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Protein-ligand binding affinity prediction based on profiles of intermolecular contacts
As a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and inte...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8902473/ https://www.ncbi.nlm.nih.gov/pubmed/35317230 http://dx.doi.org/10.1016/j.csbj.2022.02.004 |
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author | Wang, Debby D. Chan, Moon-Tong |
author_facet | Wang, Debby D. Chan, Moon-Tong |
author_sort | Wang, Debby D. |
collection | PubMed |
description | As a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and interpretable BAP remains a difficult problem in this field. Herein, we introduce the profiles of intermolecular contacts (IMCPs) as descriptors for machine-learning-based BAP. IMCPs describe each group of protein-ligand contacts by the count and average distance of the group members, and collaborate closely with classical machine-learning models. Performed on multiple validation sets, IMCP-based models often result in better BAP accuracy than those originating from other similar descriptors. Additionally, IMCPs are simple and concise, and easy to interpret in model training. These descriptors highly conclude the structural information of protein-ligand complexes and can be easily updated with personalized profile features. IMCPs have been implemented in the BAP Toolkit on github ( https://github.com/debbydanwang/BAP). |
format | Online Article Text |
id | pubmed-8902473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-89024732022-03-21 Protein-ligand binding affinity prediction based on profiles of intermolecular contacts Wang, Debby D. Chan, Moon-Tong Comput Struct Biotechnol J Research Article As a key element in structure-based drug design, binding affinity prediction (BAP) for putative protein-ligand complexes can be efficiently achieved by the incorporation of structural descriptors and machine-learning models. However, developing concise descriptors that will lead to accurate and interpretable BAP remains a difficult problem in this field. Herein, we introduce the profiles of intermolecular contacts (IMCPs) as descriptors for machine-learning-based BAP. IMCPs describe each group of protein-ligand contacts by the count and average distance of the group members, and collaborate closely with classical machine-learning models. Performed on multiple validation sets, IMCP-based models often result in better BAP accuracy than those originating from other similar descriptors. Additionally, IMCPs are simple and concise, and easy to interpret in model training. These descriptors highly conclude the structural information of protein-ligand complexes and can be easily updated with personalized profile features. IMCPs have been implemented in the BAP Toolkit on github ( https://github.com/debbydanwang/BAP). Research Network of Computational and Structural Biotechnology 2022-02-28 /pmc/articles/PMC8902473/ /pubmed/35317230 http://dx.doi.org/10.1016/j.csbj.2022.02.004 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Wang, Debby D. Chan, Moon-Tong Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
title | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
title_full | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
title_fullStr | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
title_full_unstemmed | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
title_short | Protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
title_sort | protein-ligand binding affinity prediction based on profiles of intermolecular contacts |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8902473/ https://www.ncbi.nlm.nih.gov/pubmed/35317230 http://dx.doi.org/10.1016/j.csbj.2022.02.004 |
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