HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps

In light of the recent very rapid progress in protein structure prediction, accessing multitude functional 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 cryogenic electron microscopy can catch relatively few states. Many others only accessible under physiological conditions solution. Therefore, there a pressing need to fill this gap with computational approaches. We present HOPMA, novel method predict transitions using modified elastic network model. The exploits patterns contact map, taking its 3D input, excludes some disconnected patches from network. Combined nonlinear normal mode analysis, strategy boosts conformational space exploration, especially when input highly constrained, we demonstrate on set 400 transitions. Our results let us envision discovery new conformations, which were unreachable previously, starting experimentally known structures. The computationally efficient available at https://github.com/elolaine/HOPMA https://team.inria.fr/nano-d/software/nolb-normal-modes.