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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
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.
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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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