Barcode-free hit discovery from massive libraries enabled by automated small molecule structure annotation
Authors/Creators
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van der Nol, Edith
(Data collector)1
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Haupt, Nils Alexander
(Data collector)2
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Gao, Qing Qing
(Data collector)1
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Smit, Benthe A.M.
(Data collector)1
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Hoffmann, Martin Andre
(Data collector)3
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Engler-Lukajewski, Martin
(Data collector)3
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Ludwig, Marcus
(Data collector)3
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McKenna, Sean
(Data collector)1
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Mata, J. Miguel
(Data collector)1
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Béquignon, Olivier J. M.
(Data collector)1
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van Westen, Gerard
(Data collector)1
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Wendel, Tiemen J.
(Data collector)4
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Noordermeer, Sylvie M.
(Data collector)4
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Pomplun, Sebastian
(Contact person)1
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Böcker, Sebastian
(Contact person)2
Description
The discovery of high affinity ligands is crucial for virtually any drug discovery campaign. Affinity-selection platforms are powerful tools for rapid early hit discovery. Among these, DNA-encoded libraries (DELs) have found widespread application for the identification of small molecule ligands. However, the DNA barcodes required for the decoding of hit-compounds bring challenges in terms of synthesis complexity and reaction scope, and can also influence binding by interacting with the target. We present a self-encoded library (SEL) platform that simultaneously screens up to a million druglike small molecules without barcoding tags. We developed computational methods to automatically decode each query MS/MS spectrum of a selection outcome, matching it against the molecular structures of the library. This combinatorial mass encoding decoding tool (COMET) succeeded in correctly annotating spectra from a wide variety of chemical structures, enabling the screening of druglike libraries with diverse molecular architectures. We developed robust library synthesis approaches with a variety of chemical transformations and validated our platform in affinity-selections, identifying multiple nanomolar binders for the oncologic target carbonic anhydrase IX. Our SEL platform also succeeded in selecting potent inhibitors for the flap endonuclease 1 (FEN1), a promising drug target for synthetic lethality based cancer treatment. As DNA processing enzymes cannot be addressed with DELs, this example highlights the innovative potential of our SEL platform. Screening barcode-free libraries of this scale is unprecedented and promises substantial impact on both academic and industrial early drug discovery.
Files
Annotated_Spectra.zip
Files
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Additional details
Software
- Repository URL
- https://github.com/sirius-ms/comet