PT  - JOURNAL ARTICLE
AU  - Elodie Laine
AU  - Sergei Grudinin
TI  - HOPMA: Boosting protein functional dynamics with colored contact maps
AID  - 10.1101/2020.12.31.424963
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.12.31.424963
4099  - http://biorxiv.org/content/early/2021/01/04/2020.12.31.424963.short
4100  - http://biorxiv.org/content/early/2021/01/04/2020.12.31.424963.full
AB  - In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches.We present HOPMA, a novel method to predict protein functional states and transitions using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures.The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.Competing Interest StatementThe authors have declared no competing interest.