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Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the motionless “lock‐and‐key” theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein traject...

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Autores principales: Wu, Fang, Jin, Shuting, Jiang, Yinghui, Jin, Xurui, Tang, Bowen, Niu, Zhangming, Liu, Xiangrong, Zhang, Qiang, Zeng, Xiangxiang, Li, Stan Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685463/
https://www.ncbi.nlm.nih.gov/pubmed/36202759
http://dx.doi.org/10.1002/advs.202203796
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author Wu, Fang
Jin, Shuting
Jiang, Yinghui
Jin, Xurui
Tang, Bowen
Niu, Zhangming
Liu, Xiangrong
Zhang, Qiang
Zeng, Xiangxiang
Li, Stan Z.
author_facet Wu, Fang
Jin, Shuting
Jiang, Yinghui
Jin, Xurui
Tang, Bowen
Niu, Zhangming
Liu, Xiangrong
Zhang, Qiang
Zeng, Xiangxiang
Li, Stan Z.
author_sort Wu, Fang
collection PubMed
description The latest biological findings observe that the motionless “lock‐and‐key” theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory‐related studies, thus hindering the possibility of supervised learning. A spatial‐temporal pre‐training method based on the modified equivariant graph matching networks, dubbed protmd which has two specially designed self‐supervised learning tasks: atom‐level prompt‐based denoising generative task and conformation‐level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The protmd can grant the encoder network the capacity to capture the time‐dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of protmd through linear detection and task‐specific fine‐tuning. A huge improvement from current state‐of‐the‐art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision‐recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.
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spelling pubmed-96854632022-11-25 Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding Wu, Fang Jin, Shuting Jiang, Yinghui Jin, Xurui Tang, Bowen Niu, Zhangming Liu, Xiangrong Zhang, Qiang Zeng, Xiangxiang Li, Stan Z. Adv Sci (Weinh) Research Articles The latest biological findings observe that the motionless “lock‐and‐key” theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory‐related studies, thus hindering the possibility of supervised learning. A spatial‐temporal pre‐training method based on the modified equivariant graph matching networks, dubbed protmd which has two specially designed self‐supervised learning tasks: atom‐level prompt‐based denoising generative task and conformation‐level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The protmd can grant the encoder network the capacity to capture the time‐dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of protmd through linear detection and task‐specific fine‐tuning. A huge improvement from current state‐of‐the‐art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision‐recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor. John Wiley and Sons Inc. 2022-10-06 /pmc/articles/PMC9685463/ /pubmed/36202759 http://dx.doi.org/10.1002/advs.202203796 Text en © 2022 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Wu, Fang
Jin, Shuting
Jiang, Yinghui
Jin, Xurui
Tang, Bowen
Niu, Zhangming
Liu, Xiangrong
Zhang, Qiang
Zeng, Xiangxiang
Li, Stan Z.
Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
title Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
title_full Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
title_fullStr Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
title_full_unstemmed Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
title_short Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
title_sort pre‐training of equivariant graph matching networks with conformation flexibility for drug binding
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685463/
https://www.ncbi.nlm.nih.gov/pubmed/36202759
http://dx.doi.org/10.1002/advs.202203796
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