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Frontiers in Biophysics ·

Volume 4 - 2026 | https://doi.org/10.3389/frbis.2026.1923311

Peering beyond the fourth wall: should protein quaternary structure be a state space?

  • Daniela Diana1
  • Yimon Aye2
  • Marcus J. C. Long2,3*corresponding author
  1. 1Department of Immunobiology, University of Lausanne, Lausanne, Switzerland
  2. 2Department of Chemistry, Oxford University, Oxford, United Kingdom
  3. 3Department of Pharmacology, Oxford University, Oxford, United Kingdom
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Abstract

Our understanding of protein structure rests on four pillars. Primary sequence is followed by secondary structural motifs. The third pillar is the three-dimensional organisation of single polypeptide chains. The final level includes associations between individual polypeptide chains, and incorporation of structurally essential non-peptide ligands. AlphaFold and other tools can now reliably predict tertiary structure. However, quaternary structure remains relatively poorly understood and is the least predictable element. This is at least in part because quaternary structure is the most contextually governed level. Recent data, including our own work from ribonucleotide reductase and KEAP1, show that a deeper understanding of dynamic associations and factors, such as post-translational modifications, regulating those interactions is necessary to understand and predict quaternary structure. This is particularly true in cells, where we propose that quaternary structure is better conceptualized as a state space encompassing oligomerization, binding partner availability, post-translational modifications, and localization, all governed by signaling context. We discuss how such a conceptualization helps understand and describe how quaternary structure alterations are brought about by signaling changes. Methods to decode these state-gated, structural changes, and implications for gain-of-function drug mechanism and design are also addressed. We put these considerations in context of recent protein-protein interaction prediction models, interactome profiling studies, and functional omics methods that together may be combinable to predict new protein functions linked to dynamic quaternary association changes.

Keywords

1. Introduction

Anfinsen showed that secondary, and tertiary structures of proteins are, typically (Milner-White et al., 2006; Young et al., 1999) genome-encoded, i.e., determined by their specific primary structures (Figure 1a). Indeed, although protein structure can be modulated by environment (Gambardella et al., 2022), for most given primary structures secondary and tertiary structures are invariant, aside from dynamic local fluctuations (Stiller et al., 2022) and inherent disorder (Struhl, 2024). Reinforcing this notion, tertiary structures usually reflect a protein’s core function (Sun et al., 2013). This logic underpins efforts like the Critical Assessment of Structure Prediction—CASP (Kryshtafovych et al., 2023).

Unsurprisingly, and despite several exceptions (Long et al., 2025; Long et al., 2023), cellular context is, to a first approximation, of low to moderate importance to primary-tertiary structural elements. Anfinsen was able to refold proteins without chaperones (Gambardella et al., 2022) and the field of enzymology and much of drug screening is based around recombinant enzymes (Khosla, 2015), for instance.

Quaternary structure integrates with other contextual elements

On the other hand, the last level of structure, quaternary, is an inherently variable parameter—several quaternary structures of a protein often co-exist in cells (Marciano et al., 2022) (Figure 1a). This plasticity is organized by binding partner availability (Abrusán and Zelezniak, 2024), post-translational modifications (PTMs) (Barbour et al., 2023) and ligand binding (Long et al., 2019), facilitating responsive behavior (Jiao et al., 2017). These fluctuations can be harnessed to control specific signaling processes (Nie et al., 2025). Indeed, throughout the course of our own investigations, it has become clear that biological regulation often acts by changing occupancy of context-dependent assembly states. A language for describing those states, prioitizing proteins that will be involved in state transitions, and for describing new states formed by gain-of-function inducing molecules would be most useful.

We therefore propose that quaternary structure should be reconceptualized as a high-dimensional state space—QSS (Figure 1b). Without extensive formalism, we outline how QSS provides a coarse-grained framework for integrating multi-dimensional regulators, defined by five experimentally measurable, partly interdependent, axes, minimally sufficient to reconstruct association states. oligomerization,

binding partner availability,

PTM-state,

localization,

signaling context, which regulates the other four axes.

In this way, occupancy distribution can be simply written as:

P (QSS) = P (oligomerization, complex composition, PTMs, localization|context).

Thus, individual quaternary structures become non-mutually exclusive states within this multidimensional space, with biological regulation/stimuli (contexts) that modulate QSS-axes, governing state transitions. This conceptualization is timely because parameters defining QSS-axes can now be mapped experimentally with high spatiotemporal resolution (Long and Aye, 2024).

QSS is complementary to interactomics and systems biology (Luck et al., 2017), which catalog pairwise or network-level associations, often agnostic to molecular structure. QSS further encompasses quinary structure (Cohen and Pielak, 2017)-interactions that occur due to a specific context, cellular crowding (Grandori, 2023). However, QSS acknowledges that crowding is not the sole contextual determinant of cellular quaternary interactions. Another, important parameter is the propensity of a specific subcomplex to form independent quaternary structures, QSS degrees of freedom (df). QSS-df reflect the number of distinct configurations accessible under a given cellular context. High QSS-df could arise due to the need to adopt different poses during a complex cycle, such as in the MCM2-7 complex (Costa et al., 2011). Alternatively, high QSS-df can reflect intrinsic conformational plasticity of an oligomer forming multiple non-redundant interaction surfaces, for instance HSP104 uses intrinsic structural plasticity to aid unfolding (Lee et al., 2019). QSS-df therefore may be estimated from mechanistic knowledge, structural studies, as well as estimation of pathway engagement and occupancy of specific PTM states described in the literature or from profiling data (Long et al., 2021). Many proteins with high QSS-df are “ancient enzymes” whose lower order structures are well characterized (Huberts et al., 2010). We will see examples of proteins, including ancient enzymes, with high QSS-df later in the piece.

Structural biology and in silico methods favor well-defined folds

It is worthwhile to ask why concepts like QSS are relatively uncommon. Early structural breakthroughs, like X-ray diffraction analysis of lysozyme, established structural biology (Blake et al., 2020). This success focused attention on well folded, structurally stable proteins, amenable to crystallization. NMR (Hu et al., 2021), EM (Jeyaraj et al., 2025) and other physical methods (Ziegler et al., 2021) expanded our view to more dynamic/disordered structures, with NMR being particularly suited to study states with lower order and to assess dynamic changes (Alderson and Kay, 2021). NMR has, for instance, been particularly important in understanding fuzzy interactions, where disorder exists in the bound state of a protein complex (Miskei et al., 2020). Nonetheless, most high resolution methods still favor rigid, well-defined complexes, emphasizing recombinant proteins (Figure 1c).

AlphaFold enables accurate tertiary structure prediction without experimental input (Jumper et al., 2021). One key advantage is structure prediction from typically overlooked organisms. Although possible by homology modelling (Surya et al., 2018), alphaFold’s predictive power increases confidence. These pipelines become even more insightful when paired with ligand docking analysis (Ma et al., 2024). Experimental validation and refinement remain essential, although the need for bench experiments is coming at increasingly later stages. Nonetheless, a strong penchant for ordered, structured proteins remains.

Higher order structures are a key Frontier

Experimental techniques to study higher order structure are limited by complex stabilities and lack of purification techniques, amongst several other factors. Unsurprisingly, several biologically relevant quaternary states remain poorly characterized experimentally. These entities include fluctional, aggregated structures, such as condensates (Peran and Mittag, 2020), which can be formed through numerous weak multivalent interactions (Kilgore and Young, 2022) (Banani et al., 2017). However, they also include highly contextually regulated molecular complexes, such as the purinosome (Pedley et al., 2022) and signaling superstructures such as signalosomes (Shi et al., 2020).

There have, however, been numerous crystallographic successes, including the spliceosome (Pomeranz Krummel et al., 2009) and ribosomes (Ben-Shem et al., 2010). Several structures contain >70 individual macromolecular components (e.g., 5DGE). Nonetheless, highly stable, structurally “rigid” complexes remain the mainstay. EM has provided insights into conformational variability, and less stable structures, e.g., the spliceosome (Zhang et al., 2024).

The complexities of predicting protein-protein interactions (PPIs) are practical and combinatorial. Transient PPIs likely rely on electrostatic interactions (Ingold et al., 2005; Schairer et al., 2020). These associations do not follow similar rules to those governing protein folding, which are much more dominated by hydrophobic interactions (Kyte and Doolittle, 1982). Nonetheless, a recent AI-based paper analyzed all ∼200 million possible pairwise PPIs (20,000!/(19,998!*2!)), and identified 3,631 unknown interactions (Zhang et al., 2025). However, users must specify numbers of each monomer per complex, making iteration tedious. Oligomer-dependent assemblies could also have been missed.

The number of possible assemblies rises to trillions (20,000!/(19,997!*3!)) if we consider quaternary interactions of three different proteins similarly. Incorporating splice variants, cofactors, ubiquitination, and phosphorylation—amplifies complexity by many folds. Many ligands (aside from a limited number of common ligands, DNA, RNA, and other proteins), and PTMs (unless one of the supported groups of modifications, or a mutational mimic is available) are not currently modellable in the online server, alphaFold3. Running alphaFold3 locally gives more options, particularly for non-covalent ligands. However, this capability does not extend to predicting unknown PTMs or arbitrary chemical modifications. Predicting “new” 70-component complexes and their structures seems a long way away.

These bottlenecks imply that adding new dimensions to current theoretical approaches may be needed. A multiparameter QSS, rooted in experimentally determinable, widely available data would improve prediction capacity.

Setting PPIs in context-approaching a state space

Despite some exceptions (Lin et al., 2023), coevolutionary signals are the bedrock of many protein (Miyazawa, 2013) and PPI-prediction (Fang et al., 2024; Cong et al., 2019) programs, such as alphaFold2/colabFold, effectively-P (structure∣sequence, multiple sequence alignment). However, evolutionary trajectories may not always be linked to known biologically relevant aspects that are assumed to shape protein structure (Abrusán and Foguet, 2023). It is also unclear how non-canonical functions, e.g., moonlighting functions, fit into evolutionary paradigms (Jeffery, 2020), despite these functions being linked to quaternary structure (Copley, 2014). Coevolutionary logic may be less applicable to phosphorylation (Palmeri et al., 2014) and ubiquitination (Lu et al., 2017) sites, even though these modifications control many quaternary associations (Liu et al., 1999); protein modification can be retained, but precise modification sites often shift position across small evolutionary distances and/or have high redundancy. Conventional evolutionary logic may be even less applicable to non-canonical PTMs, like non-enzymatic modifications (Poganik et al., 2018), despite these modifications also controlling protein interactomes (Zhao et al., 2022) and secondary structure (Surya et al., 2018).

Crucially, many PTMs do not typically affect domain structure significantly (Nishi et al., 2014; Correa Marrero et al., 2025), but form docking sites for new interactions (Kamacioglu et al., 2021). Thus, evolution-heavy assumptions may stumble when more complex structures that likely contain some PTM-orchestrated interactions are considered.

Experimental mapping techniques are compatible with QSS

Many modern experimental methods to profile interactomes, e.g., APEX (Long et al., 2016), ultraID (Kubitz et al., 2022), and micromap (Geri et al., 2020), biotinylate proteins in proximity to a specific bait protein, permanently tagging proximal proteins, allowing enrichment. These methods capture the “QSS- complex composition” term, albeit with tens to hundreds of nanometer resolution (Figure 1d). To increase contextual specificity, these experiments can be performed in specific (sub)locales, using split proteins, for instance (Han et al., 2019). Other methods use trifunctional crosslinkers to inform on interactomes and offer course grained information on PPI-interfaces (Zhao et al., 2025).

Activity-based profiling (Long et al., 2023), spatiofunctional profiling (Long et al., 2026), phospho-profiling (Gaizley and Surinova, 2025), and other “global” omics experiments (Long et al., 2021) can provide the “QSS-PTM term”, in some instances contextually. These methods often have low spatial resolution. However, we (Liu et al., 2024), and others (Liu et al., 2021), have shown spatial and functional omics are combinable, meaning that a spatial dimension can be added to many proteomics experiments.

Adding a “final touch” of molecular level structural detail to the above data is a huge undertaking. However, as we described, the above experiments define parameters contributing to QSS. Indeed, all these parameters can be defined in a specific biological context. The resulting, annotated interactomes will likely collapse to a system with complexity similar to pairwise protein-protein interactions. This transformation is performed on an individual protein level by numerous laboratories, and it has been applied successfully at the binary level (Burke et al., 2023), and also selectively to some “proteomewide datasets” (Zhao et al., 2025; Catoiu et al., 2024; Wheat et al., 2021) although as yet it has not been expanded generally. Indeed, the main challenge (Correa Marrero et al., 2025) is integrating the data systematically and triaging data sets by confidence/species/context, such that it can be readily accessed and standardized to a more global system, potentially based around parameters of our QSS model.

We provide two examples that, in our opinion, are best understood using QSS language. These examples reflect that in our studies we frequently have come across situations where the key to the problem was in fact the context-dependent assembly state, rather than a specific protein.

Ribonucleotide reductase-high QSS-df allows for complex regulatory modes

Ribonucleotide reductase (RNR) converts NDPs to dNDPs (Aye et al., 2015), maintaining absolute, and relative dNTP levels. RNR has two essential subunits, RNR-β and RNR-α. The smaller subunit, RNR-β, activates the larger, catalytic subunit, RNR-α (Figure 2a).

RNR-α houses three nucleotide binding sites: the active site; an activity site (A-site), regulating overall activity positively (ATP) or negatively (dATP); and a specificity site (S-site), defining substrate preference. Canonical RNR-α exists as a monomer/dimer (1). ATP binding at the activity site promotes transient formation of higher-order oligomers. Activity down regulators binding the A-site, e.g., dATP, form hexameric states that have been characterized by several methods, including SAXS (Ando et al., 2016), size exclusion chromatography (Wisitpitthaya et al., 2016; Aye et al., 2012), and EM (Fu et al., 2018) (2) (Figure 2b).

Clofarabine (Aye and Stubbe, 2011) and fludarabine (Wisitpitthaya et al., 2016) are approved anti-metabolite drugs targeting RNR-α (Aye et al., 2015). Like, dATP, their triphosphate forms target RNR-α′s A-site, inducing RNR-α hexamers. However, clofarabine/fludarabine-triphosphate-induced hexamers are isolable from cells treated with the corresponding nucleosides. Intriguingly, these hexamers show structural diversity (Wisitpitthaya et al., 2016).

We investigated how clofarabine/fludarabine-triphosphate-initiated RNR-α hexamerization manifests in cells. Under these conditions, the canonically cytosolic RNR-α translocated into the nucleus (Fu et al., 2018), through a gain-of-function association with importins (3) (Figures 2a,b). Nuclear RNR-α suppressed DNA synthesis by interacting with a novel promoter of s-phase DNA synthesis, ZRANB3 (Pelucchi et al., 2026). We further showed that a known cytosolic RNR-α-hexamer-selective binder, IRBIT (Arnaoutov and Dasso, 2014), inhibits nuclear translocation by associating with RNR-α-hexamers.

Several cancer cell lines were addicted to ZRANB3’s function, explaining the decades long conundrum of how RNR-α acts as a tumor suppressor (Fu et al., 2018). It also emerged that clofarabine uses this mechanism as part of its pharmaceutical spectrum (Long et al., 2020).

This example shows how ligand-induced QSS shifts amplify signaling outputs, including non-canonical functions that oppose canonical pathway activity. The structural plasticity of RNR-α′s hexamers indicates that more unexpected functions are accessible (large QSS-df). Polyfunctionality through QSS plasticity is another property of moonlighting proteins, shared with the “ancient enzymes” GAPDH (Jung et al., 2014), PKM2 (Li et al., 2026), and tRNA synthetases (Guo and Schimmel, 2013).

Applications to QSS

Using colabFold (Zhang et al., 2025), RNR-α-hexamers were predicted to be a trimer of dimers, with constituent dimers overlapping with a known dimer structure (2) (Figure 2b). Overlapping the different hexameric states predicted by colabFold, alluded to conformational flexibility, although none of the predicted arrangements resembled the more structurally diverse hexamers observed by negative stain EM.

Docking the two hexamer-specific binders, Importin and IRBIT, showed interactions between two different RNR-α dimers within RNR-α-hexamers (3) (Figure 2b). The interaction sites on RNR-α were similar between IRBIT and Importin. These results explain the need for RNR-α hexamerization and are consistent with RNR-α partitioning between translocation and IRBIT binding.

Thus, guided by experimental data, modern prediction software can provide relevant structural information that could be useful to understand specific quaternary states and their interactions. These results indicate that a global, integrated approach may yield significant insights.

RNR-oligomerization-governed location and interactome plasticity

The RNR-α-importin interaction was found only because we had identified a new QSS-localization, nucleus, initiated by a change in QSS-oligomerization. The interaction with ZRANB3 was identified by yeast 2-hybrid analysis, a proteome wide interactomic technique providing pairwise PPI analysis. Normally, this interaction would have been discarded, because RNR-α′s predominant cytosolic location excludes a nuclear QSS-term, unless under specific conditions that were not met (Niida et al., 2010). Thus, reflection on QSS-term guided completion of this project. Text box titled “Lessons from RNR-α” lists five points: QSS changes can combine structural and conformational shifts, persist after stimulus, promote localization changes, interact with moonlighting functions, and are involved in tumor suppression and therapeutic targeting.

KEAP1-life and death under command of the fat controller

KEAP1 is the guardian of the antioxidant response-(AR), that upregulates a battery of cytoprotective genes. KEAP1’s main function is to bind the AR transcription factor, NRF2, preventing AR signaling. During cellular stress, reactive electrophilic metabolites-REMs are upregulated and label KEAP1 as a non-enzymatic PTM. This modification induces KEAP1 to release NRF2, upregulating AR (Van Hall-Beauvais et al., 2022) (Figure 2c).

We developed ways to direct REMs to KEAP1, regulating its activity in cells (Long et al., 2021). We tested this approach in zebrafish embryos, examining changes in signaling by RNA-seq (Poganik et al., 2021). Transcripts selectively expressed in innate immune cells were significantly depleted when KEAP1 was REM-labelled. Loss of innate immune cells occurred under the same conditions, using embryos expressing fluorescently labeled innate immune cells.

We proceeded to investigate changes in KEAP1 interactome upon labelling by REMs, using immunoprecipitation. WDR1, a KEAP1 associator, was released from KEAP1 upon labelling. Liberated WDR1 interacted with cofilin and actin, triggering BAX-dependent apoptosis. Innate immune cells were particularly sensitive to buildup of free WDR1, explaining selective loss of these cells. Using zebrafish and primary mouse BMDMs we showed that the approved multiple sclerosis drug, Tecfidera, itself a reactive metabolite analog, operates by a similar mechanism.

Applications to QSS

Using colabFold (Zhang et al., 2025), we recapitulated literature data (Van Hall-Beauvais et al., 2022). KEAP1 dimer (4) formed a complex with NRF2, (5), bound via two different binding regions, an ETGE sequence, which is present in many Kelch domain binders and is the higher affinity binder, and a DLG motif. Each region interacted with one of the two Kelch domains within the BTB-domain-linked KEAP1 dimer (Suzuki et al., 2025). WDR1 (6), in one of the best predicted poses, also docked to the Kelch domains in the KEAP1 dimer, consistent with our conclusions that NRF2 and WDR1 bind KEAP1 competitively. This predicted interaction did not involve WDR1’s ETGE motif. However, unlike many KEAP1 interactors, WDR1 does not bind to the purified Kelch domain, indicating that WDR1’s interaction with KEAP1 may be distinct from canonical KEAP1 interactors. These results are altogether consistent with KEAP1 showing high QSS-df (Figure 2d) (Hast et al., 2013). KEAP1’s interaction with NRF2 and WDR1 via a similar site explains why KEAP1 releases both WDR1 and NRF2 upon electrophile labeling. Unfortunately, it cannot account for why proapoptotic signals dominate in innate immune cells, but other cells are activated for cellular defence. However, in QSS terms, electrophile labeling does not simply disrupt a single interaction. Rather, it defines QSS transition vectors across a family of KEAP1 assembly states whose composition depends on cell-type-specific expression of competing interactors. This implies that to understand signaling outputs from one quaternary state, we may need to factor in other quaternary states that either regulate, compete, or otherwise intersect with the quaternary state of interest. Effectively, signaling outputs emerge from net trajectories through QSS to contextual attractor states, rather than from effects on individual quaternary structures.

KEAP1-interactome rewiring by REMs

Like many global profiling projects, this investigation started out by coupling a specific chemical perturbation to a “global” data-set, in this instance RNA-seq. Having identified an interesting downstream phenotype, defining the signaling pathway, we tested how selectively modulating KEAP1’s PTM-QSS term, affected its interactome. The hit proteins here underwent a compensatory change in QSS, allowing new interactions to form. Extrapolating this example, linking reactive molecule labelling of different proteins to downstream phenotypes would uncover QSS modulations that could be modelled structurally.

We have discovered several proteins whose association-QSS terms are regulated by reactive molecule signaling (a signaling-QSS term). These include CDK9 (Zhao et al., 2022) and NCBP1 (Chang et al., 2026). Text excerpt titled “Lessons from KEAP1/WDR1” with two points: QSSs can be regulated by REMs, and QSS changes can have cell-type specific, and even opposing, outputs.

Bio-orthogonal (o)QSS

Several drugs promote new biological quaternary interactions. These include molecular glues, e.g., thalidomide (Ichikawa et al., 2022), and inducers of dimerization, e.g., PROTACs (Békés et al., 2022) and RIPTACs (Raina et al., 2024). These molecules effectively access oQSS. Our definition of quaternary structure adapts quite well to these molecules and may help conceptualizing how these molecules function.

As oQSS lies outside biologically accessible QSS, it is possible that biological systems are also less well adapted to regulate them. Examining PROTACs through this lens highlights that PROTACS target oQSS while retaining aspects of accessible QSS, in this instance K48-linked ubiquitin chains. Unsurprisingly, K48-linked-Ub deubiquitinating enzymes are known suppressors of PROTAC function (Cardno et al., 2026). Our formalism argues that newly designed molecule classes allowing us to target more divergent oQSS (Long, 2021) could be more robust to drug resistance, for this reason. Indeed molecules that induce highly orthogonal QSS should be prioritized for drug discovery because they access assembly states that evolution has not necessarily optimized to regulate or buffer.

2. Conclusions

Unlike other structural elements, developing holistic prediction software for protein quaternary interactions requires integration not only of sequence and evolution, but also of modern “omics” data. This is because the number of potential interactions, regulatory factors, PTMs, and contexts, are too large to cover computationally.

Huge datasets exist that are ripe for integration into these pipelines. Other papers contain smaller datasets that can further be used to defined specific nuanced aspects of QSS (Long et al., 2025). Unfortunately, these are often carried out by different laboratories, and often under slightly different conditions and potentially in different lines, etc. Nonetheless, fusion of large datasets from different laboratories, across a multidimensional landscape, defined by our five parameters, should rigidify analysis. In the future, it is hoped that standardization of conditions will aid such data pooling.

Quaternary structure is therefore not a discrete organizational layer, but a context-dependent distribution over functional protein states that intersects with poorly defined biological properties, such as moonlighting functions. QSS provides a framework for linking molecular perturbation to emergent cellular behavior across spatial, temporal, and chemical dimensions.

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

DD: Writing – original draft, Visualization, Writing – review and editing. YA: Writing – review and editing. ML: Writing – original draft, Writing – review and editing.

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Citation

Diana, D., et al. (2026). Peering beyond the fourth wall: should protein quaternary structure be a state space?. Frontiers in Biophysics, Volume 4. https://doi.org/
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Copyright © 2026 Diana, Aye and Long.

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*Correspondence: Marcus J. C. Long

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