely dedicated to a gene. It has been found that less than 5000 human genes have each at least 100 FPEs in the available literature corpus. This group of elite genes (4817 protein-coding genes, 119 non-coding RNAs) attracts the overwhelming majority of the scientific literature about genes. Yet, thousands of proteins have never been mentioned at all, ≈2000 further proteins have not even one FPE of literature and, for ≈4600 additional proteins, the FPE count is below 10. The protein function discovery rate measured as numbers of proteins first mentioned or crossing a threshold of accumulated FPEs in a given year has grown until 2000 but is in decline thereafter. This drop is partially offset by function discoveries for non-coding RNAs. The full human genome sequencing does not boost the function discovery rate. Since 2000, the fastest growing group in the literature is that with at least 500 FPEs per gene.
Collapse
Affiliation(s)
- Swati Sinha
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
| | - Birgit Eisenhaber
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein ResearchFaculty of Health and Medical SciencesUniversity of CopenhagenDK-2200 CopenhagenDenmark
| | - Bharata Kalbuaji
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
| | - Frank Eisenhaber
- Bioinformatics Institute (BII)Agency for Science and Technology (A*STAR)Matrix138671Singapore
- School of Computer Science and Engineering (SCSE)Nanyang Technological University (NTU)637553Singapore
| |
Collapse
|
213
|
Dobrev VS, Fred LM, Gerhart KP, Metallo SJ. Characterization of the Binding of Small Molecules to Intrinsically Disordered Proteins. Methods Enzymol 2018; 611:677-702. [PMID: 30471704 DOI: 10.1016/bs.mie.2018.09.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Intrinsically disordered proteins (IDPs) comprise a large fraction of eukaryotic proteomes. IDPs are prevalent in cellular regulation, signaling networks, and disease pathways. The abundance and activity of IDPs is tightly controlled at multiple levels, and their dysregulation is associated with disease. Because of the importance of IDPs in both normal and disease states of the cell, IDPs are attractive targets for modulation by small molecules both to understand their biology and to provide potential drug leads. Multiple screens have successfully identified small molecules that bind to IDPs. Here, we describe how surface plasmon resonance, NMR, and fluorescence methods can be used to characterize the direct binding affinity between small molecules and IDPs. We describe how these techniques can contribute to identifying previously unknown small-molecule binding sites on IDPs.
Collapse
Affiliation(s)
- Veselin S Dobrev
- Department of Chemistry, Georgetown University, Washington, DC, United States
| | - Lisette M Fred
- Department of Chemistry, Georgetown University, Washington, DC, United States
| | - Kaitlyn P Gerhart
- Department of Chemistry, Georgetown University, Washington, DC, United States
| | - Steven J Metallo
- Department of Chemistry, Georgetown University, Washington, DC, United States; Institute for Soft Matter Synthesis and Metrology, Georgetown University, Washington, DC, United States.
| |
Collapse
|
214
|
Freudenberg JM, Dunham I, Sanseau P, Rajpal DK. Uncovering new disease indications for G-protein coupled receptors and their endogenous ligands. BMC Bioinformatics 2018; 19:345. [PMID: 30285606 PMCID: PMC6167889 DOI: 10.1186/s12859-018-2392-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/23/2018] [Indexed: 11/29/2022] Open
Abstract
Background The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands. Results Utilizing the data available in the Open Targets platform, potential G-protein coupled receptor and endogenous ligand disease association pairs were systematically identified. Intriguing examples such as GPR35 for inflammatory bowel disease and CXCR4 for viral infection are used as illustrations of how a systematic approach can aid in the prioritization of interesting drug discovery hypotheses. Combining evidences for G-protein coupled receptors and their corresponding endogenous peptidergic ligands increases confidence and provides supportive evidence for potential new target-disease hypotheses. Comparing such hypotheses to the global pharma drug discovery pipeline to validate the approach showed that more than 93% of G-protein coupled receptor-disease pairs with a high overall Open Targets score involved receptors with an existing drug discovery program. Conclusions The Open Targets gene-disease score can be used to prioritize potential G-protein coupled receptors-indication hypotheses. In addition, availability of multiple different evidence types markedly increases confidence as does combining evidence from known receptor-ligand pairs. Comparing the top-ranked hypotheses to the current global pharma pipeline serves validation of our approach and identifies and prioritizes new therapeutic opportunities. Electronic supplementary material The online version of this article (10.1186/s12859-018-2392-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
| | - Ian Dunham
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Philippe Sanseau
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.,Computational Biology and Stats, Target Sciences, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY, UK
| | - Deepak K Rajpal
- Computational Biology, Target Sciences, GlaxoSmithKline, Collegeville, PA, 19426, USA.
| |
Collapse
|
215
|
Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes. Molecules 2018; 23:molecules23102434. [PMID: 30249057 PMCID: PMC6222907 DOI: 10.3390/molecules23102434] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 09/19/2018] [Accepted: 09/21/2018] [Indexed: 11/16/2022] Open
Abstract
Chemical probes are of central relevance for chemical biology. To unambiguously explore the role of target proteins in triggering or mediating biological functions, small molecules used as probes should ideally be target-specific; at least, they should have sufficiently high selectivity for a primary target. We present a thorough analysis of currently available activity data for designated chemical probes to address several key questions: How well defined are chemical probes? What is their level of selectivity? Is there evidence for additional activities? Are some probes "better" than others? Therefore, highly curated chemical probes were collected and their selectivity was analyzed on the basis of publicly available compound activity data. Different selectivity patterns were observed, which distinguished designated high-quality probes.
Collapse
|