Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling

Degiacomi, M.T.; Dal Peraro, M.

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.

Citation

Degiacomi, M., & Dal Peraro, M. (2013). Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling. Structure, 21(7), 1097-1106. https://doi.org/10.1016/j.str.2013.05.014

• Journal Article Type: Article
• Acceptance Date: May 22, 2013
• Online Publication Date: Jun 27, 2013
• Publication Date: Jul 2, 2013
• Deposit Date: Jul 26, 2017
• Publicly Available Date: Aug 8, 2017
• Journal: Structure
• Print ISSN: 0969-2126
• Electronic ISSN: 1878-4186
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 7
• Pages: 1097-1106
• DOI: https://doi.org/10.1016/j.str.2013.05.014
• Public URL: https://durham-repository.worktribe.com/output/1353612