TY - JOUR
T1 - Machine learning and network analysis of molecular dynamics trajectories reveal two chains of red/ox-specific residue interactions in human protein disulfide isomerase
AU - Karamzadeh, Razieh
AU - Karimi-Jafari, Mohammad Hossein
AU - Sharifi-Zarchi, Ali
AU - Chitsaz, Hamidreza
AU - Salekdeh, Ghasem Hosseini
AU - Moosavi-Movahedi, Ali Akbar
N1 - Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.
AB - The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.
UR - http://www.scopus.com/inward/record.url?scp=85020897876&partnerID=8YFLogxK
U2 - 10.1038/s41598-017-03966-5
DO - 10.1038/s41598-017-03966-5
M3 - Article
C2 - 28623339
AN - SCOPUS:85020897876
VL - 7
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
M1 - 3666
ER -