bioRxivBiochemistry
Integrative solution structure of a PTBP1-viral IRES complex reveals strong compaction and ordering with residual conformational flexibility
G. Dorn, C. Gmeiner, T. de Vries, E. Dedic, M. Novakovic, F. F. Damberger, C. Maris, E. Finol, C. P. Sarnowski, J. Kolhbrecher, T. J. Welsh, S. Bolisetty, R. Mezzenga, R. Aebersold, A. Leitner, M. Yulikov, G. Jeschke, F. H.- T. Allain
Abstract
RNA-binding proteins (RBPs) are crucial regulators of gene expression and often comprise well-defined domains interspersed by flexible, intrinsically disordered regions. The structure determination of ribonucleoprotein complexes involving such RBPs is not common practice and requires integrative structural modeling approaches due to the fact that they often do not form a single stable globular state. Here, we integrate data from magnetic resonance, mass spectrometry, and small angle scattering to determine the solution structure of the polypyrimidine-tract binding protein 1 (PTBP1 also called hnRNP I) bound to an RNA which is part of the internal ribosome entry site (IRES) of the encephalomyocarditis virus (EMCV). PTBP1 binding to this IRES element enhances translation of the viral RNA. The determined structural ensemble reveals that both RNA and protein experience a strong compaction upon complex formation, get ordered but still maintain a substantial conformational flexibility. The C-terminal RNA recognition motif (RRM4) of PTBP1 rigidifies the complex by binding a single-strand RNA linker and, in turn, is essential for IRES-mediated translation. PTBP1 acts as an RNA chaperone for the IRES, by ordering the RNA into a few discrete conformations that expose the RNA stems to the outer surface of the RNP complex for subsequent interactions with the translation machinery. The conformational diversity within this structural ensemble is likely common among RNP complexes and important for their functionality. The presented approach opens the possibility to characterize heterogeneous RNP structures at atomic level.
- assay: structural biology
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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