Biology subjects

Elofsson, A.

Publications and source records attributed to Elofsson, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Conserved mycobacteriophage protein uses structural mimicry to displace host initiation factors

Compact viral genomes encode sophisticated strategies to reprogram host gene expression, often by co-opting rather than replacing the host RNA polymerase. How bacteriophages achieve such control in Mycobacteria remains largely unknown. Here, time-resolved interaction proteomics of mycobacteriophage D29 infection identifies a conserved two-protein module, gp53-gp52, that binds the host RNA polymerase. Affinity purification confirmed association of both proteins with the polymerase, while native mass spectrometry demonstrated that gp53 displaces the host sigma factor. Structural modelling places gp53 at the sigma-binding surface of RNA polymerase, consistent with molecular mimicry of sigma-factor engagement. The module is conserved throughout Cluster A mycobacteriophages and occurs in fused or split genomic architectures. These findings suggest sigma-factor displacement as a mechanism of transcriptional takeover in viruses of mycobacteria and show how a compact viral module can repurpose an essential host molecular machine

microbiology↗

bioRxiv2026

ABAG-Rank: Improving Model Selection of AlphaFold Antibody-Antigen Complexes by Learning to Rank

MotivationAlphaFold has transformed structural biology with an unprecedented accuracy in modeling protein structures and their interactions with biomolecules, with AlphaFold3 (AF3) achieving state-of-the-art performance. However, AF3 and other methods often struggle to accurately predict the structure of protein complexes that lack strong co-evolutionary information, such as antibody-antigen (Ab-Ag) complexes. One of the fundamental issues is that AF3 often generates accurate predictions, but fails to reliably distinguish them from the much larger set of incorrect ones. ResultsTo address this, we propose ABAG-Rank, a deep neural network that provides an efficient and robust solution for model selection of Ab-Ag interactions from a pool of structural ensembles predicted with AlphaFold. Built on the permutation-invariant DeepSets architecture, ABAG-Rank can process variable-sized ensembles of structural decoys and is directly applicable to prediction settings in which the number of candidates may vary. We train a model on a redundancy-reduced set of all known antibody-antigen complexes and find that simple geometric descriptors, along with confidence scores from AlphaFold, provide rich information about interface quality without requiring intensive physics-based calculations. Our experiments demonstrate that ABAG-Rank significantly outperforms AF3 internal scoring and the ranking performance of existing deep learning baselines. ImplementationSource code can be found at: https://github.com/tadteo/ABAG-Rank

bioinformatics↗

bioRxiv2025

Reliable Identification of Homodimers Using AlphaFold

MotivationProtein-protein interactions are central for understanding biological processes. The ability to predict interaction partners is extremely valuable for avoiding costly, time-consuming experiments. It has been shown that AlphaFold has an unsurpassed ability to accurately evaluate interacting protein pairs. However, a protein can also form homomeric interactions, i.e. interact with itself. ResultsWe found that AlphaFold yielded a significantly higher false-positive rate for identifying homodimers than for heterodimers. True Positive Rate (TPR) at 1% False Positive Rate (FPR) drops from 63% for heterodimers to 18% for homodimers. When we investigated the high-scoring false positives, i.e., non-homodimers with high AlphaFold scores when predicted as such, we found that their homologs were enriched for homomultimeric proteins. Using a simple logistic regression model that combines AlphaFold scores with structural and homology information, we increased the TPR (at 1% FPR) to 42{+/-}8% (5-fold cross-validation) from 19%. If we excluded the homology information, we achieved a TPR of 28{+/-}7%, which is still better than using AlphaFold metrics. Availability and implementationAll data are available from Zenodo DOI:10.5281/zenodo.17738668 and all code from https://github.com/SarahND97/alphafold-homodimers. Supplementary informationSupplementary information is available online.

bioinformatics↗

bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗

bioRxiv2025

Evaluating Deep Learning Based Structure Prediction Methods on Antibody-Antigen Complexes

MotivationAlphaFold2 significantly improved the prediction of protein complex structures. However, its accuracy is lower for interactions without coevolutionary signals, such as host-pathogen and antibody-antigen interactions. Two strategies have been developed to address this limitation: massive sampling and replacing the evoformer with the pairformer, which does not rely on coevolution, as introduced in AlphaFold3, thereby enabling more structural reasoning by the network. ResultsIn this study, we benchmark structure prediction methods on unseen antibody-antigen complexes. We found that increased sampling improves the chances of generating a correct protein model, roughly in a log-linear manner. However, the internal quality estimates by AlphaFold often cannot identify the best predicted structures for each target, resulting in a significant loss of performance for the top-ranked protein model compared with the best model. For all methods, a significant challenge remains the identification of the best model. We also show that AlphaFold3 outperforms AlphaFold2, Boltz-1, and Chai-1. Furthermore, AlphaFold3 performance declines significantly for complexes that lack structural similarity to the training set, indicating that it has to some extent learned to detect remote structural similarities. Availability and implementationAll code is available from github.com/samuelfromm/abag-benchmark-set/ and all data from DOI:10.5281/zenodo.17978681. The latter repository also contains the code. Supplementary informationSupplementary information is available online.

bioinformatics↗

bioRxiv2025

Limits of deep-learning-based RNA prediction methods

In recent years, tremendous advances have been made in predicting protein structures and protein-protein interactions. However, progress in predicting RNA structure, either alone or in complex with other macromolecules, has been less prominent, though some recent developments have been reported. It remains unclear whether the improved prediction accuracy is sustained for novel RNA structures. Here, we use an independent benchmark to evaluate the performance of the latest methods. First, we show that state-of-the-art methods can sometimes predict the structure of single-chain RNA strands, with accurate models observed for RNAs with well-defined or regular secondary structures. Next, our evaluation was extended to RNA complexes, where prediction accuracy was notably higher for those involving extensive canonical base pairing. Additionally, a structural similarity analysis revealed that prediction success strongly correlates with resemblance to known structures, indicating that current methods recognise recurring motifs rather than generalising to novel folds. Finally, we also noted that the accuracy estimates for RNA models are far from accurate. Therefore, it is not possible to reliably identify the correctly predicted models with todays methods. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/651414v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@fbdd3eorg.highwire.dtl.DTLVardef@17a31c0org.highwire.dtl.DTLVardef@158391corg.highwire.dtl.DTLVardef@10d78a0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

AlphaFold3 at CASP16

The CASP16 experiment provided the first benchmark for AlphaFold3. In contrast to AlphaFold2 and other methods, AlphaFold3 can also predict the structure of non-protein molecules. In this study, we assess the performance of AlphaFold3 using both automatic server submissions and manual predictions from the Elofsson group. All predictions were generated via the AF3 web server, with manual interventions applied to large targets. Our analysis shows that AF3 performs comparably to top predictors for proteins and complexes, with average GDT_TS and DockQ scores indicating high model quality. Manual curation did not significantly improve results over default AF3 server submissions. Compared to AlphaFold2-based methods, we found that AF3 performs slightly better for protein complexes. Still, when massive sampling is applied to AlphaFold2, the difference disappears, using standard measurements. The performance of the AlphaFold3 server is comparable to the best methods when only taking the top-ranked predictions into account, but slightly behind when examining the best out of the five submitted models. Further, there are many targets where one method makes a good prediction while another top-ranked method fails, indicating that a venue for progress could be to develop better methods for identifying the best models. In the official ranking from CASP, AlphaFold3 performs better than AlphaFold2 for easier targets, but not for harder targets. Finally, RNA predictions remain difficult, and the accuracy of stoichiometry predictions is limited, especially for heteromeric targets. Overall, AlphaFold3 provides an easy-to-use method that offers close to state-of-the-art predictions.

bioinformatics↗

bioRxiv2023

Structural Host-Virus Interactome Profiling of Intact Infected Cells

Virus-host protein-protein interactions (PPIs) are fundamental to viral infections, yet high-resolution identification within the native context of intact infected cells has remained an unsolved challenge. Here, we developed structural host-virus interactome profiling (SHVIP) that combines in situ cross-linking mass spectrometry with the enrichment of newly synthesized viral proteins from infected cells. We established SHVIP using herpes simplex virus type 1 and obtained 739 PPIs based on 6,194 cross-links from productively infected cells. SHVIP captures PPIs across intracellular compartments and at the intact host endomembrane system. It resolves PPIs to the protein domain level and seamlessly integrates with AlphaFold-based structural modeling, facilitating detailed predictions of PPI sites within structured and intrinsically disordered regions. We show that SHVIP captures parts of the virus-host PPI space that are elusive to traditional interaction proteomics approaches. By selectively disrupting several newly identified virus-host PPIs, we confirm SHVIPs ability to uncover genuine virus-host PPIs in the intact complex environment of infected cells.

microbiology↗

bioRxiv2023

Deep learning insights into the architecture of the mammalian egg-sperm fusion synapse

A crucial event in sexual reproduction is when haploid sperm and egg fuse to form a new diploid organism at fertilization. In mammals, direct interaction between egg JUNO and sperm IZUMO1 mediates gamete membrane adhesion, yet their role in fusion remains enigmatic. We used AlphaFold to predict the structure of other extracellular proteins essential for fertilization to determine if they could form a complex that may mediate fusion. We first identified TMEM81, whose gene is expressed by mouse and human spermatids, as a protein having structural homologies with both IZUMO1 and another sperm molecule essential for gamete fusion, SPACA6. Using a set of proteins known to be important for fertilization and TMEM81, we then systematically searched for predicted binary interactions using an unguided approach and identified a pentameric complex involving sperm IZUMO1, SPACA6, TMEM81 and egg JUNO, CD9. This complex is structurally consistent with both the expected topology on opposing gamete membranes and the location of predicted N-glycans not modeled by AlphaFold-Multimer, suggesting that its components could organize into a synapse-like assembly at the point of fusion. Finally, the structural modeling approach described here could be more generally useful to gain insights into transient protein complexes difficult to detect experimentally. Impact statementStructural modeling with AlphaFold-Multimer was used to investigate extracellular protein interactions involved in mammalian egg-sperm recognition, suggesting a putative pentameric complex that includes TMEM81, a sperm protein not previously involved in gamete recognition.

biophysics↗

bioRxiv2023

Enhancing Inter-link Coverage in Cross-Linking Mass Spectrometry through Context-Sensitive Subgrouping and Decoy Fusion

In cross-linking mass spectrometry, sensitivity and specificity in assigning mass spectra to cross-links between different proteins (inter-links) remains challenging. Here, we report on limitations of commonly used concatenated target-decoy searches and propose a target-decoy competition strategy on a fused database as a solution. Further, we capitalize on context-divergent error rates by implementing a novel context-sensitive subgrouping strategy. This approach increases inter-link coverage by [~] 30 - 75 % across XL-MS datasets, maintains low error rates, and preserves structural accuracy.

biochemistry↗

bioRxiv2023

M-Ionic: Prediction of metal ion binding sites from sequence using residue embeddings

MotivationUnderstanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (PLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the PLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal-binding. ResultsOn independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall=84.6%) in distinguishing metal-binding from non-binding proteins compared to AUROC of 0.74 (recall =61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e., Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the PLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. Availability and ImplementationM-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data. Contactarne@bioinfo.se Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

The structural basis of hyperpromiscuity in a core combinatorial network of Type II toxin-antitoxin and related phage defence systems

Toxin-antitoxin (TA) systems are a large group of small genetic modules found in prokaryotes and their mobile genetic elements. Type II TAs are encoded as bicistronic (two-gene) operons that encode two proteins: a toxin and a neutralising antitoxin. Using our tool NetFlax (standing for Network-FlaGs for toxins and antitoxins) we have performed a large-scale bioinformatic analysis of proteinaceous TAs, revealing interconnected clusters constituting a core network of TA-like gene pairs. To understand the structural basis of toxin neutralisation by antitoxins, we have predicted the structures of 3,419 complexes with AlphaFold2. Together with mutagenesis and functional assays, our structural predictions provide insights into the neutralising mechanism of the hyperpromiscuous Panacea antitoxin domain. In antitoxins composed of standalone Panacea, the domain mediates direct toxin neutralisation, while in multidomain antitoxins the neutralisation is mediated by other domains, such as PAD1, Phd-C and ZFD. We hypothesise that Panacea acts as a sensor that regulates TA activation. We have experimentally validated 16 new NetFlax TA systems. We used functional domain annotations and with metabolic labelling assays to predict their potential mechanisms of toxicity (such as disruption of membrane integrity, inhibition of cell division and abrogation of protein synthesis) as well as biological functions (such as antiphage defence). The interactive version of the NetFlax TA network that includes structural predictions can be accessed at http://netflax.webflags.se/. Significance statementToxin-antitoxin systems are enigmatic components of microbial genomes, with their biological functions being a conundrum of debate for decades. Increasingly, TAs are being found to have a role in defence against bacteriophages. By mapping and experimentally validating a core combinatorial network of TA systems and high-throughput prediction of structural interfaces, we uncover the evolutionary scale of TA partner swapping and discover new toxic effectors. We validate the predicted toxin:antitoxin complex interfaces of four TA systems, uncovering the evolutionary malleable mechanism of toxin neutralisation by Panacea-containing PanA antitoxins. We find TAs are evolutionarily related to several other phage defence systems, cementing their role as important molecular components of the arsenal of microbial warfare.

microbiology↗

bioRxiv2022

Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

MotivationDespite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established. ResultsWe analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g., TM-score) but poorly on another (e.g., DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures. AvailabilityAll scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark. Contactarne@bioinfo.se Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Multiplexed selectivity screening of anti-GPCR antibodies

G protein-coupled receptors (GPCRs) control critical cellular signaling pathways. Therapeutic agents, such as antibodies (Abs), are being developed to modulate GPCR signaling pathways. However, validating the selectivity of anti-GPCR Abs is challenging due to sequence similarities of individual receptors within GPCR subfamilies. To address this, we developed a multiplexed immunoassay to test >400 anti-GPCR Abs from the Human Protein Atlas targeting a customized library of 215 expressed and solubilized GPCRs representing all GPCR subfamilies. We found that ~61% of Abs were selective for their intended target, ~11% to bind off-target, and ~28% not to bind any GPCR. Antigens of on-target Abs were, on average, significantly longer, more disordered, and less likely to be buried in the interior of the GPCR protein than the other Abs. These results provide important insights into the immunogenicity of GPCR epitopes and form a basis for the design of therapeutic Abs and the detection of pathological auto-antibodies. TEASERA multiplexed library-to-library selectivity analysis of 400 anti-GPCR antibodies within subfamilies of 200 solubilized receptors.

biochemistry↗

bioRxiv2022

Insights into the structure-function relationship of the NorQ/NorD chaperones from Paracoccus denitrificans reveal shared principles of interacting MoxR AAA+/VWA domain proteins

NorQ, a member of the MoxR-class of AAA+ ATPases, and NorD, a protein containing a Von Willebrand Factor Type A (VWA) domain, are essential for non-heme iron (FeB) cofactor insertion into cytochrome c-dependent nitric oxide reductase (cNOR). cNOR catalyzes the NO reduction, a key step of bacterial denitrification. This work aimed at elucidating the specific mechanism of NorQD-catalyzed FeB insertion, and the general mechanism of the MoxR/VWA interacting protein families. We show that NorQ-catalyzed ATP hydrolysis, an intact VWA-domain in NorD and specific surface carboxylates on cNOR are all features required for cNOR activation. Supported by BN-PAGE, low-resolution cryo-EM structures of NorQ and the NorQD complex show that NorQ forms a circular hexamer with a monomer of NorD binding both to the side and to the central pore of the NorQ ring. Guided by AlphaFold predictions, we assign the density that plugs the NorQ ring pore to the VWA domain of NorD with a protruding finger inserting through the pore, and suggest this binding mode to be general for MoxR/VWA couples. We present a tentative model for the mechanism of NorQD-catalyzed cNOR remodelling and suggest many of its features to be applicable to the whole MoxR/VWA family.

biochemistry↗

bioRxiv2022

A grappling hook interaction balances self-assembly and chaperone activity of Nucleophosmin 1

How the self-assembly of partially disordered proteins generates functional compartments in the cytoplasm and particularly in the nucleus is poorly understood. Nucleophosmin 1 (NPM1) is an abundant nucleolar protein that forms large oligomers which provide the scaffold for ribosome assembly but also prevent protein aggregation as part of the cellular stress response. Examining the relationship between the self-assembly and chaperone activity of NPM1, we find that oligomerization of full-length NPM1 modulates its ability to retard amyloid formation in vitro. Machine learning and cryo-electron microscopy reveal fuzzy interactions between the disordered region and the C-terminal nucleotide-binding domain that cross-link NPM1 pentamers into oligomers. Ribosomal peptides mediate in a tighter association within the oligomers, reducing their capacity to prevent amyloid formation. We conclude that NPM1 uses a "grappling hook" interaction to form a network-like structure whose chaperone activity is tuned by basic proteins, suggesting a regulatory mechanism for the nucleolar stress response.

biophysics↗

bioRxiv2022

EvoBind: in silico directed evolution of peptide binders with AlphaFold

Currently, there is no accurate method to computationally design peptide binders towards a specific protein interface using only a target structure. Experimental methods such as phage display can produce strong binders, but it is impossible to know where these bind without solving the structures. Using AlphaFold2 (AF) and other AI methods to distinguish true binders has proven highly successful but relies on the availability of binding scaffolds. Here, we develop EvoBind, an in silico directed-evolution platform based on AF that designs peptide binders towards an interface using only sequence information. We show that AF can distinguish between native and mutated peptide binders using the plDDT score and find that AF adapts the receptor interface structure to the binders during optimisation. We analyse previously designed minibinder proteins and show that AF can distinguish designed binders from non-binders. We compare ELISA ratios of different peptide binders and find the affinity can not be distinguished among binders, possibly due to varying binding sites and low AF confidence. We test the recovery of binding motifs and find that up to 75% of motifs are recovered. In principle, EvoBind can be used to design binders towards any interface conditioned on if AF can predict these. We expect that EvoBind will aid experimentalists substantially, providing a starting point for further laboratory analysis and optimisation. We hope that the use of AI-based methods will come to make binder design significantly cheaper and more accurate in tackling unmet clinical needs. EvoBind is freely available at: https://colab.research.google.com/github/patrickbryant1/EvoBind/blob/master/EvoBind.ipynb

bioinformatics↗

bioRxiv2022

Complementing machine learning-based structure predictions with native mass spectrometry

The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) has moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user-provided input and may therefore require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations, that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on the small and the large scale.

biophysics↗