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Rama-Garda R, Domínguez E, Loza MI, Lallena MJ, de Blas J, Toledo MÁ, Haro R. High-Throughput DNA-Encoded Libraries Affinity Selection Platform for Binder Identification with Solid Support Protein Immobilization. Assay Drug Dev Technol 2024; 22:192-202. [PMID: 38638103 DOI: 10.1089/adt.2024.010] [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: 04/20/2024] Open
Abstract
DNA-encoded libraries (DELs) have demonstrated to be one of the most powerful technologies within the ligand identification toolbox, widely used either in academia or biotech and pharma companies. DEL methodology utilizes affinity selection (AS) as the approach to interrogate the protein of interest for the identification of binders. Here we present a high-throughput, fully automated AS platform developed to fulfill industrial standards and compatible with different assay formats to improve the reproducibility of the AS process for DEL binders identification. This platform is flexible enough to virtually set aside all kinds of DELs and AS methods and conditions using immobilized proteins. It bears the two main immobilization methods to support of the proteins of interest: magnetic beads or resin tip columns. A combination of a broad variety of protocol options with a wide range of different experimental conditions can be set up with a throughput of 96 samples at the same time. In addition, small modifications of the protocols provide the platform with the versatility to run not only the routine DEL screens, but also test covalent libraries, the successful immobilization of the proteins of interest, and many other experiments that may be required. This versatile AS platform for DEL can be a powerful instrument for direct application of the technology in academic and industry settings.
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Affiliation(s)
- Ramón Rama-Garda
- Discovery Chemistry Research & Technologies, Lilly Research Laboratories, Eli Lilly and Company, Alcobendas, Spain
- BioFarma, Universidad de Santiago de Compostela (USC), Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), A Coruña, Spain
| | - Eduardo Domínguez
- Genomic Medicine, Universidad de Santiago de Compostela (USC), Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), A Coruña, Spain
| | - María Isabel Loza
- BioFarma, Universidad de Santiago de Compostela (USC), Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), A Coruña, Spain
| | - María José Lallena
- Discovery Chemistry Research & Technologies, Lilly Research Laboratories, Eli Lilly and Company, Alcobendas, Spain
| | - Jesús de Blas
- Discovery Chemistry Research & Technologies, Lilly Research Laboratories, Eli Lilly and Company, Alcobendas, Spain
| | - Miguel Ángel Toledo
- Discovery Chemistry Research & Technologies, Lilly Research Laboratories, Eli Lilly and Company, Alcobendas, Spain
| | - Rubén Haro
- Discovery Chemistry Research & Technologies, Lilly Research Laboratories, Eli Lilly and Company, Alcobendas, Spain
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2
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Zhang C, Pitman M, Dixit A, Leelananda S, Palacci H, Lawler M, Belyanskaya S, Grady L, Franklin J, Tilmans N, Mobley DL. Building Block-Based Binding Predictions for DNA-Encoded Libraries. J Chem Inf Model 2023; 63:5120-5132. [PMID: 37578123 PMCID: PMC10466377 DOI: 10.1021/acs.jcim.3c00588] [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: 04/24/2023] [Indexed: 08/15/2023]
Abstract
DNA-encoded libraries (DELs) provide the means to make and screen millions of diverse compounds against a target of interest in a single experiment. However, despite producing large volumes of binding data at a relatively low cost, the DEL selection process is susceptible to noise, necessitating computational follow-up to increase signal-to-noise ratios. In this work, we present a set of informatics tools to employ data from prior DEL screen(s) to gain information about which building blocks are most likely to be productive when designing new DELs for the same target. We demonstrate that similar building blocks have similar probabilities of forming compounds that bind. We then build a model from the inference that the combined behavior of individual building blocks is predictive of whether an overall compound binds. We illustrate our approach on a set of three-cycle OpenDEL libraries screened against soluble epoxide hydrolase (sEH) and report performance of more than an order of magnitude greater than random guessing on a holdout set, demonstrating that our model can serve as a baseline for comparison against other machine learning models on DEL data. Lastly, we provide a discussion on how we believe this informatics workflow could be applied to benefit researchers in their specific DEL campaigns.
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Affiliation(s)
- Chris Zhang
- Department
of Chemistry, University of California,
Irvine, 1120 Natural Sciences II, Irvine, California 92697, United States
| | - Mary Pitman
- Department
of Pharmaceutical Sciences, University of
California, Irvine, 856
Health Sciences Road, Irvine, California 92697, United States
| | - Anjali Dixit
- Department
of Pharmaceutical Sciences, University of
California, Irvine, 856
Health Sciences Road, Irvine, California 92697, United States
| | - Sumudu Leelananda
- Anagenex, 20 Maguire Road Suite 302, Lexington, Massachusetts 02421, United States
| | - Henri Palacci
- Anagenex, 20 Maguire Road Suite 302, Lexington, Massachusetts 02421, United States
| | - Meghan Lawler
- Anagenex, 20 Maguire Road Suite 302, Lexington, Massachusetts 02421, United States
| | - Svetlana Belyanskaya
- Anagenex, 20 Maguire Road Suite 302, Lexington, Massachusetts 02421, United States
| | - LaShadric Grady
- Anagenex, 20 Maguire Road Suite 302, Lexington, Massachusetts 02421, United States
| | - Joe Franklin
- Anagenex, 20 Maguire Road Suite 302, Lexington, Massachusetts 02421, United States
| | - Nicolas Tilmans
- Anagenex, 20 Maguire Road Suite 302, Lexington, Massachusetts 02421, United States
| | - David L. Mobley
- Department
of Chemistry, University of California,
Irvine, 1120 Natural Sciences II, Irvine, California 92697, United States
- Department
of Pharmaceutical Sciences, University of
California, Irvine, 856
Health Sciences Road, Irvine, California 92697, United States
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3
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Hou R, Xie C, Gui Y, Li G, Li X. Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries. ACS OMEGA 2023; 8:19057-19071. [PMID: 37273617 PMCID: PMC10233830 DOI: 10.1021/acsomega.3c02152] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure-activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections.
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Affiliation(s)
- Rui Hou
- Department
of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong SAR, China
- Laboratory
for Synthetic Chemistry and Chemical Biology LimitedHealth@InnoHK, Innovation and Technology Commission, Hong Kong SAR, China
| | - Chao Xie
- Department
of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yuhan Gui
- Department
of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong SAR, China
| | - Gang Li
- Institute
of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Xiaoyu Li
- Department
of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong SAR, China
- Laboratory
for Synthetic Chemistry and Chemical Biology LimitedHealth@InnoHK, Innovation and Technology Commission, Hong Kong SAR, China
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4
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Gironda-Martínez A, Donckele EJ, Samain F, Neri D. DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges. ACS Pharmacol Transl Sci 2021; 4:1265-1279. [PMID: 34423264 PMCID: PMC8369695 DOI: 10.1021/acsptsci.1c00118] [Citation(s) in RCA: 111] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Indexed: 12/27/2022]
Abstract
DNA-encoded chemical libraries (DELs) represent a versatile and powerful technology platform for the discovery of small-molecule ligands to protein targets of biological and pharmaceutical interest. DELs are collections of molecules, individually coupled to distinctive DNA tags serving as amplifiable identification barcodes. Thanks to advances in DNA-compatible reactions, selection methodologies, next-generation sequencing, and data analysis, DEL technology allows the construction and screening of libraries of unprecedented size, which has led to the discovery of highly potent ligands, some of which have progressed to clinical trials. In this Review, we present an overview of diverse approaches for the generation and screening of DEL molecular repertoires. Recent success stories are described, detailing how novel ligands were isolated from DEL screening campaigns and were further optimized by medicinal chemistry. The goal of the Review is to capture some of the most recent developments in the field, while also elaborating on future challenges to further improve DEL technology as a therapeutic discovery platform.
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Affiliation(s)
| | | | - Florent Samain
- Philochem
AG, Libernstrasse 3, CH-8112 Otelfingen, Switzerland
| | - Dario Neri
- Department
of Chemistry and Applied Biosciences, Swiss
Federal Institute of Technology, CH-8093 Zürich, Switzerland
- Philogen
S.p.A, 53100 Siena, Italy
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Huang Y, Li X. Recent Advances on the Selection Methods of DNA-Encoded Libraries. Chembiochem 2021; 22:2384-2397. [PMID: 33891355 DOI: 10.1002/cbic.202100144] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 04/23/2021] [Indexed: 12/15/2022]
Abstract
DNA-encoded libraries (DEL) have come of age and become a major technology platform for ligand discovery in both academia and the pharmaceutical industry. Technological maturation in the past two decades and the recent explosive developments of DEL-compatible chemistries have greatly improved the chemical diversity of DELs and fueled its applications in drug discovery. A relatively less-covered aspect of DELs is the selection method. Typically, DEL selection is considered as a binding assay and the selection is conducted with purified protein targets immobilized on a matrix, and the binders are separated from the non-binding background via physical washes. However, the recent innovations in DEL selection methods have not only expanded the target scope of DELs, but also revealed the potential of the DEL technology as a powerful tool in exploring fundamental biology. In this Review, we first cover the "classic" DEL selection methods with purified proteins on solid phase, and then we discuss the strategies to realize DEL selections in solution phase. Finally, we focus on the emerging approaches for DELs to interrogate complex biological targets.
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Affiliation(s)
- Yiran Huang
- Department of Chemistry and the State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Xiaoyu Li
- Department of Chemistry and the State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
- Laboratory for Synthetic Chemistry and Chemical Biology Limited, Health@InnoHK, Innovation and Technology Commission, Units 1503-1511, 15/F., Building 17W, Hong Kong Science and Technology Parks, New Territories, Hong Kong SAR, China
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Goodnow R. DNA-Encoded Library Technology (DELT) After a Quarter Century. SLAS DISCOVERY 2019; 23:385-386. [PMID: 29781351 DOI: 10.1177/2472555218766250] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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7
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Faver JC, Riehle K, Lancia DR, Milbank JBJ, Kollmann CS, Simmons N, Yu Z, Matzuk MM. Quantitative Comparison of Enrichment from DNA-Encoded Chemical Library Selections. ACS COMBINATORIAL SCIENCE 2019; 21:75-82. [PMID: 30672692 PMCID: PMC6372980 DOI: 10.1021/acscombsci.8b00116] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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DNA-encoded
chemical libraries (DELs) provide a high-throughput
and cost-effective route for screening billions of unique molecules
for binding affinity for diverse protein targets. Identifying candidate
compounds from these libraries involves affinity selection, DNA sequencing,
and measuring enrichment in a sample pool of DNA barcodes. Successful
detection of potent binders is affected by many factors, including
selection parameters, chemical yields, library amplification, sequencing
depth, sequencing errors, library sizes, and the chosen enrichment
metric. To date, there has not been a clear consensus about how enrichment
from DEL selections should be measured or reported. We propose a normalized z-score enrichment metric using a binomial distribution
model that satisfies important criteria that are relevant for analysis
of DEL selection data. The introduced metric is robust with respect
to library diversity and sampling and allows for quantitative comparisons
of enrichment of n-synthons from parallel DEL selections.
These features enable a comparative enrichment analysis strategy that can
provide valuable information about hit compounds in early stage drug
discovery.
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Affiliation(s)
| | | | - David R. Lancia
- FORMA Therapeutics Inc., 500 Arsenal Street, Suite 100, Watertown, Massachusetts 02472, United States
| | - Jared B. J. Milbank
- FORMA Therapeutics Inc., 500 Arsenal Street, Suite 100, Watertown, Massachusetts 02472, United States
| | - Christopher S. Kollmann
- FORMA Therapeutics Inc., 500 Arsenal Street, Suite 100, Watertown, Massachusetts 02472, United States
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