Normal-mode driven exploration of protein domain motions
Yves-Henri Sanejouand

TL;DR
This paper introduces a novel two-step method combining normal mode analysis and distance geometry to accurately predict large protein conformational changes, leveraging low-frequency modes and the ROSETTA software.
Contribution
It presents a new approach that transforms normal mode-based conformer generation into a distance-geometry problem, enabling accurate modeling of large protein motions.
Findings
Successfully reconstructed six large amplitude conformational changes.
Low-dimensional normal coordinates suffice for near-native conformer generation.
Random exploration effectively finds low-energy conformers close to known states.
Abstract
Domain motions involved in the function of proteins can often be well described as a combination of motions along a handfull of low-frequency modes, that is, with the values of a few normal coordinates. This means that, when the functional motion of a protein is unknown, it should prove possible to predict it, since it amounts to guess a few values. However, without the help of additional experimental data, using normal coordinates for generating accurate conformers far away from the initial one is not so straightforward. To do so, a new approach is proposed: instead of building conformers directly with the values of a subset of normal coordinates, they are built in two steps, the conformer built with normal coordinates being just used for defining a set of distance constraints, the final conformer being built so as to match them. Note that this approach amounts to transform the problem…
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