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Building Block-Based Binding Predictions for DNA-Encoded Libraries
[Image: see text] 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,...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466377/ https://www.ncbi.nlm.nih.gov/pubmed/37578123 http://dx.doi.org/10.1021/acs.jcim.3c00588 |
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author | Zhang, Chris Pitman, Mary Dixit, Anjali Leelananda, Sumudu Palacci, Henri Lawler, Meghan Belyanskaya, Svetlana Grady, LaShadric Franklin, Joe Tilmans, Nicolas Mobley, David L. |
author_facet | Zhang, Chris Pitman, Mary Dixit, Anjali Leelananda, Sumudu Palacci, Henri Lawler, Meghan Belyanskaya, Svetlana Grady, LaShadric Franklin, Joe Tilmans, Nicolas Mobley, David L. |
author_sort | Zhang, Chris |
collection | PubMed |
description | [Image: see text] 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. |
format | Online Article Text |
id | pubmed-10466377 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-104663772023-08-31 Building Block-Based Binding Predictions for DNA-Encoded Libraries Zhang, Chris Pitman, Mary Dixit, Anjali Leelananda, Sumudu Palacci, Henri Lawler, Meghan Belyanskaya, Svetlana Grady, LaShadric Franklin, Joe Tilmans, Nicolas Mobley, David L. J Chem Inf Model [Image: see text] 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. American Chemical Society 2023-08-14 /pmc/articles/PMC10466377/ /pubmed/37578123 http://dx.doi.org/10.1021/acs.jcim.3c00588 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Zhang, Chris Pitman, Mary Dixit, Anjali Leelananda, Sumudu Palacci, Henri Lawler, Meghan Belyanskaya, Svetlana Grady, LaShadric Franklin, Joe Tilmans, Nicolas Mobley, David L. Building Block-Based Binding Predictions for DNA-Encoded Libraries |
title | Building Block-Based
Binding Predictions for DNA-Encoded
Libraries |
title_full | Building Block-Based
Binding Predictions for DNA-Encoded
Libraries |
title_fullStr | Building Block-Based
Binding Predictions for DNA-Encoded
Libraries |
title_full_unstemmed | Building Block-Based
Binding Predictions for DNA-Encoded
Libraries |
title_short | Building Block-Based
Binding Predictions for DNA-Encoded
Libraries |
title_sort | building block-based
binding predictions for dna-encoded
libraries |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466377/ https://www.ncbi.nlm.nih.gov/pubmed/37578123 http://dx.doi.org/10.1021/acs.jcim.3c00588 |
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