Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function enable RNA virus discovery
[en] The functions of RNA-dependent RNA polymerases (RdRps) in RNA viruses are demonstrably modulated by native substrates of dynamic and interconvertible conformational ensembles. Many of these are populated by essential flexible or intrinsically disordered regions (IDRs) that lack a stable three-dimensional (3D) structure and that make up nearly 16% of the conserved RdRp domains across Riboviria lineages. Typical structural models of RdRps are conversely generally agnostic of multiple conformations and their fluctuations, whether derived from protein structure predictors or from experimentally resolved structures from crystal states or dynamic conformer sets. In this review, we highlight how biophysics-inspired prediction tools combined with advanced deep learning algorithms, such as AlphaFold2 (AF2), can help efficiently infer the conformational heterogeneity and dynamics of RdRps. We discuss the use of AF2 for protein structure prediction, together with its limitations and impacts on RNA virus protein characterization, and specifically address its low-confidence prediction scores, which largely capture IDRs. Key examples illustrate how biophysical-encoded preferences of generic sequence–ensemble relationships can help estimate the global RdRp structural diversity and RNA virus discovery. The quantitative perception we present also highlights the challenging magnitude of the emergent sequence-to-conformations relationships of proteins and illustrates more robust and accurate annotations of novel or divergent RdRps. Finally, the coarse-grained IDR-based structural depiction of RdRp conformations offers concrete perspectives on an integrative framework to directly generate innovative avenues to better understand viral replication in the early disease stages and the protein–protein affinities through the folding dynamics of these viral proteins. Overall, tapping into the current knowledge of RdRp conformational heterogeneity will serve further RNA virus discovery as similarities in the global RdRp landscape emerge with more clarity.
Disciplines :
Biochemistry, biophysics & molecular biology
Author, co-author :
Tahzima, Rachid; Laboratory of Plant Pathology, TERRA, Gembloux Agro-BioTech, University of Liège (ULg), Gembloux, Belgium ; Department of Plant Sciences, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Ghent, Belgium ; Interuniversity Institute of Bioinformatics in Brussels (ULB/VUB), Brussels, Belgium ; Artificial Intelligence Lab, Vrije Universiteit Brussel (VUB), Brussels, Belgium
Charon, Justine; Fruit Biology and Pathology Unit, National Research Institute for Agriculture, Food and Environment (INRAE), University of Bordeaux, Bordeaux, France
Diaz, Adrian; Interuniversity Institute of Bioinformatics in Brussels (ULB/VUB), Brussels, Belgium
De Jonghe, Kris; Department of Plant Sciences, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Ghent, Belgium
Massart, Sébastien ; Université de Liège - ULiège > TERRA Research Centre > Gestion durable des bio-agresseurs
Michon, Thierry; Fruit Biology and Pathology Unit, National Research Institute for Agriculture, Food and Environment (INRAE), University of Bordeaux, Bordeaux, France
Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function enable RNA virus discovery
The author(s) declare financial support was received for the research and/or publication of this article. RT was funded by the Belgian Federal Public Service Health, Food Chain Safety and Environment through the contract GenoPREDICT (RF 22/635). JC and TM were supported by the ID4VISA grant from the French National Research Agency (ANR-21-CE35-0009). WV acknowledges the Research Foundation Flanders (FWO) G.0328.16N project grant and the International Research Infrastructure grant I000323N. Acknowledgments
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