bims-lances 2024-12-22 papers

Nature. 2024 Dec 18.

The conformational space of RNase P RNA in solution.

Yun-Tzai Lee, Maximilia F S Degenhardt, Ilias Skeparnias, Hermann F Degenhardt, Yuba R Bhandari, Ping Yu, Jason R Stagno, Lixin Fan, Jinwei Zhang, Yun-Xing Wang.

RNA conformational diversity has fundamental biological roles1-5, but direct visualization of its full conformational space in solution has not been possible using traditional biophysical techniques. Using solution atomic force microscopy, a deep neural network and statistical analyses, we show that the ribonuclease P (RNase P) RNA adopts heterogeneous conformations consisting of a conformationally invariant core and highly flexible peripheral structural elements that sample a broad conformational space, with amplitudes as large as 20-60 Å in a multitude of directions, with very low net energy cost. Increasing Mg2+ drives compaction and enhances enzymatic activity, probably by narrowing the conformational space. Moreover, analyses of the correlations and anticorrelations between spatial flexibility and sequence conservation suggest that the functional roles of both the structure and dynamics of key regions are embedded in the primary sequence. These findings reveal the structure-dynamics basis for the embodiment of both enzymatic precision and substrate promiscuity in the RNA component of the RNase P. Mapping the conformational space of the RNase P RNA demonstrates a new general approach to studying RNA structure and dynamics.

DOI: https://doi.org/10.1038/s41586-024-08336-6

Int J Biol Macromol. 2024 Dec 12. pii: S0141-8130(24)09425-X. [Epub ahead of print]288 138614

Visualizing gaussian-chain like structural models of human α-synuclein in monomeric pre-fibrillar state: Solution SAXS data and modeling analysis.

Madhumita Dey, Arpit Gupta, Maulik D Badmalia, Ashish, Deepak Sharma.

Here, using small angle X-ray scattering (SAXS) data profile as reference, we attempted to visualize conformational ensemble accessible prefibrillar monomeric state of α-synuclein in solution. In agreement with previous reports, our analysis also confirmed that α-synuclein molecules adopted disordered shape profile under non-associating conditions. Chain-ensemble modeling protocol with dummy residues provided two weighted averaged clusters of semi-extended shapes. Further, Ensemble Optimization Method (EOM) computed mole fractions of semi-extended "twisted" conformations which might co-exist in solution. Since these were only Cα traces of the models, ALPHAFOLD2 server was used to search for all-atom models. Comparison with experimental data showed all predicted models disagreed equally, as individuals. Finally, we employed molecular dynamics simulations and normal mode analysis-based search coupled with SAXS data to seek better agreeing models. Overall, our analysis concludes that a shifting equilibrium of curved models with low α-helical content best-represents non-associating monomeric α-synuclein.

Keywords: ALPHAFOLD2; EOM; Monomer; Normal mode analysis; Protein structure; SAXS; α-Synuclein

DOI: https://doi.org/10.1016/j.ijbiomac.2024.138614

Annu Rev Biophys. 2024 Dec 17.

Protein Modeling with DEER Spectroscopy.

Maxx H Tessmer, Stefan Stoll.

Double electron-electron resonance (DEER) combined with site-directed spin labeling can provide distance distributions between selected protein residues to investigate protein structure and conformational heterogeneity. The utilization of the full quantitative information contained in DEER data requires effective protein and spin label modeling methods. Here, we review the application of DEER data to protein modeling. First, we discuss the significance of spin label modeling for accurate extraction of protein structural information and review the most popular label modeling methods. Next, we review several important aspects of protein modeling with DEER, including site selection, how DEER restraints are applied, common artifacts, and the unique potential of DEER data for modeling structural ensembles and conformational landscapes. Finally, we discuss common applications of protein modeling with DEER data and provide an outlook.

DOI: https://doi.org/10.1146/annurev-biophys-030524-013431