Biology subjects

Lindorff-Larsen, K.

Publications and source records attributed to Lindorff-Larsen, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Towards transferable explicit-solvent coarse-grained models for biomolecular condensates

Biomolecular condensates formed by intrinsically disordered proteins require molecular models that accurately describe proteins in both dilute solution and condensed phases. Explicit-solvent coarse-grained models offer an attractive balance between chemical resolution and computational efficiency. Yet, it remains unclear whether improving dilute-state properties is sufficient to obtain an accurate description of condensates. Here, we address this question by introducing minimal modifications to the Martini 3 force field that combine recent advances in bonded interactions with refined protein-water interactions and strengthened glycine self-interactions, while preserving the underlying chemical transferability of the model. The resulting model substantially improves the description of single-chain conformations across a diverse benchmark of disordered proteins. We then investigate phase separation of the well-characterized low-complexity domain of heterogeneous nuclear ribonucleoprotein A1 and its sequence variants. The model reproduces several key physicochemical properties of biomolecular condensates, including chain expansion in the dense phase, sequence-dependent intermolecular contacts, protein diffusion and its relation to single-chain dimensions, and hydration, while revealing quantitative limitations in condensate density, phase equilibria, and ion partitioning. Our results show that improving dilute-state behaviour translates into a better description of condensed-phase properties, including condensate density, but is not sufficient to quantitatively reproduce the equilibrium between the dilute and dense phases.

biophysics↗

bioRxiv2026

Coarse-grained models for simulations of double-stranded nucleic acids for mixed protein-nucleic acid condensates

Biomolecular condensates function as membraneless compartments, and some protein condensates can selectively concentrate single-stranded nucleic acids while excluding double-stranded nucleic acids. Understanding how nucleic acid structure affects partitioning into condensates has important implications for nucleic acid activity and function within condensates. Here, we present a set of coarse-grained two-bead-per-nucleotide models for simulations of double-stranded RNA and DNA in the CALVADOS framework. Our models separately represent the backbone and base, and maintain the helical structures using an elastic network potential tuned to capture chain stiffness. For dsRNA, the base stickiness was tuned using experimental data on differential partitioning of single- and double-stranded RNA into Ddx4N1 condensates in order to account for reduced base accessibility upon duplex formation. This RNA structural selectivity varied with the balance of electrostatic and non-electrostatic interactions, as revealed by simulations of condensates of the CAPRIN1 disordered region at varying ionic concentrations and with an R-to-K sequence variant. Finally, we developed parameters for double-stranded DNA using a similar approach. We envision that the CALVADOS models for double-stranded RNA and DNA will be useful for studying co-condensates of proteins and structured nucleic acids.

biophysics↗

bioRxiv2026

Nanosecond methyl dynamics in the eukaryotic RNA exosome core

Dynamics in proteins occur on a wide range of timescales and are crucial for protein function. On the fast end of that timescale, pico- to nanosecond dynamics have been extensively employed as proxies for entropy and their amplitude can be described by order parameters. Experimentally, NMR can be used to determine order parameters of the protein backbone and, via deuterium relaxation, of methyl groups, yet such experiments cannot be applied to large protein assemblies. In contrast, relaxation-violated coherence transfer experiments, that allow for the determination of side chain order parameters in highly deuterated, methyl-labeled proteins, are more sensitive. Here, we demonstrate that such experiments can be applied to very large, asymmetric protein assemblies by determining axial methyl order parameters for the 300 kDa fully asymmetric core of the eukaryotic RNA exosome complex. Ile-{delta}1[13CH3] methyl groups adopt a wide range of order parameters but highly flexible side chains are infrequent. High quality data, which we obtain for flexible regions, is required to observe subtle effects of RNA binding on order parameters. Local cryo-EM Q-scores correlate moderately with order parameters suggesting that Q-scores contain information on nanosecond motions. AF2{chi}, a recently described prediction tool for side-chain variability, provides good estimates of methyl order parameters, which are, in favorable cases, strongly correlated with experimental values. We thus demonstrate that relaxation-violated coherence transfer experiments can be employed to determine order parameters in large, asymmetric protein complexes that are difficult to capture by other methods, yet are crucial for the understanding of protein function. SignificanceNanosecond side chain dynamics contribute to the entropy of proteins and are therefore proxies for protein stability and binding. Here, we demonstrate that NMR can be employed to experimentally quantify nanosecond dynamics in large, asymmetric proteins paving the way to assess contributions of fast dynamics to the quality of static protein structures. Furthermore, we employ the experimental data to validate computational methods that provide structural insights into nanosecond dynamics.

biophysics↗

bioRxiv2026

Not All Charges Are Equal: Side-Chain Chemistry Reshapes the Disordered Ensemble of α-synuclein

The conformational ensembles of intrinsically disordered proteins (IDPs) are governed by the balance of electrostatic and hydrophobic interactions encoded in their primary sequences. Current polymer-physics models of IDPs frequently group amino acids by coarse-grained properties, such as net charge, often overlooking the distinct residues side-chain chemistries. Analysis across the IDP database reveals that these sequences are sensitive to specific residue identities, where the substitution of aromatic, proline, or hydrophobic groups serves as a primary driver of chain dimensions. However, these data also highlight that even subtle chemical variations between similarly charged residues can consistently shift global compaction. Here, we explore the role of residue identity using small angle X-ray scattering (SAXS) of seven -synuclein variants with progressively increasing numbers of lysine-to-arginine substitutions, two positively charged amino acids with different side-chain chemistry. We show that increasing arginine content drives a systematic compaction of the conformational ensemble, although variants with identical number of substitutions but different positional arrangements suggest influence to the sequence context. Moreover, while increasing salt concentrations shift the structural ensemble from a Gaussian toward a self-avoiding-walk statistics, the arginine-dependent contraction trend remains robust across both regimes. Molecular-dynamics simulations combined with SAXS data reveal that arginine substitutions reduce ensemble heterogeneity by stabilizing transient long-range contacts. Finally, aggregation assays demonstrate that this arginine-driven compaction correlates with an accelerated transition to amyloid fibrils. Our findings demonstrate that chemically subtle substitutions between similarly charged residues can fundamentally reshape the conformational ensemble of IDPs, suggesting that side-chain identity is a critical, yet underappreciated, determinant of protein disorder and proteotoxicity. O_TEXTBOXSignificance StatementIntrinsically disordered proteins (IDPs) are critical to cellular signaling and neurodegeneration, yet our ability to predict their behavior remains limited by a "coarse-grained" understanding of their sequences. We expand the view that net charge and patterning is a primary determinant of IDP dimensions by showing that lysine and arginine, residues identical in charge, exert opposite effects on the conformational landscape of -synuclein model-system. We find that arginine substitutions act as a "molecular glue", driving protein compaction and reducing ensemble heterogeneity. Crucially, this compaction accelerates amyloid aggregation, overturning the conventional assumption that collapsed states protect against fibrillization. This work demonstrates that side-chain identity and patterning are vital for protein homeostasis, providing a new framework for the rational design of IDP-based therapeutics. C_TEXTBOX

biophysics↗

bioRxiv2026

Full-Length Molecular Models of Brain-Derived α-Synuclein Fibrils Reveal a Fuzzy-Coat-Mediated Mechanism for Selective Peptide Binding

Parkinsons disease (PD) is characterized by the aggregation of -synuclein (aSyn) into amyloid fibrils that seed further aggregation and contribute to pathological spreading. Peptides that bind aggregated aSyn are promising therapeutic leads, but their validation is slow, difficult to standardize, and often relies on structural models limited to the ordered cross-{beta} core. Here, we built models of brain-derived full-length aSyn fibrils by extending the Lewy-fold cryo-EM structure with disordered N- and C-terminal segments and sampling the resulting ensembles with the CALVADOS coarse-grained force field. The resulting fibrils display a dynamic fuzzy coat in which the termini, especially the acidic C-terminal tails, form recurrent transient contacts with the core, including the aggregation-prone {beta}5 and {beta}9 motifs. We then used these full-length fibrils in a standardized in silico assay for peptide binding. Simulations of the validated peptide binders PSM3 and LL-37 reproduced their relative binding behavior and converged on a common mechanism in which electrostatic capture by the anionic fuzzy coat precedes stabilization on recurrent P2 and P3 hotspots within the structured core. Control simulations with monomeric aSyn or core-only fibrils showed that persistent association is lost in the absence of the full-length architecture, providing a mechanism for selectivity toward aggregated species. Finally, screening 123 peptides from aSynPEP-DB using a relative contact-based binding score yielded a ranked set of candidate binders and identified net positive charge as the dominant determinant of sustained association, with hydrophobicity acting as a secondary modulator. Together, these results establish full-length, brain-derived fibril ensembles as a practical framework for understanding ligand recognition at pathological amyloid surfaces and for prioritizing therapeutic peptide binders targeting aggregated aSyn. SignificanceParkinsons disease is driven by the assembly of -synuclein into amyloid fibrils, yet most structural models of these aggregates omit the disordered termini that form the fibrillar "fuzzy coat" in vivo. Here we use coarse-grained simulations to reconstruct full-length, brain-derived -synuclein fibrils and show that this fuzzy coat transiently contacts the Lewy-fold core, reshaping access to cross-{beta} surface motifs. Using these ensembles in a computational assay, we recapitulate the relative binding behavior of validated peptide inhibitors and reveal a two-step mechanism in which cationic and amphipathic peptides are first captured by the anionic fuzzy coat and then engage recurrent core hotspots. This framework explains selective recognition of aggregated -synuclein and provides a practical route to prioritize therapeutic peptide binders.

bioinformatics↗

bioRxiv2026

Proteome-wide identification and modeling of interactions between transactivation domains and arginine-glycine-rich regions

Transcription factors (TFs) and RNA-binding proteins (RBPs) coordinate gene expression across transcriptional and post-transcriptional layers, yet the principles that govern their direct physical coupling, especially through intrinsically disordered regions, remain unclear. Here we combine proteome-scale interaction mapping, disordered-region annotation, coarse-grained simulations and sequence-based prediction to dissect a prevalent TF-RBP interface mediated by acidic/hydrophobic transactivation domains (TADs) and arginine-glycine-rich (RG/RGG) regions. Network analysis reveals a global enrichment of RBP partners among TF interactions and identifies TF and RBP hubs that bridge transcriptional regulation with RNA-centered pathways. Using a sequence grammar enriched in acidic and aromatic residues, we define 230 RG/RGG-binding TAD-like segments across 190 TFs and we map 1,008 compact RG/RGG regions across 823 RBPs based on proteome-wide motif spacing. Coarse-grained simulations (CALVADOS) of representative TAD-RGG pairs quantify interaction propensities and indicate that association is primarily driven by electrostatic complementarity and charge patterning, with sequence "stickiness" modulating interaction strength. Using a hybrid machine-learning model we predicted simulated interaction strengths from a compact, interpretable set of features and extrapolate these rules to the full combinatorial space, enabling systematic prioritization of candidate TF-RBP couplings. To validate these predictions experimentally, we used NMR titration experiments on a subset of TAD-RGG pairs spanning the predicted affinity range, which showed agreement between predicted affinities and NMR-derived dissociation constants. Together, our results support a predominantly electrostatic mode of association and establish a quantitative framework for identifying and prioritising TF-RBP partnerships, revealing how complementary sequence grammars within disordered regions couple transcriptional regulation to RNA processing and transport.

biophysics↗

bioRxiv2026

Disagreement among variant effect predictors guides experimental prioritization of target proteins

Interpreting the functional consequences of genetic variation, especially rare missense variants, remains a significant challenge in human genetics. Computational variant effect predictors (VEPs) and multiplexed assays of variant effects (MAVEs) provide complementary approaches, with VEPs offering scalable predictions and MAVEs delivering detailed empirical measurements. However, MAVEs are resource intensive and cannot yet be applied broadly across the proteome, making it important to identify proteins where experimental mapping will be most informative. We hypothesised that MAVEs should be particularly valuable for proteins where computational predictors disagree, as such disagreement may highlight mechanistic blind spots. To test this, we analysed predictions from ten distinct VEPs across more than 13,000 human proteins and quantified inter-predictor concordance. We observed substantial variability across proteins in the degree of agreement across predictors and investigated structural, functional and gene-level features associated with this variation. We find that inter-VEP concordance showed no relationship with agreement to experimental MAVE data. If predictor agreement reflected how intrinsically predictable a protein is, these quantities would be expected to correlate. Their decoupling instead suggests that MAVEs may provide orthogonal information to VEPs, supporting the use of inter-VEP disagreement to prioritise proteins where experimental data will be most informative. We therefore propose using inter-VEP disagreement as a practical strategy to prioritise proteins for experimental characterization. Focusing on proteins with low predictor concordance should maximise the informational value of new MAVEs, and improve variant interpretation in both research and clinical contexts.

bioinformatics↗

bioRxiv2026

Comprehensive Variant Effect Map of Parkin-Mediated Mitophagy in Parkinson's Disease

The development of Parkinsons disease (PD) has a substantial genetic basis. Variants in the PRKN gene account for roughly half of autosomal-recessive PD cases, yet most variants remain classified as variants of uncertain significance. PRKN encodes the E3 ubiquitin-protein ligase, Parkin, that plays a key role in mitochondrial quality control by initiating mitophagy. In this work, we introduce a multiplexed mitophagy assay and measure the activity of more than 99% of all possible single-amino acid substitution and nonsense variants of Parkin. We also demonstrate that misfolded, rapidly degraded variants autonomously trigger Parkin-independent mitophagy. The obtained activity landscape closely reflects known structural and functional aspects of Parkin while revealing new insights into specific functional effects across the protein. This dataset near-perfectly distinguishes pathogenic from benign variants and surpasses both computational predictors and abundance-based metrics in pathogenicity assessment. Finally, the data pinpoint hypomorphic variants that could be amenable to rescue in future personalized therapeutic approaches for PD.

genetics↗

bioRxiv2026

On the Use of Double Mutant Cycles to Probe the Molecular Interactions in Biomolecular Condensates

Disordered proteins can form biomolecular condensates by demixing from their environment, enabling reversible compartmentalisation of cellular components in the form of membraneless organelles. Multivalent interactions are essential for this type of phase separation behaviour, and for disordered proteins, the potential for multivalent interactions is encoded in the sequence composition and patterning. Mutational studies have been instrumental in helping elucidate this sequence grammar by perturbing the amino acid sequence and quantifying the resulting changes in the driving force for phase separation. While such studies have provided a detailed and predictive understanding of the driving forces for phase separation, they strictly do not inform on the nature of the interactions that drive phase separation. Here, we propose using double mutant cycles to explore molecular interactions and their contributions to condensate properties more directly. We explore the applicability of double mutant cycles for different types of interactions in condensates formed by the low-complexity domain of hnRNPA1 using coarse-grained molecular dynamics simulations. We find that the interactions between arginine and tyrosine residues, as well as between aromatic residues, contribute mostly additively to the propensity for phase separation. However, for the interactions between charged residues, we find that--in an interplay with the net charge of the protein--there is a measurable non-additive contribution to the phase separation propensity. Based on our results, we envisage that double mutant cycles could provide additional insights into protein phase separation, thus expanding the understanding of the sequence grammar and the underlying molecular interactions.

biophysics↗

bioRxiv2026

BAG6 and RNF126 are broadly involved in protein quality control of non-native missense protein variants

Protein quality control (PQC) degradation is a vigorous and selective system that limits the accumulation of harmful, non-native and aggregation-prone proteins. Specific PQC pathways have been described for misfolded ER proteins, aberrant translational products and mislocalized proteins. However, many components involved in PQC of structurally unstable cytosolic proteins are unknown despite such proteins being a common consequence of pathogenic coding variants. Here, we present results from a genome-wide CRISPR knockout screen to identify components involved in PQC of the Parkin R42P variant. We find that HSP90 and STIP1 stabilize Parkin, while the BAG6 chaperone and E3 ubiquitin ligase RNF126 are critical for PQC degradation. Variant abundance by massively parallel sequencing (VAMP-seq) reveal >1000 Parkin variants, including several variants linked to autosomal recessive juvenile Parkinsonism, are BAG6 targets. Pathogenic missense variants in phenylalanine hydroxylase (PAH) and the tumor suppressor FLCN are also stabilized in BAG6 and RNF126 knockout cells. We propose that BAG6 and RNF126 are broadly involved in PQC and may represent targets for the development of therapeutics for protein misfolding diseases.

biochemistry↗

bioRxiv2026

A stickiness scale for disordered proteins

Disordered proteins are a heterogeneous group of proteins that play a broad range of functions in biology, and display conformational properties that range from compact globules to expanded chains. We here describe the results of a data-driven approach to derive a scale that represents the propensity of the twenty amino acids to interact with one another relative to water. The scale is based on biophysical experiments on 115 proteins and can be thought of as a stickiness (or hydropathy) scale specific for disordered proteins. We compare the scale to 70 other previously reported hydropathy scales and find that it is closer to four scales related to membrane proteins or the transition temperatures of elastin-like peptides. We envisage that the new scale will be useful in bioinformatics and machine learning approaches to quantify the role of sequence composition and patterning in disordered proteins, to understand the driving forces for their interactions with other molecules, and their evolutionary conservation.

biophysics↗

bioRxiv2026

Transient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling

Intrinsically disordered proteins populate heterogeneous conformational ensembles that are challenging to characterise. While all-atom molecular dynamics simulations can provide highly detailed insights into dynamic ensembles, achieving sufficient sampling remains difficult. Here, we show that On-the-fly Probability Enhanced Sampling (OPES) in the multithermal ensemble enables efficient generation of atomistic ensembles for disordered peptides and proteins ranging from 15 to 71 residues in length. Using the potential energy as a collective variable, OPES achieves multithermal sampling within a single simulation replica, without replica exchange or extensive parameter tuning. Across multiple systems, OPES yields reweighted ensembles broadly consistent with replica-exchange with solute tempering (REST2) and unbiased simulations, while accelerating convergence and enabling broader exploration of low-population conformational states. Applied to the intrinsically disordered transcriptional coactivator ACTR, OPES reveals transiently structured states in which multiple -helices involved in partner binding fold cooperatively and form tertiary contacts. These rare, partially structured conformations are reversibly sampled during the simulations, consistent with extensive NMR and SAXS data, and could facilitate folding-upon-binding through conformational selection. They may also represent viable targets for drug design or for engineering disordered proteins with customised conformational landscapes. More broadly, our results establish OPES multithermal sampling as a robust and accessible approach for uncovering rare, functionally relevant conformations in intrinsically disordered proteins.

biophysics↗

bioRxiv2026

Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex

Proteins execute cellular functions through a structural continuum ranging from stable, folded domains to highly dynamic intrinsically disordered regions (IDRs). Conformational ensembles represent the set of three-dimensional structures a protein adopts under a specific set of conditions, and underlie essential processes from catalysis to complex signaling networks. While deep learning has revolutionized structure prediction, capturing the distinct conformational diversity of folded and disordered regions--especially within multidomain proteins and large assemblies--remains a fundamental challenge. Here we introduce IDPFold2, a generative framework that models the heterogenous protein thermodynamics by integrating a Mixture-of-Experts architecture into the flow matching framework. By routing residues from different regions to specialized expert networks, IDPFold2 accurately predicts conformational ensembles for folded domains, IDRs and multidomain proteins. IDPFold2 outperforms state-of-the-art methods in capturing key functional states and fitting the experimental observations across local and global scales. Furthermore, we describe an extension of IDPFold2 to protein assemblies, deciphering the complex binding modes of IDRs within large macromolecular complexes, providing a generalizable tool for exploring the dynamic proteome.

bioinformatics↗

bioRxiv2026

Mapping the GCK-GKRP interaction landscape using deep mutational scanning by reverse two-hybrid screening

Glucokinase (GCK) regulates insulin secretion in the pancreas and glycogen synthesis in the liver. The best characterized interaction partner of GCK is the glucokinase regulatory protein (GKRP). GKRP is expressed in the liver where it regulates GCK activity and abundance. In this study, we used a library of GCK single amino acid substitutions and a reverse two-hybrid screen to characterize the impact of GCK variants on GKRP interaction. We obtained interaction scores for 7534 GCK single amino acid substitutions. A major driver of loss of interaction appeared to be loss of protein abundance, and we therefore compared interaction scores with our previously obtained GCK abundance scores to disentangle variant effects on interaction and abundance. Variants specifically perturbing interactions were found at the interaction interface but also structurally far from GKRP, revealing potential allosteric effects. In addition, unstable variants at buried residues in the large domain were stabilized by GKRP. These variants could potentially also be stabilized by e.g. pharmacological chaperones to rescue GCK function. In conclusion, we have now characterized the activity, abundance and interaction of 7128 GCK variants. Collectively these scores help pinpoint variants solely perturbing GKRP interaction, giving rise to liver-specific effects and potentially subtle glucose homeostasis phenotypes.

biochemistry↗

bioRxiv2025

Molecular interactions underlying selective partitioning of clients into MED1 and FUS condensates

AbstractBiomolecular condensates selectively recruit and exclude proteins, and this partitioning is sometimes dictated by their intrinsically disordered regions. Factors that determine partitioning include client-scaffold interactions and stabilisation of the scaffold network within the condensate, yet a molecular and predictive understanding has not been fully achieved. Here, we used coarse-grained molecular dynamics simulations to elucidate molecular interactions between two types of nuclear proteins as scaffolds: the charge-rich disordered region of Mediator 1 (MED1) and the aromaticrich disordered region of Fused in sarcoma (FUS), in mixtures with client intrinsically disordered regions from six different proteins. We show distinct partitioning into the MED1 and FUS condensates, while enrichment of RNA in the dilute phase modulated partitioning in FUS condensates. Favourable client-scaffold interaction energies within condensates were associated with client partitioning, while client-scaffold interactions competed with scaffold self-interactions. Analysis of interaction energies of individual residues revealed that the interactions were localised to specific sequence regions: charged blocks for interactions with MED1, and dispersed aromatic residues for interactions with FUS. Based on these molecular insights, we developed a sequence-based predictor of partitioning trends and applied it to a set of 243 sequences. Our prediction approach for partitioning can be extended to other biomolecules, offering a framework to analyse their partitioning in cellular environments.

biophysics↗

bioRxiv2025

AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level

Deep-learning methods have transformed our ability to predict the three-dimensional structures of folded proteins from sequence, and coarse-grained simulations have made it possible to study intrinsically disordered proteins at the proteome scale. More than half of human proteins, however, contain mixtures of disordered regions and one or more folded domains, and the biological function of these multi-domain proteins depends on the interplay between the folded and disordered regions. Here, we developed AF-CALVADOS, a coarse-grained simulation model that is informed by AlphaFold to model the dynamics of intrinsically disordered proteins and multi-domain proteins containing mixtures of folded and disordered regions. AF-CALVADOS leverages information from AlphaFold 2 to model folded regions that we then integrate with the coarse-grained CALVADOS model. Our automated framework makes it possible to perform simulations of any soluble folded or disordered protein without manually defining the folded regions, enabling scaling to the proteome level. We validate AF-CALVADOS using experimental SAXS data for more than 400 proteins and find that it performs well across proteins with varying amounts of ordered and disordered regions. We demonstrate the scalability of AF-CALVADOS by performing simulations of 12,483 intracellular human proteins and make the data freely available; we envisage that large-scale simulation data generated by AF-CALVADOS can be used to benchmark or train machine learning models for flexible, multi-domain proteins. The conformational ensembles can also be used to study sequence-dynamics-function relationships at scale, and can shed light on the interplay between folded and disordered regions. We exemplify this by analysing the disordered regions in 1,487 human transcription factors.

biophysics↗

bioRxiv2025

Advancing Protein Ensemble Predictions Across the Order-Disorder Continuum

While deep learning has transformed structure prediction for ordered proteins, intrinsically disordered proteins remain poorly predicted due to systematic underrepresentation in training data, despite constituting approximately 30% of eukaryotic proteomes. We introduce PeptoneBench, the first benchmark to enable systematic assessment of ensemble generators for both ordered and disordered proteins, integrating diverse experimental observables. Our analysis reveals that existing evaluation metrics exhibit systematic bias toward the structured spectrum of the proteome. Assessment of popular predictors (AlphaFold2, ESMFlow, Boltz2) confirms high accuracy on ordered proteins but shows performance degradation with increasing disorder. We further present PepTron, a flow-matching ensemble generator trained on data augmented with synthetic disordered protein ensembles. On our benchmark PepTron matches BioEmu on disordered regions while maintaining competitive accuracy on ordered protein benchmarks. Our data augmentation approach demonstrates that targeted training strategies can approach the performance of computationally expensive simulation-based methods, establishing a generalizable framework applicable to other protein generative models. All datasets, models, and code are openly available.

biophysics↗

bioRxiv2025

The importance of UBQLN2 ubiquitylation for its turnover and localization

UBQLN2 is a member of the UBL-UBA domain protein family that functions as extrinsic substrate receptors for the 26S proteasome. UBQLN2 has been shown to undergo phase separation in vitro. In cells, UBQLN2 forms condensates that may be of importance for tuning protein degradation via the ubiquitin-proteasome system and potentially of relevance for UBQLN2-linked amyotrophic lateral sclerosis (ALS). Here we show that UBQLN2 is ubiquitylated on lysine residues in the N-terminal UBL domain. The C-terminal region of UBQLN2 is lysine-depleted, and we show that introducing lysine residues in this region leads to its E6AP-dependent degradation. The UBL domain critically stabilizes UBQLN2 and protects it from proteasomal degradation. Fusion of ubiquitin to the UBQLN2 N-terminus stabilizes UBQLN2 and increases its propensity for locating in puncta, indicating that ubiquitylation of the UBQLN2 UBL domain regulates abundance and localization.

biochemistry↗