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

Rachid TAHZIMA^1,2*Justine Charon³Adrian Diaz⁴Kris De Jonghe¹Thierry Michon³Sebastien Massart⁵Wim Vranken^4,6,7,8

• ¹Institute for Agricultural, Fisheries and Food Research (ILVO), Merelbeke, Belgium
• ²Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Namur, Belgium
• ³Fruit Biology and Pathology Unit, National Research Institute for Agriculture, Food and Environment (INRAE)/University of Bordeaux, Bordeaux, France
• ⁴Interuniversity Institute of Bioinformatics in Brussels (ULB/VUB), Brussels, Belgium
• ⁵Laboratory for Integrated and Urban Phytopathology, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Namur, Belgium
• ⁶Artificial Intelligence Laboratory, Vrije Universiteit Brussel, Brussels, Brussels, Belgium
• ⁷Division of Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Brussels, Brussels, Belgium
• ⁸Chemistry department, Vrije Universiteit Brussel (VUB), 1050, Brussels, Belgium., Brussels, Belgium

RNA-dependent RNA polymerase (RdRp) functions 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 (or IDRs) that lack stable 3D structure and make up nearly 16% of conserved RdRp domains, across Riboviria lineages. Typical structural models of RdRps are conversely typically 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 RdRp conformational heterogeneity and dynamics. We discuss the use of AF2 for protein structure prediction together with its limitations and impacts on RNA virus protein characterization and specifically address AF2’s low-confidence prediction scores, which largely capture IDRs. Key examples illustrate how biophysical-encoded preferences of generic sequence ensembles relationships can help estimate the global RdRp structural diversity as well as RNA virus discovery. The quantitative perception we present also highlights the challenging magnitude of 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 early disease stages and protein-protein affinities through 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.