tracerDB: A crowdsourced fluorescent tracer database for target engagement analysis

tracerDB: A crowdsourced fluorescent tracer database for target engagement analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article tracerDB: A crowdsourced fluorescent tracer database for target engagement analysis Martin Schwalm, Johannes Dopfer, James Vasta, Susanne Muller, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3967452/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Investigating ligand-protein complexes is an essential technology in chemical biology and drug discovery. However, information on key reagents such as fluorescent tracers and associated data for the development of widely used NanoBRET, TR-FRET and FP assays is not easily accessible to the research community. We have created tracerDB, a curated database of validated tracers. This resource enables an open access knowledge base and a unified system for tracer and assay validation. The database is freely available at https://www.tracerdb.org/. Biological sciences/Chemical biology/Chemical tools Scientific community and society/Scientific community/Research data/Databases Biological sciences/Drug discovery/Drug screening/High-throughput screening Biological sciences/Chemical biology/Screening Figures Figure 1 Figure 2 Introduction Well-characterised, selective small molecules -‘chemical probes’ - are essential tools for target validation during drug development and in basic biological research. 1 Criteria for small molecule modulators to qualify as chemical probes have been established by chemical biologists and are widely accepted in the community. 2 These include target-related criteria for potency, selectivity, and proof for target engagement in addition to the suitability of the chemical matter itself. 1 By creating these quality criteria, chemical probes became important and generally recognized tools aiding the scientific community and accelerating drug discovery. Inspired by this approach, our goal is the standardization of quality criteria within the drug candidate evaluation process. Biochemical and cellular assays often rely on displacement assays using fluorescence-labelled molecules, called tracers (sometimes referred to as fluorescent probes - not to be confused with chemical probes themselves or medical radiotracers). 3-5 Tracers comprise a chemical moiety that binds to the protein of interest (POI), such as a small molecule, DNA, RNA, and peptides, a chemical linker, and a reporter label, typically a fluorescent dye. 6,7 To avoid interference of the linker with the binding of the molecule to the POI, the choice of the right exit vector, a solvent exposed attachment point of the linker to the molecule, is important (Figure 1 a). Tracers are used in cellular target engagement assays ( in cellulo ) such as time-resolved Förster resonance energy transfer (TR-FRET) 6 or bioluminescence resonance energy transfer (BRET) 4 assays or biochemical in vitro studies, which can be BRET, TR-FRET- based or comprise fluorescence polarization (FP) 8 . In particular, NanoBRET, a method frequently applied in kinase live-cell target engagement assays, critically relies on the use of suitable tracer molecules. This method validates the binding of a small molecule such as an inhibitor to its cognate target in the cell. It is also suitable for assessing cellular selectivity using a single tracer. 9 Owing to the stringent distance constraints of BRET and the localization-specificity of the BRET donor, tracers do not need to be specific for the protein of interest. On the contrary, unselective BRET tracers are ideal as they allow assay development for multiple targets. Using this principle, we successfully included 206 (as of Feb. 2024) validated kinase BRET assays using tracer K10 (T000008). Due to the importance of quantifying protein-ligand interactions, a large number of tracers are reported in the literature. However, scientists face several problems in establishing displacement assays for their respective target: I) finding established tracers in the literature using search engines is difficult, as much of the required information is buried in supplemental methods; II) reproducibility of the reported assays is often problematic due to insufficient validation of the tracer or unfavourable assay parameters; III) the availability of the tracer is often unknown. We created a database for fluorescent tracer molecules named tracerDB to address these problems. It has been developed and standardized to provide design and application guidance based on strict performance criteria. For each tracer-based assay, chemical structure or commercial availability is provided, as well as the assay parameters and a reference. tracerDB allows to search for the protein of interest or the tracer enabling fast assessment of available assay options for a specific target. Within the first four months (as of Feb. 2024), 38 tracers, targeting 308 different proteins in 464 experimentally validated assays were reviewed and uploaded. Scientists worldwide can submit their tracer data for review and subsequent inclusion in the tracerDB. The submission of tracer data must include all necessary information (no physical molecules) required to judge the quality and reproducibility of a tracer-based assay. First, general information such as the molecular structure (SMILES, fluorophore characteristics and trivial name) are required for the creation of a tracer page (Figure 1 b). All target proteins bound by the tracer need to be listed in UniProt 10 . Experimental data from the tracer validation process against a certain target have to be uploaded, consisting of information on a recommended concentration, Z’ value of the assay, and the observed assay window. Additionally, the measured data for tracer titration and compound displacement is required. To facilitate the upload and review process, a submission file is available for download at the footer of the website (https://www.tracerdb.org/). After insertion of all required information, the sheet is sent to [email protected] , for final approval and upload. Additionally, tracer IDs can be assigned prior publication, allowing a direct link to the database (in analogy to PDB). The interaction network between tracer molecules and their respective targets can be modelled as a many-to-many relationship. As a result, the underlying database structure consists of three entity sets: the tracer, the protein, and their interaction (Figure 1b). To ensure a user-friendly submission of data and standardize the presentation, all molecular representations and calculations are created and executed on the server side. We chose Django 11 as a python-based web framework together with a MySQL database to enable high-frequency read operations. In addition to the information on the crowdsourced tracers, we have also included general information on tracer molecules and illustrations of different assay systems on the 'about' page (https://www.tracerdb.org/about/). Here, we describe the quality control criteria and how to calculate the respective values. In order to further increase the reproducibility of the described assays, each assay is classified according to its parameters into robust, expert and unsuitable assays with exemplary data for clarification (Figure 2). These assay levels are represented by a traffic light icon for each registered assay. In addition, we have included a methods section describing the different assays used to collect the submitted data (https://www.tracerdb.org/methods/). This is supported by an illustration and key references. As an additional purpose, the chimeric structure of tracer molecules provides an excellent basis for the development of degraders including proteolysis targeting chimeras (PROTACs). It allows for the replacement of the fluorescent dye with a binder to an E3 ubiquitin ligase. A PROTAC leads to the formation of a ternary complex (POI, E3 ligase and PROTAC), ubiquitination and subsequent proteasomal degradation of the target protein. 12 The validated exit vector in the tracer, assessed tolerance of the linker, and -in case of NanoBRET- limited information about cellular permeability of the parent ligand, providing an ideal starting point for PROTAC synthesis. Furthermore, a functional NanoBRET assay indicates which protein terminus is suitable for fusion proteins. TracerDB therefore marks the first resource for drug-screening scientists as well as the chemical biology community, that gathers detailed, reviewed and high-quality information on tracer-based assays and their applications. Methods Architecture of the database: RDkit 13 , a commonly used cheminformatics package for python is employed to render SMILES strings as two-dimensional molecular representations. The average molecular weight and the estimated logP value of the compound- and peptide-based tracers are calculated using RDkit’s implemented methods for molecular descriptors. In order to avoid having to deal with complex SMILES of large peptide tracers, the pyPept package 14 has been incorporated into this project to allow for flexible declaration of custom amino acids, i.e. fluorophore peptide labels. These artificial building blocks are then included into the string representation of the peptides and stored in the database as BILN 15 . To ensure consistency in the depiction and analysis of experimental data uploaded to the webserver, fitting and plotting are executed on the server side. The experimental titration data is plotted via Matplotlib 16 and the fitting is conducted through SciPy 17 using non-linear least squares optimization. It is assumed that the data from concentration response experiments exhibit a sigmoidal shape. Hence, to fit the data the following logistic equation is employed: $$f\left(x\right)=\frac{a}{1+{e}^{-b\left(x-{\text{XC}}_{\text{50}}\right)}}+c$$ The response of the measurement is a function of the logarithmic concentration \(x\) , with the additional parameters \(a\) , \(b\) , and, \(c\) which are utilized to scale and transform \(f\) , because the input is not normalized. \({\text{XC}}_{50}\) is the parameter determining the log concentration halfway between the plateaus of the sigmoidal curve. Depending on the experimental context this parameter may be interpreted as \({\text{EC}}_{50}\) or \({\text{IC}}_{50}\) . Protein titrations performed during the development of fluorescence polarization assay are commonly plotted as signal in millipolarization units versus the molar concentration. These saturation curves are estimated using the following hyperbolic model: $$f\left(x\right)=\frac{{B}_{max}\bullet x}{{K}_{d}+x}+cx+d$$ where \({B}_{max}\) denotes the extrapolated maximum specific binding to the protein for high ligand concentrations. \({K}_{d}\) is the equilibrium dissociation constant, which specifies the concentration \(x\) required for half-maximum binding at equilibrium. The parameter \(c\) accounts for the ratio of nonspecific binding to total binding and \(d\) corrects for background signals 18 . Protein information is automatically retrieved through the UniProt REST API, enabling the search for alternative protein and gene names. The retrieved XML files are processed using Biopython’s UniProt parser 19 , resulting in standardized and well-annotated protein entries, ultimately leading to more robust search functionality. Declarations Conflict of interest: The authors have no conflict of interest to declare Acknowledgements: The authors are thankful for all current and future tracer submissions from diverse laboratories, especially the extensive submissions of the Arrowsmith and Mazitschek Labs. M.P.S., J.D., S.M.K. and S.K. are grateful for support by the Structural Genomics Consortium (SGC), a registered charity (no: 1097737) that receives funds from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute, EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking [EUbOPEN grant 875510], Janssen, Merck KGaA, Pfizer and Takeda, and by the German Cancer Research Center DKTK, and the Frankfurt Cancer Institute (FCI). M.P.S. is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), CRC1430 (Project-ID 424228829). J.D.V and M. B. R. are employees of Promega Corp. Figures were created with BioRender.com. Tracer dye structures within the TOC were reproduced with permission from Promega Corporation ©2024 Promega Corporation. References Hartung, I. V., Rudolph, J., Mader, M. M., Mulder, M. P. C. & Workman, P. Expanding Chemical Probe Space: Quality Criteria for Covalent and Degrader Probes. J Med Chem 66 , 9297-9312 (2023). https://doi.org:10.1021/acs.jmedchem.3c00550 Muller, S. et al. Target 2035 - update on the quest for a probe for every protein. RSC Med Chem 13 , 13-21 (2022). https://doi.org:10.1039/d1md00228g Schwalm, M. P. et al. Tracking the PROTAC degradation pathway in living cells highlights the importance of ternary complex measurement for PROTAC optimization. Cell Chem Biol (2023). https://doi.org:10.1016/j.chembiol.2023.06.002 Robers, M. B. et al. Target engagement and drug residence time can be observed in living cells with BRET. Nat Commun 6 , 10091 (2015). https://doi.org:10.1038/ncomms10091 Cho, E. J. & Dalby, K. N. Luminescence Energy Transfer-Based Screening and Target Engagement Approaches for Chemical Biology and Drug Discovery. SLAS Discov 26 , 984-994 (2021). https://doi.org:10.1177/24725552211036056 Payne, N. C., Kalyakina, A. S., Singh, K., Tye, M. A. & Mazitschek, R. Bright and stable luminescent probes for target engagement profiling in live cells. Nat Chem Biol 17 , 1168-1177 (2021). https://doi.org:10.1038/s41589-021-00877-5 Blazer, L. L. et al. A Suite of Biochemical Assays for Screening RNA Methyltransferase BCDIN3D. SLAS Discov 22 , 32-39 (2017). https://doi.org:10.1177/1087057116666276 Schwalm, M. P. et al. Targeting LC3/GABARAP for degrader development and autophagy modulation. bioRxiv , 2023.2010. 2005.560930 (2023). Robers, M. B. et al. Single tracer-based protocol for broad-spectrum kinase profiling in live cells with NanoBRET. STAR Protoc 2 , 100822 (2021). https://doi.org:10.1016/j.xpro.2021.100822 UniProt, C. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47 , D506-D515 (2019). https://doi.org:10.1093/nar/gky1049 Django (Version 4.2) Retrieved from https://www.djangoproject.com/ (2023). Bekes, M., Langley, D. R. & Crews, C. M. PROTAC targeted protein degraders: the past is prologue. Nat Rev Drug Discov 21 , 181-200 (2022). https://doi.org:10.1038/s41573-021-00371-6 RDKit: Open-source cheminformatics. https://www.rdkit.org , (2023). Ochoa, R., Brown, J. B. & Fox, T. pyPept: a python library to generate atomistic 2D and 3D representations of peptides. J Cheminform 15 , 79 (2023). https://doi.org:10.1186/s13321-023-00748-2 Fox, T. et al. BILN: A Human-Readable Line Notation for Complex Peptides. J Chem Inf Model 62 , 3942-3947 (2022). https://doi.org:10.1021/acs.jcim.2c00703 Hunter, J. D. Matplotlib: A 2D graphics environment. Computing in science & engineering 9 , 90-95 (2007). Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17 , 261-272 (2020). https://doi.org:10.1038/s41592-019-0686-2 Motulsky, H. & Christopoulos, A. Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting . (Oxford University Press, 2004). Cock, P. J. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25 , 1422-1423 (2009). https://doi.org:10.1093/bioinformatics/btp163 Schwalm, M. P. et al. A Toolbox for the Generation of Chemical Probes for Baculovirus IAP Repeat Containing Proteins. Front Cell Dev Biol 10 , 886537 (2022). https://doi.org:10.3389/fcell.2022.886537 Additional Declarations There is NO Competing Interest. Supplementary Files GraphicalAbstract.png reportingsummaryTracerDB.pdf Reporting Summary Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3967452","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":284267533,"identity":"cada1e3a-5fce-48ba-958a-42e77476ba4a","order_by":0,"name":"Martin Schwalm","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-1252-1829","institution":"Goethe University Frankfurt","correspondingAuthor":true,"prefix":"","firstName":"Martin","middleName":"","lastName":"Schwalm","suffix":""},{"id":284267534,"identity":"289d3555-bea9-4c3a-baa6-917c08f00dab","order_by":1,"name":"Johannes Dopfer","email":"","orcid":"https://orcid.org/0009-0008-6356-1864","institution":"Goethe-University Frankfurt","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Dopfer","suffix":""},{"id":284267535,"identity":"1b672312-7f3d-40e4-a6f3-f78838f37ba4","order_by":2,"name":"James Vasta","email":"","orcid":"","institution":"Promega Corp.","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Vasta","suffix":""},{"id":284267536,"identity":"d8436f35-4d0f-4c1f-847e-83f7897dd99e","order_by":3,"name":"Susanne Muller","email":"","orcid":"https://orcid.org/0000-0003-2402-4157","institution":"Buchmann Institute for Molecular Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Susanne","middleName":"","lastName":"Muller","suffix":""},{"id":284267537,"identity":"41106002-3b4a-453d-b300-9f2a324e9a1f","order_by":4,"name":"Stefan Knapp","email":"","orcid":"","institution":"Goethe-University Frankfurt","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Knapp","suffix":""},{"id":284267538,"identity":"e4832273-f3a5-4dbf-8126-411567213085","order_by":5,"name":"Matthew Robers","email":"","orcid":"https://orcid.org/0000-0001-8505-5227","institution":"Promega (United States)","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Robers","suffix":""}],"badges":[],"createdAt":"2024-02-18 16:25:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3967452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3967452/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53619542,"identity":"15a1d8c0-939c-4b4f-a413-1639b96789a0","added_by":"auto","created_at":"2024-03-28 07:21:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":416948,"visible":true,"origin":"","legend":"\u003cp\u003eComposition of a tracer (T000001)\u003csup\u003e20\u003c/sup\u003e and the principle underlying the tracerDB. a) schematic representation of a tracer molecule with its 3 distinct substructures. The POI ligand marks the binding moiety to the target protein, ultimately generating the proximity between target and the label. The optical reporter (dye) is chemically linked to this POI ligand via a chemical linker. The attached dye is selected according to the requirements of the assay system to achieve the desired excitation and emission wavelengths. b) underlying principles of tracer and experimental data processing. Information provided through submission is displayed in the upper panels outside the tracerDB framework. This data is parsed and assigned to each entity: the tracer molecule, the protein and interaction between the former as shown in detail in Figure\u0026nbsp;2.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3967452/v1/26ee629fbbe05c9f9a5121ef.png"},{"id":53619539,"identity":"614758e9-af32-41d2-afe2-f49beedd16a5","added_by":"auto","created_at":"2024-03-28 07:21:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":620083,"visible":true,"origin":"","legend":"\u003cp\u003eData input and processing carried out by the webserver. Input data is depicted on the left, outside the tracerDB framework. Calculations carried out by the database are marked with a processor symbol. First, the database calculates tracer parameters and generates a schematic representation of the tracer molecule. Next, assay parameters and experimental data are uploaded and processed. From the experimental data (.csv file) the number of replicates is extracted and the datapoints are plotted. The data is interpolated using the indicated function to yield the tracer K\u003csub\u003ed\u003c/sub\u003e and displacement K\u003csub\u003ed\u003c/sub\u003e. The recommended tracer concentration is estimated from the tracer K\u003csub\u003ed\u003c/sub\u003e but can be changed, if more optimal conditions are known. Finally, the target is registered using its UniProt ID, resulting in searchable accession numbers, gene, and protein names.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3967452/v1/2ddaaddf926f50eb379ad80d.png"},{"id":53620006,"identity":"f9496cd8-a1b9-454b-8ae6-a15022213a74","added_by":"auto","created_at":"2024-03-28 07:29:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":652166,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3967452/v1/a428711b-646a-40ba-b0a0-10e0cde89b3e.pdf"},{"id":53619538,"identity":"61c272d4-8956-4872-a057-139595209374","added_by":"auto","created_at":"2024-03-28 07:21:45","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1430795,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-3967452/v1/396819fa4355afac6736186a.png"},{"id":53619547,"identity":"b5b238be-ea9d-4cca-932d-4ba136a9cecd","added_by":"auto","created_at":"2024-03-28 07:21:47","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1664733,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"reportingsummaryTracerDB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3967452/v1/48e693e14df841cb0d1f1b9d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"tracerDB: A crowdsourced fluorescent tracer database for target engagement analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWell-characterised, selective small molecules -\u0026lsquo;chemical probes\u0026rsquo; - are essential tools for target validation during drug development and in basic biological research.\u003csup\u003e1\u003c/sup\u003e Criteria for small molecule modulators to qualify as chemical probes have been established by chemical biologists and are widely accepted in the community.\u003csup\u003e2\u003c/sup\u003e These include target-related criteria for potency, selectivity, and proof for target engagement in addition to the suitability of the chemical matter itself.\u003csup\u003e1\u003c/sup\u003e By creating these quality criteria, chemical probes became important and generally recognized tools aiding the scientific community and accelerating drug discovery. Inspired by this approach, our goal is the standardization of quality criteria within the drug candidate evaluation process. Biochemical and cellular assays often rely on displacement assays using fluorescence-labelled molecules, called tracers (sometimes referred to as fluorescent probes - not to be confused with chemical probes themselves or medical radiotracers).\u003csup\u003e3-5\u003c/sup\u003e Tracers comprise a chemical moiety that binds to the protein of interest (POI), such as a small molecule, DNA, RNA, and peptides, a chemical linker, and a reporter label, typically a fluorescent dye.\u003csup\u003e6,7\u003c/sup\u003e To avoid interference of the linker with the binding of the molecule to the POI, the choice of the right exit vector, a solvent exposed attachment point of the linker to the molecule, is important (Figure 1 a).\u003c/p\u003e\n\u003cp\u003eTracers are used in cellular target engagement assays (\u003cem\u003ein cellulo\u003c/em\u003e) such as time-resolved F\u0026ouml;rster resonance energy transfer (TR-FRET)\u003csup\u003e6\u003c/sup\u003e or bioluminescence resonance energy transfer (BRET)\u003csup\u003e4\u003c/sup\u003e assays or biochemical \u003cem\u003ein vitro\u003c/em\u003e studies, which can be BRET, TR-FRET- based or comprise fluorescence polarization (FP)\u003csup\u003e8\u003c/sup\u003e. In particular, NanoBRET, a method frequently applied in kinase live-cell target engagement assays, critically relies on the use of suitable tracer molecules. This method validates the binding of a small molecule such as an inhibitor to its cognate target in the cell. It is also suitable for assessing cellular selectivity using a single tracer.\u003csup\u003e9\u003c/sup\u003e Owing to the stringent distance constraints of BRET and the localization-specificity of the BRET donor, tracers do not need to be specific for the protein of interest. On the contrary, unselective BRET tracers are ideal as they allow assay development for multiple targets. Using this principle, we successfully included 206 (as of Feb. 2024) validated kinase BRET assays using tracer K10 (T000008).\u003c/p\u003e\n\u003cp\u003eDue to the importance of quantifying protein-ligand interactions, a large number of tracers are reported in the literature. However, scientists face several problems in establishing displacement assays for their respective target: I) finding established tracers in the literature using search engines is difficult, as much of the required information is buried in supplemental methods; II) reproducibility of the reported assays is often problematic due to insufficient validation of the tracer or unfavourable assay parameters; III) the availability of the tracer is often unknown. We created a database for fluorescent tracer molecules named tracerDB to address these problems. It has been developed and standardized to provide design and application guidance based on strict performance criteria. For each tracer-based assay, chemical structure or commercial availability is provided, as well as the assay parameters and a reference. tracerDB allows to search for the protein of interest or the tracer enabling fast assessment of available assay options for a specific target. Within the first four months (as of Feb. 2024), 38 tracers, targeting 308 different proteins in 464 experimentally validated assays were reviewed and uploaded.\u003c/p\u003e\n\u003cp\u003eScientists worldwide can submit their tracer data for review and subsequent inclusion in the tracerDB. The submission of tracer data must include all necessary information (no physical molecules) required to judge the quality and reproducibility of a tracer-based assay. First, general information such as the molecular structure (SMILES, fluorophore characteristics and trivial name) are required for the creation of a tracer page (Figure 1 b). All target proteins bound by the tracer need to be listed in UniProt\u003csup\u003e10\u003c/sup\u003e. Experimental data from the tracer validation process against a certain target have to be uploaded, consisting of information on a recommended concentration, Z\u0026rsquo; value of the assay, and the observed assay window. Additionally, the measured data for tracer titration and compound displacement is required. To facilitate the upload and review process, a submission file is available for download at the footer of the website (https://www.tracerdb.org/). After insertion of all required information, the sheet is sent to [email protected], for final approval and upload. Additionally, tracer IDs can be assigned prior publication, allowing a direct link to the database (in analogy to PDB).\u003c/p\u003e\n\u003cp\u003eThe interaction network between tracer molecules and their respective targets can be modelled as a many-to-many relationship. As a result, the underlying database structure consists of three entity sets: the tracer, the protein, and their interaction (Figure 1b). To ensure a user-friendly submission of data and standardize the presentation, all molecular representations and calculations are created and executed on the server side. We chose Django\u003csup\u003e11\u003c/sup\u003e as a python-based web framework together with a MySQL database to enable high-frequency read operations.\u003c/p\u003e\n\u003cp\u003eIn addition to the information on the crowdsourced tracers, we have also included general information on tracer molecules and illustrations of different assay systems on the \u0026apos;about\u0026apos; page (https://www.tracerdb.org/about/). Here, we describe the quality control criteria and how to calculate the respective values. In order to further increase the reproducibility of the described assays, each assay is classified according to its parameters into robust, expert and unsuitable assays with exemplary data for clarification (Figure 2). These assay levels are represented by a traffic light icon for each registered assay. In addition, we have included a methods section describing the different assays used to collect the submitted data (https://www.tracerdb.org/methods/). This is supported by an illustration and key references.\u003c/p\u003e\n\u003cp\u003eAs an additional purpose, the chimeric structure of tracer molecules provides an excellent basis for the development of degraders including proteolysis targeting chimeras (PROTACs). It allows for the replacement of the fluorescent dye with a binder to an E3 ubiquitin ligase. A PROTAC leads to the formation of a ternary complex (POI, E3 ligase and PROTAC), ubiquitination and subsequent proteasomal degradation of the target protein.\u003csup\u003e12\u003c/sup\u003e The validated exit vector in the tracer, assessed tolerance of the linker, and -in case of NanoBRET- limited information about cellular permeability of the parent ligand, providing an ideal starting point for PROTAC synthesis. Furthermore, a functional NanoBRET assay indicates which protein terminus is suitable for fusion proteins.\u003c/p\u003e\n\u003cp\u003eTracerDB therefore marks the first resource for drug-screening scientists as well as the chemical biology community, that gathers detailed, reviewed and high-quality information on tracer-based assays and their applications. \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eArchitecture of the database:\u003c/p\u003e \u003cp\u003eRDkit\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, a commonly used cheminformatics package for python is employed to render SMILES strings as two-dimensional molecular representations. The average molecular weight and the estimated logP value of the compound- and peptide-based tracers are calculated using RDkit\u0026rsquo;s implemented methods for molecular descriptors. In order to avoid having to deal with complex SMILES of large peptide tracers, the pyPept package\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e has been incorporated into this project to allow for flexible declaration of custom amino acids, i.e. fluorophore peptide labels. These artificial building blocks are then included into the string representation of the peptides and stored in the database as BILN\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. To ensure consistency in the depiction and analysis of experimental data uploaded to the webserver, fitting and plotting are executed on the server side. The experimental titration data is plotted via Matplotlib\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and the fitting is conducted through SciPy\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e using non-linear least squares optimization. It is assumed that the data from concentration response experiments exhibit a sigmoidal shape. Hence, to fit the data the following logistic equation is employed:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$f\\left(x\\right)=\\frac{a}{1+{e}^{-b\\left(x-{\\text{XC}}_{\\text{50}}\\right)}}+c$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe response of the measurement is a function of the logarithmic concentration \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e, with the additional parameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(a\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(b\\)\u003c/span\u003e\u003c/span\u003e, and, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\)\u003c/span\u003e\u003c/span\u003e which are utilized to scale and transform \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(f\\)\u003c/span\u003e\u003c/span\u003e, because the input is not normalized. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{XC}}_{50}\\)\u003c/span\u003e\u003c/span\u003e is the parameter determining the log concentration halfway between the plateaus of the sigmoidal curve. Depending on the experimental context this parameter may be interpreted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{EC}}_{50}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{IC}}_{50}\\)\u003c/span\u003e\u003c/span\u003e. Protein titrations performed during the development of fluorescence polarization assay are commonly plotted as signal in millipolarization units versus the molar concentration. These saturation curves are estimated using the following hyperbolic model:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$f\\left(x\\right)=\\frac{{B}_{max}\\bullet x}{{K}_{d}+x}+cx+d$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({B}_{max}\\)\u003c/span\u003e\u003c/span\u003e denotes the extrapolated maximum specific binding to the protein for high ligand concentrations. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}_{d}\\)\u003c/span\u003e\u003c/span\u003e is the equilibrium dissociation constant, which specifies the concentration \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e required for half-maximum binding at equilibrium. The parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\)\u003c/span\u003e\u003c/span\u003e accounts for the ratio of nonspecific binding to total binding and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(d\\)\u003c/span\u003e\u003c/span\u003e corrects for background signals\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProtein information is automatically retrieved through the UniProt REST API, enabling the search for alternative protein and gene names. The retrieved XML files are processed using Biopython\u0026rsquo;s UniProt parser\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, resulting in standardized and well-annotated protein entries, ultimately leading to more robust search functionality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e \u003cp\u003eThe authors have no conflict of interest to declare\u003c/p\u003e \u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors are thankful for all current and future tracer submissions from diverse laboratories, especially the extensive submissions of the Arrowsmith and Mazitschek Labs.\u003c/p\u003e\n\u003cp\u003eM.P.S., J.D., S.M.K. and S.K. are grateful for support by the Structural Genomics Consortium (SGC), a registered charity (no: 1097737) that receives funds from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute, EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking [EUbOPEN grant 875510], Janssen, Merck KGaA, Pfizer and Takeda, and by the German Cancer Research Center DKTK, and the Frankfurt Cancer Institute (FCI). M.P.S. is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), CRC1430 (Project-ID 424228829). J.D.V and M. B. R. are employees of Promega Corp. Figures were created with BioRender.com. Tracer dye structures within the TOC were reproduced with permission from Promega Corporation \u0026copy;2024 Promega Corporation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHartung, I. V., Rudolph, J., Mader, M. M., Mulder, M. P. C. \u0026amp; Workman, P. 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B. \u0026amp; Fox, T. pyPept: a python library to generate atomistic 2D and 3D representations of peptides. \u003cem\u003eJ Cheminform\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 79 (2023). https://doi.org:10.1186/s13321-023-00748-2\u003c/li\u003e\n\u003cli\u003eFox, T.\u003cem\u003e et al.\u003c/em\u003e BILN: A Human-Readable Line Notation for Complex Peptides. \u003cem\u003eJ Chem Inf Model\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 3942-3947 (2022). https://doi.org:10.1021/acs.jcim.2c00703\u003c/li\u003e\n\u003cli\u003eHunter, J. D. Matplotlib: A 2D graphics environment. \u003cem\u003eComputing in science \u0026amp; engineering\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 90-95 (2007).\u003c/li\u003e\n\u003cli\u003eVirtanen, P.\u003cem\u003e et al.\u003c/em\u003e SciPy 1.0: fundamental algorithms for scientific computing in Python. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 261-272 (2020). https://doi.org:10.1038/s41592-019-0686-2\u003c/li\u003e\n\u003cli\u003eMotulsky, H. \u0026amp; Christopoulos, A. \u003cem\u003eFitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting\u003c/em\u003e. (Oxford University Press, 2004).\u003c/li\u003e\n\u003cli\u003eCock, P. J.\u003cem\u003e et al.\u003c/em\u003e Biopython: freely available Python tools for computational molecular biology and bioinformatics. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1422-1423 (2009). https://doi.org:10.1093/bioinformatics/btp163\u003c/li\u003e\n\u003cli\u003eSchwalm, M. P.\u003cem\u003e et al.\u003c/em\u003e A Toolbox for the Generation of Chemical Probes for Baculovirus IAP Repeat Containing Proteins. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 886537 (2022). https://doi.org:10.3389/fcell.2022.886537\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3967452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3967452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInvestigating ligand-protein complexes is an essential technology in chemical biology and drug discovery. However, information on key reagents such as fluorescent tracers and associated data for the development of widely used NanoBRET, TR-FRET and FP assays is not easily accessible to the research community. We have created tracerDB, a curated database of validated tracers. This resource enables an open access knowledge base and a unified system for tracer and assay validation. The database is freely available at https://www.tracerdb.org/.\u003c/p\u003e","manuscriptTitle":"tracerDB: A crowdsourced fluorescent tracer database for target engagement analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 07:21:27","doi":"10.21203/rs.3.rs-3967452/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f79a986d-a006-47f6-a85c-68a445856d4a","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":29926099,"name":"Biological sciences/Chemical biology/Chemical tools"},{"id":29926100,"name":"Scientific community and society/Scientific community/Research data/Databases"},{"id":29926101,"name":"Biological sciences/Drug discovery/Drug screening/High-throughput screening"},{"id":29926102,"name":"Biological sciences/Chemical biology/Screening"}],"tags":[],"updatedAt":"2024-06-20T16:30:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 07:21:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3967452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3967452","identity":"rs-3967452","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}