Scalable rule-based modelling of allosteric proteins and biochemical networks.

Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds....

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Autores principales:	Julien F Ollivier, Vahid Shahrezaei, Peter S Swain
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6cb68f305e13429885e5414b4864b9c4
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spelling	oai:doaj.org-article:6cb68f305e13429885e5414b4864b9c42021-11-18T05:50:52ZScalable rule-based modelling of allosteric proteins and biochemical networks.1553-734X1553-735810.1371/journal.pcbi.1000975https://doaj.org/article/6cb68f305e13429885e5414b4864b9c42010-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21079669/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.Julien F OllivierVahid ShahrezaeiPeter S SwainPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 11, p e1000975 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Julien F Ollivier Vahid Shahrezaei Peter S Swain Scalable rule-based modelling of allosteric proteins and biochemical networks.
description	Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.
format	article
author	Julien F Ollivier Vahid Shahrezaei Peter S Swain
author_facet	Julien F Ollivier Vahid Shahrezaei Peter S Swain
author_sort	Julien F Ollivier
title	Scalable rule-based modelling of allosteric proteins and biochemical networks.
title_short	Scalable rule-based modelling of allosteric proteins and biochemical networks.
title_full	Scalable rule-based modelling of allosteric proteins and biochemical networks.
title_fullStr	Scalable rule-based modelling of allosteric proteins and biochemical networks.
title_full_unstemmed	Scalable rule-based modelling of allosteric proteins and biochemical networks.
title_sort	scalable rule-based modelling of allosteric proteins and biochemical networks.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/6cb68f305e13429885e5414b4864b9c4
work_keys_str_mv	AT julienfollivier scalablerulebasedmodellingofallostericproteinsandbiochemicalnetworks AT vahidshahrezaei scalablerulebasedmodellingofallostericproteinsandbiochemicalnetworks AT petersswain scalablerulebasedmodellingofallostericproteinsandbiochemicalnetworks
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Scalable rule-based modelling of allosteric proteins and biochemical networks.

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