HOPMA: Boosting protein functional dynamics with colored contact maps

Abstract

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.

Why are large conformational changes well described by harmonic normal modes?

“…This field has now established a fruitful dialog with novel experimental techniques (26) and is contributing to the goal of structural ensemble determination (27), benefiting from innovative techniques such as cryo-electron microscopy (cryo-EM) tightly coupled with computational modeling (28). Applications of ENM in this context include high resolution modeling of cryo-EM structures (28), protein-protein docking (29), protein-ligand docking at allosteric ligand binding sites (30), and extensive and fast exploration of the conformational space to discover new potentially functional sites (31).…”

Pepsi-SAXS/SANS – small-angle scattering guided tools for integrative structural bioinformatics

“…NMA determines low-frequency motions at a very low computational cost and these are particularly interesting to the structural biology community because they give insight into protein function and dynamics. On our side, we have proposed a computationally efficient nonlinear NMA method that can be applied to largest complexes from the Protein Data Bank (PDB), and which also very well preserves local stereochemistry [3][4][5].…”

“…Recently, we have shown that it can be extended to model local deformations and to better preserve the stereochemistry of the protein. We have developed a computationally efficient nonlinear NMA method that can be applied to the largest complexes from the Protein Data Bank (PDB) [3][4][5].…”