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bioRxiv · 10.1101/2023.07.24.550386

Data-driven probabilistic definition of the low energy conformational states of protein residues

Gavalda-Garcia, J.·Bickel, D.·Roca-Martinez, J.·Raimondi, D.·Orlando, G.·Vranken, W. F.
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Abstract

Protein dynamics and related conformational changes are essential for their function but difficult to characterise and interpret. Amino acids in a protein behave according to their local energy landscape, which is determined by their local structural context and environmental conditions. The lowest energy state for a given residue can correspond to sharply defined conformations, e.g., in a stable helix, or can cover a wide range of conformations, e.g., in intrinsically disordered regions. A good definition of such low energy states is therefore important to describe the behavior of a residue and how it changes with its environment. We propose a data-driven probabilistic definition of six low energy conformational states typically accessible for amino acid residues in proteins. This definition is based on solution NMR information of 1,322 proteins through a combined analysis of structure ensembles with interpreted chemical shifts. We further introduce a conformational state variability parameter that captures, based on an ensemble of protein structures from molecular dynamics or other methods, how often a residue moves between these conformational states. The approach enables a different perspective on the local conformational behavior of proteins that is complementary to their static interpretation from single structure models.

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Gavalda-Garcia, J., Bickel, D., Roca-Martinez, J., Raimondi, D., Orlando, G., Vranken, W. F.. 2023-07-26. Data-driven probabilistic definition of the low energy conformational states of protein residues. https://doi.org/10.1101/2023.07.24.550386

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