# Structural biology of single-stranded, positive-sense RNA viruses in the age of accurate atomic-scale predictions of protein structures

Stéphane Bressanelli, Sonia Fieulaine, Thibault Tubiana

► To cite this version:

Stéphane Bressanelli, Sonia Fieulaine, Thibault Tubiana. Structural biology of single-stranded, positive-sense RNA viruses in the age of accurate atomic-scale predictions of protein structures. Virology, 2025, Part of special issue 70th Anniversary Special Issue Edited by Dr. Jasmine Tomar (Elsevier B.V., Amsterdam, Netherlands), 608, pp.110546. 10.1016/j.virol.2025.110546. hal-05353138

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## Invited Review

# Structural biology of single-stranded, positive-sense RNA viruses in the age of accurate atomic-scale predictions of protein structures

Stéphane Bressanelli* ID, Sonia Fieulaine**, Thibault Tubiana***

Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, 91198, France

<figure><img src="image_3.png" /></figure>

### ARTICLE INFO

**Keywords:**

Single-stranded positive-sense RNA viruses

Replication

AlphaFold

Hepatitis E virus

Norovirus

Hepatitis C virus

### ABSTRACT

For decades atomic structures of proteins could only be determined experimentally and at a very slow pace. This was a particular problem for RNA viruses, for which sequences diverge fast and horizontal transfers are common. This made modeling from known structures difficult and uncertain. Only hard experimental structural data could allow accurate atomic descriptions of viral proteins and subsequent analyses, from mutant phenotype prediction to drug design. This has changed. With the advent of AlphaFold, that allows accurate protein structure prediction from sequence only, it is now possible in most cases to have the structure of a new protein of interest in a matter of minutes. In this mini review we focus on important consequences of this new state of affairs. While most of our conclusions are likely relevant to RNA viruses in general, here we focus on single-stranded, positive-sense RNA viruses. Taking as case studies proteins that are studied in our lab, we highlight why these viruses generally encode proteins that are particularly tough cases, being membrane-associated proteins with alternate conformations, structures, and interactions that may not be conserved even between close relatives. For these proteins AlphaFold may even fail or at least mislead, but with a proper approach it may also allow jump-starting the study of difficult or understudied viruses.

## 1. Introduction

### 1.1. Single-stranded, positive-sense RNA viruses: small, fast, modular, membrane-bound

Viruses with single-stranded RNA genomes of positive (mRNA) polarity (hereafter RNA(+) viruses), like all RNA viruses, display small genomes (3–32 kbases) and fast sequence divergence. These two features are both linked to the error-prone nature of their replication complexes, as beautifully illustrated by the RNA(+) viruses with the largest genomes, namely the animal viruses coronaviruses, toroviruses, and roniviruses (Gorbalenya et al., 2006). Fast sequence divergence may lead to substantial genetic diversity already in a single species such as hepatitis C virus (HCV) (Smith et al., 2014). When it comes to comparing different genera of RNA(+) viruses, the rule is that RNA and protein sequences have diverged beyond similarity recognition in most parts of the genome, making e.g. protein domains difficult to discern without

special expertise (Kelly et al., 2016; Koonin et al., 1992). The case is made all the more complex because of the well-known capability of RNA (+) viruses to acquire genes, or even sets of genes, from their hosts or from co-infecting viruses. This makes their evolution essentially modular down to the most primordial level (Koonin et al., 2015). In conjunction with fast sequence divergence, this raised a conundrum when two viral proteins had very dissimilar sequences but were coded in the same place of the genomes of two related viruses. Then, it was difficult to ascertain whether this meant that the proteins had diverged beyond sequence similarity recognition, that the proteins were unrelated, or a complex mixture of the two (Kuchibhatla et al., 2014).

RNA(+) viruses replicate their RNAs in endomembrane-bound replication compartments and dedicate a significant proportion of their small genomes to encoding proteins that direct membrane rearrangements necessary for the formation of these compartments. The difficulty of comparing such proteins is compounded by the fact that physico-chemical properties rather than amino acid sequences tend to

This article is part of a special issue entitled: 70th Anniversary Special Issue published in Virology.

E-mail addresses: stephane.bressanelli@i2bc.paris-saclay.fr (S. Bressanelli), sonia.fieulaine@i2bc.paris-saclay.fr (S. Fieulaine), thibault.tubiana@i2bc.paris-saclay.fr (T. Tubiana).

https://doi.org/10.1016/j.virol.2025.110546

Received 8 February 2025; Received in revised form 2 April 2025; Accepted 15 April 2025

Available online 24 April 2025

0042-6822/© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>*** Corresponding author.</footnote>
be conserved in them, e.g. amphipathicity or hydrophobicity for membrane-interacting regions (Ahola and Karlin, 2015).

## 1.2. Structural biology for RNA(+) viruses' proteins B.A. (Before AlphaFold): insightful but labor-intensive and slow

Structural biology, particularly determination of the atomic structures of viral proteins, has long been known to allow recognition of homology in highly divergent proteins, as protein structures evolve much more slowly than sequences (Chothia and Lesk, 1986). For instance, only the structures of the envelope glycoprotein E of flaviviruses (Rey et al., 1995) and E1 of alphaviruses (Lescar et al., 2001) could establish that the two are homologous, as sequences had diverged so much that even cysteines forming disulfide bridges were no longer conserved. Still, attempts to extend this insight to other genera in the *Flaviviridae* family (Krey et al., 2010) turned out to be misguided. The conundrum was that it was necessary to produce experimental atomic structures to ascertain this (El Omari et al., 2013; Kong et al., 2013). However, experimental determination of atomic structures of proteins is a slow and often labor-intensive process, and it is out of the question to produce more than a handful despite the progresses made in throughput for instance through so-called 'Structural Genomics' initiatives (Chandonia and Brenner, 2006). This state of affair, together with limited accuracy of homology modeling with highly divergent sequences and very limited accuracy of structure modeling without a homology model (so-called 'template-free' modeling), made structural virology of RNA(+) viruses limited in scope up to 2020.

## 1.3. Structural biology of proteins A.A. (After AlphaFold): Structural Genomics come true

The situation described above has now been completely changed and even reversed, with the advent of accurate atomic structure prediction of proteins from sequence alone. It is not the purpose of this review to describe this major breakthrough, heralded by the results of AlphaFold2 (AF2) at the Critical Assessment of Structure Prediction #14 in 2020 (Jumper et al., 2021). Suffice to say that in most cases it is now possible to have, in a matter of minutes, a protein structure, predicted from its sequence alone, with an accuracy rivaling experimental structure determination (Akdel et al., 2022). The ready availability of such software, for AF2 through Colabfold (Mirdita et al., 2022), has brought on a revolution in practice in structural biology. It is now customary to interleave AF2 predictions and experimental structural biology (Terwilliger et al., 2024). Several works have taken advantage of these opportunities to explore the structures of viral proteins. The goals could be to make available as many viral protein structural models as possible (Kim et al., 2025; Nomburg et al., 2024), to raise new data-driven hypotheses on some viral process or clade (Nomburg et al., 2024; Soh et al., 2024), or to focus on a specific, previously intractable question in virology (Oliver et al., 2023). The latter work is a particularly noteworthy illustration of the radical change in structural virology, as it convincingly establishes in a single paper the architecture of the above-mentioned non-flavivirus *Flaviviridae* envelope glycoproteins (Oliver et al., 2023). In this mini review we want to stress several features of the new situation that are especially relevant to structural biology of RNA(+) viruses. Particularly, we would like to highlight specific pitfalls that can arise when modeling 3D structures of RNA(+) viruses' complexes.

# 2. Application of AF2 to RNA(+) viruses

## 2.1. Hepatitis E virus as a case study

Hepatitis E virus (HEV) is an excellent case study of the opportunities afforded by the new capabilities for atomic structure predictions. It is an RNA(+) virus whose 7.2 kb genome encodes three ORFs. ORF1 is

translated from the genomic RNA and comprises the replicase proteins, while ORF2 and ORF3 are translated from a subgenomic RNA. Molecular studies on HEV have increased in recent years, in part because of concerns for the zoonotic potential of *Hepeviridae* (Zahmanova et al., 2023). However, structural biology of HEV was lagging behind.

## 2.2. HEV pORF1: accurate breakdown and atomic structures of domains in the absence of close homologs of known structure

Indeed, a groundbreaking sequence comparison study tentatively assigned functional domains with fuzzy boundaries to pORF1 (Koonin et al., 1992), a breakdown that was later updated (Kelly et al., 2016) but is still the basis of pORF1 molecular description (Purdy et al., 2022). However, no structural data was available until 2019. Then a crystal structure of a 180-residue domain of the 1700-residue pORF1 was published (Proudfoot et al., 2019). Similarly to the first structure of an envelope protein from a non-flavivirus *Flaviviridae* (El Omari et al., 2013), the structure of this pORF1 protein domain showed that this part of pORF1 was nothing like what was expected: It had a fatty acid binding domain fold whereas it was supposed to comprise part of a putative protease. This example nicely illustrates the fact that even plausible breakdowns based not only on sequence comparisons but also on viral genome organization, such as had been proposed for pORF1 (Kelly et al., 2016; Koonin et al., 1992), may turn out to be false and how they can be disproved by structural data.

With the availability of AF2, several groups including ours independently predicted the structures of pORF1 proteins or full-length pORF1 (Fieulaine et al., 2023; Goulet et al., 2022; LeDesma et al., 2023). Two important lessons can be drawn from these results: The first is that, despite no detectable sequence identity (except in the active sites of some enzymes), excellent atomic models can be predicted by AF2. Thus, the successes often reported for cellular proteins also hold for RNA (+) viruses despite the very different dynamics in sequence divergence: For RNA(+) viruses too AlphaFold2 does output protein structures that are both accurate at the atomic level and whose quality control indicators are very reliable, with few false positives. Thus, errors in assignment of domains in RNA(+) viruses should be much less frequent now. We are now in a position to directly apply structural analysis to potentially any virus for which we have a sequence. We can thus easily extend a particular structural study to another related virus, whereas previously this meant engaging on years of work before starting to get results, if any. The second lesson is that two features of proteins of RNA (+) viruses make the results harder to interpret: These proteins contain both membrane-interacting segments, for which AF2 performs more poorly, and regions of interactions with various partners, including intrinsically disordered segments and/or proteins that may assume different local structures in different contexts. What insights can still be gained, and what pitfalls should be avoided, are discussed in the next sections.

## 2.3. HEV pORF2: protein-protein interactions

HEV pORF2 is a complex protein, with an N-terminal signal peptide that triggers secretion of most translated pORF2 in a glycosylated form. Still, the minor fraction of translated pORF2 that is not secreted and remains in the infected cell fulfills the all-important role of HEV capsid protein (Montpellier et al., 2018). This form, ORF2i (residues 14–660 of pORF2) has both classical and unusual features for a capsid protein. Residues 130–660 fold as three domains, the first of which (domain S, residues 130–314) displays the jelly roll fold that is classical for a subset of viral capsids (e.g. picornaviruses or noroviruses). Domain S forms the shell of the icosahedral HEV particle, as seen in crystal structures of recombinant HEV pORF2 130–660 comprising domain S and the subsequent domains M and P (hereafter pORF2_S-M-P (Guu et al., 2009)). The most unusual feature of ORF2i is that it is still anchored to endomembranes through its N-terminus. Indeed, our collaborators recently
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** AlphaFold-predicted models of HEV pORF2 interactions with the AP1 adaptor complex.

(A) AlphaFold2 model of the interaction between the ORF2-S domain and the AP1-σ1 subunit (Ferrié et al., 2024), colored by predicted local distance difference test (pLDDT) scores (left) and by chain (right). pLDDT color range is: orange for very low confidence, yellow for low confidence, cyan for high confidence and blue for very high confidence. The corresponding predicted aligned error (PAE, in Å) matrix is displayed on the right. PAE matrices use a blue-white-red color scale, representing values from 0 to 30 Å. Thus, blue colors indicate high confidence in the relative positions of pairs of residues. (B) Conformational changes in the helix spanning residues 165–182 (within the S domain) of ORF2i. The helix is shown in the absence of a binding partner (ORF2i full-length; left) and in complex with AP1-σ1 (middle). The PAE matrix for the full-length ORF2i model is displayed on the right. Note that domains S and M form a tight unit. (C) An initial AlphaFold2 model of full-length ORF2i in complex with all AP1 subunits. On the right, ORF2i is shown in blue; AP1 subunits are colored as follows: AP1-β1, AP1-γ, and AP1-μ1 in gray-levels, and AP1-σ1 in red. The corresponding predicted aligned error (PAE) matrix is displayed alongside. Note that PAE reaches very low values between pairs of AP1 subunits (e.g. β1 vs μ1), indicating that the AP1 complex by itself is correctly modeled, but not between ORF2i and any of the AP1 subunits.</figcaption></figure>

established that ORF2i shuttles to the HEV viral factories through its interaction with the AP1 adaptor complex, that is involved in intracellular sorting of membrane-bound proteins (Ferrié et al., 2024). Using AF2, we could pinpoint the interaction to a contact between ORF2i S and the small AP1 subunit, σ1 (Fig. 1A). It is noteworthy that this interaction, predicted with confidence as shown in the ‘predicted aligned error’ (PAE) plot (Fig. 1A, right), entails a local structural rearrangement of domain S (Fig. 1B, left): Compared to the known structure of pORF2_S-M-P, a small helix 165–182 at the surface of S (in orange on Fig. 1B) is displaced and thus contributes to the interface with

σ1 both directly and by unmasking a stretch of residues that is otherwise buried in all experimental structures of pORF2. In these structures, as in most AF2 models, domains S and M are tightly associated and preclude domain S assuming the conformation seen in the interaction with σ1.

## 2.4. RNA(+) virus interactomes in 3D? Some words of caution

The example of the pORF2/AP1 interaction is enlightening in several ways. Indeed, RNA(+) virus, and more generally RNA virus proteins often have several partners to which they may adjust in different ways.
Despite this added difficulty, we have here an example in which a transient interaction from a minor conformation was successfully predicted with the help of AF2, with unambiguous *in silico* quality indicators. This gives substance to the hope that comparisons that were made in sequence space can now be made in structure space. However, we want to stress that this endeavor will not in general be straightforward for RNA viruses.

### 2.4.1. False negatives

First, in the case of pORF2/AP1 there was experimental evidence for the interaction when we set out to model it (Ferrié et al., 2024). Yet repeated attempts trying to match pORF2 and AP1 were unsuccessful (Tubiana & Bressanelli, unpublished). Such a negative result is shown in Fig. 1C: Even though the target contained σ1, the correct isoform of AP1 subunit σ, AF2 failed to find an interaction with ORF2i, as shown by uniformly large PAE values in ORF2i vs AP1 sectors of the PAE plot. Pairwise trials with all isoforms of all four AP1 subunits on the one hand and ORF2i on the other hand also failed (not shown). Finally, breaking down ORF2i into its component domains S, M and P succeeded, but even so we obtained a clear hit (Fig. 1A) only in a minority of cases. With hindsight, the interaction between ORF2i and AP1-σ1 relies both on dissociation of domains S and M and on the subsequent local structural rearrangement of S described above (Fig. 1B). This readily explains why the complex was difficult to predict. We stress that this is likely to be generally the case for proteins of RNA(+) viruses. Relying solely on the default five models generated by AF2 may overlook relevant conformations, highlighting the usefulness of generating additional models using different random seeds to enhance prediction robustness.

Another example where AF2 altogether failed to predict interactions while there is conclusive experimental evidence for them comes from our work on the human norovirus nonstructural protein 4, NS4. NS4 is a small protein, 179 residues for the genogroup II genotype 4 strains that have been dominant in epidemics and pandemics of acute viral gastroenteritis in the period 2012–2024 (Chhabra et al., 2024). NS4 is the main driver of endomembrane rearrangements that occur in cells during norovirus replication (Doerflinger et al., 2017). We recently established that recombinant NS4 by itself forms membrane-bridging structures whose size indicates self-assembling of NS4 on membranes (Royet et al., 2024). Yet all our attempts to model oligomers or polymers of NS4 with AF2 failed. Here we may have reached a limit in which too many confounding factors prevent correct prediction of interactions. Indeed, NS4 combines features that are known to make the task more difficult: Firstly, it makes up several kinds of assemblies; Secondly, it assumes alternate conformations in different environments, some of which are linked to intrinsically disordered regions and others to membrane-interacting regions (Royet et al., 2024); Thirdly, the membrane-interacting regions at least seem to overlap with protein interaction regions (Tubiana & Bressanelli, unpublished); Finally, NS4 self-assembling does not seem to extend beyond the norovirus genus in the *Caliciviridae* family (Emmott et al., 2019; Kaiser et al., 2006), which may blur coevolutionary signals that AF2 relies on. This is a good opportunity to signal that in cases of alternate conformations or structures of a protein it has been shown that clustering of sequences in the multiple sequence alignment may help (Wayment-Steele et al., 2024): A minor conformation is generally difficult to find but can be successfully predicted by restricting the alignment to a subgroup in which it is stabilized. This is likely to be true also in the case of an interaction that is specific to a lineage in the multiple sequence alignment.

### 2.4.2. False positives

As indicated above, there are generally few false positives output by AF2, that is a false model of a protein or complex with excellent quality indicators. However, we did find some cases that look like false positives looking at interactions with intrinsically disordered proteins. Hepatitis C virus (HCV) NS5A is a multifunctional protein involved in both RNA replication and assembly. It fulfills its many functions in large part by

interacting with several cellular and viral proteins. Half of its ~450 residues make up two intrinsically disordered domains D2 and D3 that are primary factors of interactions with protein partners (Badillo et al., 2017). As one crucial partner of NS5A is the polymerase, NS5B, we sought to model NS5A/NS5B interactions (Tubiana & Bressanelli, unpublished). We thus made the curious observation that D3 tended to wind into the RNA binding cleft of NS5B, sometimes with good PAE statistics. While we cannot rule out that these models reflect some form of regulation of NS5B by NS5A, we rather think that it is a case similar to the one found by Bret el al. in their benchmarking study of intrinsically disordered regions with AF2 (Bret et al., 2024): Using a dataset of experimental protein-peptide complexes, they also found what amounted to true false positives, with an incorrect peptide predicted to bind to the target with high confidence, in cases where a ‘sticky’ target was presented to a non-cognate peptide.

### 2.4.3. Real hits, but superposition of states

A more subtle case, that may be called composite false positive, occurs in the very common case where an RNA(+) viral protein A has several partners but interaction with partner B is normally exclusive of interaction with partner C. This may be because the two interactions are made by different copies of A at different locations in the cell, or because they actually occur successively during viral replication. When challenged to model complex A-B-C, AF2 will try to satisfy simultaneously all interactions. Furthermore, one interaction will be dominant, and it is not necessarily the most important one. An enlightening example here is the case of the protease-helicase of HCV, NS3. Its N-terminal protease domain cleaves among others NS4A from NS3 and NS4B from NS4A. Furthermore NS4A is a cofactor of the protease domain of NS3, contributing one full beta strand to the protease’s fold (Bartenschlager et al., 2013). As a result, AF2 models of the NS3/NS4A complex, though they correctly place this beta strand, also struggle to place simultaneously NS4A’s C-terminus into the protease’s active site. Generally speaking, we have found that the active sites of processing proteases of RNA(+) virus polyproteins are attractors for their substrates in the rest of the polyprotein (Bressanelli, unpublished). With hindsight this is unsurprising, as the simultaneous conservation of the active site of the protease and of the sequences it recognizes makes a clear coevolutionary signal. However, this is also detrimental to modeling of the final processed complexes that normally form after this transient interaction breaks and the product leaves. One protein for which it is to be expected that disentangling superpositions of states will be particularly difficult is the viral polymerase, as in these viruses it is a hub of interactions with other viral proteins and *a minima* with RNA, nucleotides and metal cofactors.

## 3. Concluding remarks and future perspective

### 3.1. AlphaFold3 and cousins: enter RNA

This brings us to our final remarks on atomic modeling of RNA(+) virus protein complexes. Obviously, the inability to include RNA in AF modeling until very recently was a particularly important gap. Now AlphaFold3 purports to fill this gap by including diverse biomolecules—including proteins, RNA, DNA, ligands, ions, and post-translational modifications—in complex assemblies (Abramson et al., 2024). However, its restrictive licensing (limiting commercial use, modifications, and redistribution) has spurred efforts to develop open-access alternatives. For example, RoseTTAFold, a predecessor to AlphaFold3 similar to AlphaFold2, remains widely accessible and continues to evolve to include other kinds of biomolecules (like nucleic acids and ligands with RoseTTAFold All-Atom) (Krishna et al., 2024). Emerging tools like Chai-1 (Discovery et al., 2024), Protenix (Team et al., 2025), and Boltz-1 (Wohlwend et al., 2024) aim to replicate AlphaFold3’s multi-molecule capabilities under more permissive licenses, prioritizing community-driven accessibility. While these
<figure><img src="image_5.png" /><figcaption>**Box 1.** Dialog between modeling and experiment. The red box underlines the critical importance of analyzing the structural models.</figcaption></figure>

alternatives may currently lack the holistic accuracy or broad molecular scope of AlphaFold3, they address critical gaps in usability for academic and industry researchers.

The future thus seems particularly bright for atomic modeling of RNA(+) virus complexes and the potential is enormous. Still, the same caveats apply as outlined above. Particularly the problem of superposition of states will be compounded by introducing RNA in the modeling. This is what we have indeed observed in our trials with AF3 (Bressanelli, unpublished). Thus, the same RNA sequence may be successfully unwound by the viral helicase then fed into the polymerase active site where its complementary strand is synthesized, yielding a double-stranded replicative intermediate that may be locally a perfect match to the initial RNA secondary structure. Communication between helicase and polymerase in these transient processes can be mediated by direct contacts and/or indirectly through contacts with the RNA. We have found that it may be difficult not only to define precisely the complex to be modeled, but also to assess a result with excellent quality indicators, as these may reflect correct modeling of a relevant complex, but alternately may be a collage of interactions between subcomplexes. Obviously, this problem will increase in severity with combinatorial complexity as more partners are added.

### 3.2. Suggested workflow between dry and wet labs

While it is undeniably easy to obtain high-quality models, interpreting them remains a crucial stage. It is not enough to obtain a good prediction score. You also need to know how to analyze and contextualize these models in order to avoid erroneous conclusions. Thus, we advocate, more than ever, a workflow involving a close dialog between structural biology and virology (Box 1). One major breakthrough brought by the new modeling tools is that structural biology can now come into play quickly. For instance, when it is suspected that a protein of interest is active as an oligomer, models can be produced quickly and suggest mutations to validate its function, e.g. in the case of viroporins (Chai et al., 2024). Importantly, accurate structural models can be produced right at the onset of a project, for instance as soon as a viral sequence becomes available.

But precisely because the number of models that can be generated is

now near infinite, analysis of the models is a crucial point in the workflow that should jointly involve modelers and experimentalists. It will serve not only to assess the models but also to refine the scientific questions being addressed. In the end experimental validation remains indispensable, particularly for RNA(+) virus membrane remodeling proteins, that is membrane proteins with alternate conformations, structures, and partners (Agarwal and McShan, 2024).

### CRediT authorship contribution statement

**Stéphane Bressanelli:** Writing – review & editing, Writing – original draft, Funding acquisition, Conceptualization. **Sonia Fieulaine:** Writing – review & editing, Funding acquisition. **Thibault Tubiana:** Writing – review & editing, Visualization, Funding acquisition.

### Funding

This research received specific grants from ANRS: grant numbers ANRS0380 to S.B., ECTZ188022 to S.F. and postdoctoral fellowship ANRS0548b to T.T.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We thank the BIOI2 platform and the I2BC’s IT support team for making AlphaFold2 easily accessible at I2BC.

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