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Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning meth...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546773/ https://www.ncbi.nlm.nih.gov/pubmed/35606422 http://dx.doi.org/10.1038/s41587-022-01307-0 |
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author | Rube, H. Tomas Rastogi, Chaitanya Feng, Siqian Kribelbauer, Judith F. Li, Allyson Becerra, Basheer Melo, Lucas A. N. Do, Bach Viet Li, Xiaoting Adam, Hammaad H. Shah, Neel H. Mann, Richard S. Bussemaker, Harmen J. |
author_facet | Rube, H. Tomas Rastogi, Chaitanya Feng, Siqian Kribelbauer, Judith F. Li, Allyson Becerra, Basheer Melo, Lucas A. N. Do, Bach Viet Li, Xiaoting Adam, Hammaad H. Shah, Neel H. Mann, Richard S. Bussemaker, Harmen J. |
author_sort | Rube, H. Tomas |
collection | PubMed |
description | Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called K(D)-seq, it determines the absolute affinity of protein–ligand interactions. We also apply ProBound to profile the kinetics of kinase–substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein–ligand interactions. |
format | Online Article Text |
id | pubmed-9546773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-95467732022-10-09 Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning Rube, H. Tomas Rastogi, Chaitanya Feng, Siqian Kribelbauer, Judith F. Li, Allyson Becerra, Basheer Melo, Lucas A. N. Do, Bach Viet Li, Xiaoting Adam, Hammaad H. Shah, Neel H. Mann, Richard S. Bussemaker, Harmen J. Nat Biotechnol Article Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called K(D)-seq, it determines the absolute affinity of protein–ligand interactions. We also apply ProBound to profile the kinetics of kinase–substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein–ligand interactions. Nature Publishing Group US 2022-05-23 2022 /pmc/articles/PMC9546773/ /pubmed/35606422 http://dx.doi.org/10.1038/s41587-022-01307-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Rube, H. Tomas Rastogi, Chaitanya Feng, Siqian Kribelbauer, Judith F. Li, Allyson Becerra, Basheer Melo, Lucas A. N. Do, Bach Viet Li, Xiaoting Adam, Hammaad H. Shah, Neel H. Mann, Richard S. Bussemaker, Harmen J. Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning |
title | Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning |
title_full | Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning |
title_fullStr | Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning |
title_full_unstemmed | Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning |
title_short | Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning |
title_sort | prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546773/ https://www.ncbi.nlm.nih.gov/pubmed/35606422 http://dx.doi.org/10.1038/s41587-022-01307-0 |
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