Literature DB >> 31386362

Autoencoder-Based Detection of Dynamic Allostery Triggered by Ligand Binding Based on Molecular Dynamics.

Yuko Tsuchiya1, Kei Taneishi2, Yasushige Yonezawa3.   

Abstract

Dynamic allostery on proteins, triggered by regulator binding or chemical modifications, transmits information from the binding site to distant regions, dramatically altering protein function. It is accompanied by subtle changes in side-chain conformations of the protein, indicating that the changes in dynamics, and not rigid or large conformational changes, are essential to understand regulation of protein function. Although a lot of experimental and theoretical studies have been dedicated to investigate this issue, the regulation mechanism of protein function is still being debated. Here, we propose an autoencoder-based method that can detect dynamic allostery. The method is based on the comparison of time fluctuations of protein structures, in the form of distance matrices, obtained from molecular dynamics simulations in ligand-bound and -unbound forms. Our method detected that the changes in dynamics by ligand binding in the PDZ2 domain led to the reorganization of correlative fluctuation motions among residue pairs, which revealed a different view of the correlated motions from the PCA and DCCM. In addition, other correlative motions were also found as a result of the dynamic perturbation from the ligand binding, which may lead to dynamic allostery. This autoencoder-based method would be usefully applied to the signal transduction and mutagenesis systems involved in protein functions and severe diseases.

Year:  2019        PMID: 31386362     DOI: 10.1021/acs.jcim.9b00426

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  10 in total

1.  Neural networks for protein structure and function prediction and dynamic analysis.

Authors:  Yuko Tsuchiya; Kentaro Tomii
Journal:  Biophys Rev       Date:  2020-03-12

2.  ivis Dimensionality Reduction Framework for Biomacromolecular Simulations.

Authors:  Hao Tian; Peng Tao
Journal:  J Chem Inf Model       Date:  2020-09-01       Impact factor: 4.956

3.  Overview of the big data bioinformatics symposium (2SCA) at BSJ2019.

Authors:  Tsuyoshi Shirai; Tohru Terada
Journal:  Biophys Rev       Date:  2020-02-14

4.  Allosteric control of ACE2 peptidase domain dynamics.

Authors:  Francesco Trozzi; Nischal Karki; Zilin Song; Niraj Verma; Elfi Kraka; Brian D Zoltowski; Peng Tao
Journal:  Org Biomol Chem       Date:  2022-05-04       Impact factor: 3.890

5.  Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities.

Authors:  Ikki Yasuda; Katsuhiro Endo; Eiji Yamamoto; Yoshinori Hirano; Kenji Yasuoka
Journal:  Commun Biol       Date:  2022-05-19

6.  Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets.

Authors:  Michael D Ward; Maxwell I Zimmerman; Artur Meller; Moses Chung; S J Swamidass; Gregory R Bowman
Journal:  Nat Commun       Date:  2021-05-21       Impact factor: 14.919

7.  Explore Protein Conformational Space With Variational Autoencoder.

Authors:  Hao Tian; Xi Jiang; Francesco Trozzi; Sian Xiao; Eric C Larson; Peng Tao
Journal:  Front Mol Biosci       Date:  2021-11-12

8.  CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.

Authors:  Leili Zhang; Giacomo Domeniconi; Ruhong Zhou; Guojing Cong; Chih-Chieh Yang; Seung-Gu Kang
Journal:  BMC Bioinformatics       Date:  2021-06-22       Impact factor: 3.169

9.  MDContactCom: a tool to identify differences of protein molecular dynamics from two MD simulation trajectories in terms of interresidue contacts.

Authors:  Chie Motono; Shunsuke Yanagida; Miwa Sato; Takatsugu Hirokawa
Journal:  Bioinformatics       Date:  2021-07-21       Impact factor: 6.937

Review 10.  Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning.

Authors:  Gennady M Verkhivker; Steve Agajanian; Guang Hu; Peng Tao
Journal:  Front Mol Biosci       Date:  2020-07-09
  10 in total

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