Biophysical JournalBiophysicsPeer-reviewed journal article
Advanced coarse-grained model for fast simulation of nascent polypeptide chain dynamics within the ribosome
Shiqi Yu, Artem KushneriD, Ella Teasell, Wenjun ZhaoiD, Simcha SrebnikiD, Khanh Dao DuciD
Abstract
The nascent polypeptide exit tunnel (NPET) is a sub-compartment of the ribosome that constrains the dynamics of nascent polypeptide chains during protein translation. Simulating these dynamics has been limited due by the spatial scale of the ribosome and the temporal scale of elongation. Here, we present an automated pipeline to extract the geometry of the NPET and the ribosome surface at high resolution from any ribosome structure. We further convert this into a coarse-grained (CG) bead model that can be used in molecular simulations. This CG model more accurately captures NPET geometry than previous representations and allows for the simulation of co- and post-translational processes that are computationally prohibitive with all-atom approaches. In particular, we illustrate how the CG model may be used to simulate the elongation dynamics of the nascent polypeptide and its escape post-translation, as well as evaluate free energy landscapes and examine the influence of electrostatics on the nascent polypeptide escape. SIGNIFICANCEThe translation of nascent polypeptide chains is mediated by the ribosome, with interactions between the protein and the nascent polypeptide exit tunnel (NPET) impacting the process. However, modeling and simulating protein elongation and its escape from the NPET remain challenging due to computational limitations in spatial and temporal resolution. Here, we develop a computational pipeline for generating coarse-grained (CG) models of the NPET and ribosome surface from any ribosome structure that allow for both effective and accurate computer simulations. We demonstrate how the model can be implemented for various simulations, including the elongation dynamics of the nascent polypeptide and escape of the chain post-translation, as well as in estimating free energy landscapes and examining the impact of the charged environment on the escape time.
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
- 1
- FWCI
- 0.30
- Field percentile
- top 47%
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