Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated C$\alpha$-C$\alpha$ distance restraints and sparse exper…(more)
%0 Journal Article
%1 Sala2022-rj
%A Sala, Davide
%A Del Alamo, Diego
%A Mchaourab, Hassane S
%A Meiler, Jens
%D 2022
%I Elsevier BV
%J Structure
%K topic_lifescience EPR Rosetta; biology; changes; conformational dynamics; integrative modeling; molecular protein refinement spectroscopy; structural structure
%N 8
%P 1157--1168.e3
%T Modeling of protein conformational changes with Rosetta guided by limited experimental data
%V 30
%X Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated C$\alpha$-C$\alpha$ distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.
@article{Sala2022-rj,
abstract = {Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated C$\alpha$-C$\alpha$ distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.},
added-at = {2024-09-10T11:54:51.000+0200},
author = {Sala, Davide and Del Alamo, Diego and Mchaourab, Hassane S and Meiler, Jens},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/25716ec58190268b56498b91f785c3c9c/scadsfct},
interhash = {9fbf6e33c7d3fca026608c8e8797db74},
intrahash = {5716ec58190268b56498b91f785c3c9c},
journal = {Structure},
keywords = {topic_lifescience EPR Rosetta; biology; changes; conformational dynamics; integrative modeling; molecular protein refinement spectroscopy; structural structure},
language = {en},
month = aug,
number = 8,
pages = {1157--1168.e3},
publisher = {Elsevier BV},
timestamp = {2024-11-28T17:41:25.000+0100},
title = {Modeling of protein conformational changes with Rosetta guided by limited experimental data},
volume = 30,
year = 2022
}
