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PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein varia...

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Autores principales: Ammar, Ammar, Cavill, Rachel, Evelo, Chris, Willighagen, Egon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983232/
https://www.ncbi.nlm.nih.gov/pubmed/36864534
http://dx.doi.org/10.1186/s13321-023-00701-3
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author Ammar, Ammar
Cavill, Rachel
Evelo, Chris
Willighagen, Egon
author_facet Ammar, Ammar
Cavill, Rachel
Evelo, Chris
Willighagen, Egon
author_sort Ammar, Ammar
collection PubMed
description Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol(-1) on an independent test set with an R(2) value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00701-3.
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spelling pubmed-99832322023-03-04 PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity Ammar, Ammar Cavill, Rachel Evelo, Chris Willighagen, Egon J Cheminform Research Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol(-1) on an independent test set with an R(2) value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00701-3. Springer International Publishing 2023-03-02 /pmc/articles/PMC9983232/ /pubmed/36864534 http://dx.doi.org/10.1186/s13321-023-00701-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ammar, Ammar
Cavill, Rachel
Evelo, Chris
Willighagen, Egon
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
title PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
title_full PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
title_fullStr PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
title_full_unstemmed PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
title_short PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
title_sort psnpbind-ml: predicting the effect of binding site mutations on protein-ligand binding affinity
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983232/
https://www.ncbi.nlm.nih.gov/pubmed/36864534
http://dx.doi.org/10.1186/s13321-023-00701-3
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