A mechano-geometric framework to characterize macromolecular ensembles

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Macromolecules like protein, RNA, and DNA perform their cellular function through dynamically changing their three-dimensional structure. Understanding the overall structural ensemble of such a molecule is crucial to reveal its key roles, and potentially intervene to restore lost function. While traditional Molecular Dynamics can provide atomically detailed trajectories, their computational cost is still tremendous, often limiting analysis to small spatial or temporal scales. Hence, lower accuracy, but high throughput methods are valuable to obtain first insights and guide more detailed, subsequent analysis focused on the area of interest. Robotics-inspired kinematic methods have been applied with great success to coarse-grained molecular modeling: their efficient nature allows for fast, yet reliable insights into molecular motion, and provide useful tools for data interpretation and integration. In this proposal, we aim to extend our existing kinematic molecular modeling software into a unified, mechano-geometric framework to study conformational ensembles of complex macromolecules. We will combine geometric constraint formulations of atomic interactions with rigidity theory to gain first-order insights into molecular flexibility. This formulation imposes an intrinsic hierarchy of motions onto the molecule, providing an efficient tool to broadly and uniformly sample conformation space and energy landscape in such an ultra-high dimensional environment. Large changes of dihedral degrees of freedom, violation of imposed constraints, or non-native interactions via steric or hydrophobic contact can report on allosteric hotspots important for drug targeting. Initial exciting results from studying the transitions of several proteins between their two major configurations has revealed interaction networks of amino acids that were in great agreement with published experimental data. Directly integrating experimental data will help validate and improve our computational algorithm, and allow for data-driven conformational exploration. As experimental data is often sparse and prone to errors, our mechano-geometric modeling framework will help predict inaccessible or unknown data and design subsequent, more focused experiments.We will concentrate on experimental data from traditional and multi-temperature crystallography, new-generation X-ray free electron laser experiments and double electron-electron resonance (DEER). We already have exciting data on several systems (Isocyanide Hydratase (ICH), an enzyme of the DJ-1 hyperfamily, and two members of the G-protein family, Gi and Gs), and compelling hypotheses for GPR126, an adhesion-type G-protein coupled receptor, providing a head-start for algorithm development and optimization. Coupling this approach to the fundamental experience in optimal control at the LTD, we aim to reveal potential driving forces that may guide and stabilize conformational transitions towards the end of the project.