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Poster De Conférence Année : 2022

Characterization of the conformational space of intrinsically disordered proteins

Résumé

The TALOS-N neural network has been developed [7] for inferring information about the backbone dihedral angles from NMR chemical shifts. The chemical shifts can be measured with an equal precision in folded proteins as well as in intrinsically disordered proteins. For folded proteins, TALOS-N was initially proposed in order to provide additional restraints for structure calculation. Nevertheless, the approach TAiBP was recently proposed [5, 6] to use the TALOS-N likelihood maps as distributions of the backbone angle values in the context of disordered protein regions. This was made possible thanks to the availability of a branch-and-prune algorithm, iBP [3], allowing a systematic enumeration of protein conformations. We are presenting here the results obtained by the TAiBP algorithm on two intrinsically disordered proteins described in the Protein Ensemble Database [4], along with an original finite mixture model allowing the determination of the relative populations of conformations from the TALOS-N likelihood maps. The populations obtained using this mixture model will be compared to those determined from SAXS measurements [2]. The use of conformations obtained from TAiBP along with the selection of the most populated conformations permit a low-resolution description of the conformational space of the studied proteins. References: [1] Gomes et al. Conformational Ensembles of an Intrinsically Disordered Protein Consistent with NMR, SAXS, and Single- Molecule FRET. J Am Chem Soc, 142:15697, 2020. [2] Köfinger et al. Efficient Ensemble Refinement by Reweighting. J Chem Theory Comput, 15:3390, 2019. [3] Lavor et al. The interval Branch-and-Prune algorithm for the discretizable molecular distance geometry problem with inexact distances. J Glob Optim, 56:855, 2013. [4] Lazar et al. PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins. Nucleic Acids Res, 49:D404, 2021. [5] Malliavin et al. Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach. J Chem Inf Model, 59:4486, 2019. [6] Malliavin. Tandem domain structure determination based on a systematic enumeration of conformations. Sci Rep, 11:16925, 2021. [7] Shen and Bax. Protein structural information derived from NMR chemical shift with the neural network program TALOS-N. Methods Mol Biol, 1260:17, 2015.
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hal-04545776 , version 1 (14-04-2024)

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  • HAL Id : hal-04545776 , version 1

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Georg Daniel Förster, Jérôme Idier, Leo Liberti, Antonio Mucherino, Jung-Hsin Lin, et al.. Characterization of the conformational space of intrinsically disordered proteins. XIIth International Conference NMR: a tool for biology, May 2022, Paris, France. ⟨hal-04545776⟩
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