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Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin

Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order t...

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Autores principales: Delfino, Francesco, Porozov, Yuri, Stepanov, Eugene, Tamazian, Gaik, Tozzini, Valentina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843051/
https://www.ncbi.nlm.nih.gov/pubmed/31750313
http://dx.doi.org/10.3389/fmolb.2019.00104
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author Delfino, Francesco
Porozov, Yuri
Stepanov, Eugene
Tamazian, Gaik
Tozzini, Valentina
author_facet Delfino, Francesco
Porozov, Yuri
Stepanov, Eugene
Tamazian, Gaik
Tozzini, Valentina
author_sort Delfino, Francesco
collection PubMed
description Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order to seek for the actual transition path for systems whose complexity is already high in the stable states. Here we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to Calmodulin, based on an empirical force field with a partial structural bias, to explore the transition paths between the apo-closed state and the Ca-bound open state of the protein. We then select representative structures along the trajectory based on a structural clustering algorithm and build a cleaned-up trajectory with them. We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. The comparison is performed both on the structural and energetic level, using the coarse-grained and the atomistic force fields upon reconstruction. Our analysis indicates that this method returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multi-stable proteins transition pathways.
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spelling pubmed-68430512019-11-20 Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin Delfino, Francesco Porozov, Yuri Stepanov, Eugene Tamazian, Gaik Tozzini, Valentina Front Mol Biosci Molecular Biosciences Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order to seek for the actual transition path for systems whose complexity is already high in the stable states. Here we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to Calmodulin, based on an empirical force field with a partial structural bias, to explore the transition paths between the apo-closed state and the Ca-bound open state of the protein. We then select representative structures along the trajectory based on a structural clustering algorithm and build a cleaned-up trajectory with them. We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. The comparison is performed both on the structural and energetic level, using the coarse-grained and the atomistic force fields upon reconstruction. Our analysis indicates that this method returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multi-stable proteins transition pathways. Frontiers Media S.A. 2019-10-15 /pmc/articles/PMC6843051/ /pubmed/31750313 http://dx.doi.org/10.3389/fmolb.2019.00104 Text en Copyright © 2019 Delfino, Porozov, Stepanov, Tamazian and Tozzini. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Delfino, Francesco
Porozov, Yuri
Stepanov, Eugene
Tamazian, Gaik
Tozzini, Valentina
Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin
title Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin
title_full Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin
title_fullStr Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin
title_full_unstemmed Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin
title_short Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin
title_sort structural transition states explored with minimalist coarse grained models: applications to calmodulin
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6843051/
https://www.ncbi.nlm.nih.gov/pubmed/31750313
http://dx.doi.org/10.3389/fmolb.2019.00104
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