Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling.

Degiacomi, M.T. and Dal Peraro, M. (2013) 'Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling.', Structure., 21 (7). pp. 1097-1106.

Abstract

Proteins often assemble in multimeric complexes to perform a specific biologic function. However, trapping these high-order conformations is difficult experimentally. Therefore, predicting how proteins assemble using in silico techniques can be of great help. The size of the associated conformational space and the fact that proteins are intrinsically flexible structures make this optimization problem extremely challenging. Nonetheless, known experimental spatial restraints can guide the search process, contributing to model biologically relevant states. We present here a swarm intelligence optimization protocol able to predict the arrangement of protein symmetric assemblies by exploiting a limited amount of experimental restraints and steric interactions. Importantly, within this scheme the native flexibility of each protein subunit is taken into account as extracted from molecular dynamics (MD) simulations. We show that this is a key ingredient for the prediction of biologically functional assemblies when, upon oligomerization, subunits explore activated states undergoing significant conformational changes.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
Download PDF
(12253Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.str.2013.05.014
Publisher statement:© 2013 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Date accepted:22 May 2013
Date deposited:08 August 2017
Date of first online publication:27 June 2013
Date first made open access:No date available

Save or Share this output

Export:
Export
Look up in GoogleScholar