Biology subjects

Badonyi, M.

Publications and source records attributed to Badonyi, M..

15 recordsLinked to original sources

bioRxiv2026

Clinical evidence yield as a framework for evaluating computational predictors and multiplexed assays of variant effect

Interpreting the clinical significance of missense variants of uncertain significance (VUS) remains a major challenge in clinical genetics. Although computational variant effect predictors (VEPs) and multiplexed assays of variant effect (MAVEs) can generate large-scale functional scores, their value is typically assessed using discrimination metrics such as AUROC rather than by the strength of evidence they provide under ACMG/AMP guidelines. Here, we introduce mean evidence strength (MES), a quantitative metric that summarises the pathogenic and benign evidence assigned across missense variants following gene-level Bayesian calibration. Using the acmgscaler framework, we calibrated 12 population-free VEPs across 367 disease genes and analysed 15 MAVE datasets with sufficient clinical data. MES revealed important discrepancies with AUROC, including cases where methods with similar discrimination differed substantially in evidence yield. MAVEs achieved high average MES despite lower AUROC, while several VEPs showed strong discrimination but more limited calibrated evidence. Among predictors, CPT-1 achieved the highest MES and provided moderate or stronger evidence for the largest fraction of ClinVar VUS. MES therefore provides a practical framework for evaluating computational and experimental variant effect datasets in terms of calibrated clinical evidence yield.

bioinformatics↗

bioRxiv2026

Pinc: a simple probabilistic AlphaFold interaction score

AlphaFold has enabled large-scale prediction of protein-protein and protein-nucleic acid complexes, but ranking and assessing the quality of predicted models remain challenging. Existing confidence scores are often highly parametrised and provide limited interpretability. We introduce a simple geometric framework that converts AlphaFold predicted aligned error (PAE) into conditional contact probability. We show that these probabilities are well calibrated to the fraction of native contacts observed across experimentally determined structures. Motivated by this, we define the Pinc score (Probability of interface native contacts) as the mean contact probability between interacting chains. Because the probabilistic interpretation extends to individual residues, Pinc captures local structural constraint beyond interfacial burial, enabling residue-level prioritisation of hotspot positions for mutational studies. Depending solely on a single empirically fixed contact radius, Pinc offers an interpretable path from PAE to interface confidence, matching or exceeding the classification performance of more complex methods across five independent benchmark sets. We provide a portable, dependency-free C program and a Google Colab notebook for calculating Pinc scores for AlphaFold models at https://git.mpi-cbg.de/tothpetroczylab/Pinc.

bioinformatics↗

bioRxiv2026

BAF complex-independent gene activation by SS18::SSX

In synovial sarcoma, the BAF subunit SS18 is fused to SSX, a transcriptional repressor, generating the oncogenic SS18::SSX fusion protein. Incorporation of SS18::SSX into BAF complexes leads to their aberrant retargeting to Polycomb-repressed genes via SSX, while simultaneously altering their composition and activity. The presence of BAF at Polycomb target sites is widely assumed to be essential for gene activation. Here, we directly tested the requirement for BAF activity in synovial sarcoma cell survival and SS18::SSX-driven transcription. Using targeted degradation of BAF ATPase subunits and deletion of core components, we show that BAF loss has modest effects on sarcoma cell viability and does not impede SS18::SSX target gene expression. Consistently, deletion of the BAF ATPase subunit Smarca4 does not impair SS18::SSX-driven tumor formation in vivo. Using domain-specific SS18::SSX mutants, we further demonstrate that the fusion can activate oncogenic transcription independently of BAF interaction, and that this activity depends on the C-terminal QPGY-rich domain of SS18. Mechanistically, SS18::SSX promotes transcription by engaging the histone acetyltransferase EP300, independently of BAF. Accordingly, pharmacologic degradation of EP300/CREBBP suppresses SS18::SSX-driven transcriptional programs and impairs synovial sarcoma cell survival. Together, these findings challenge the view that BAF activity is required for SS18::SSX-mediated transcriptional activation and demonstrate that aberrant Polycomb target gene expression is sustained through recruitment of transcriptional coactivators in the absence of BAF. Our work reveals new therapeutic vulnerabilities in synovial sarcoma and suggests broader relevance for targeting coactivator-dependent transcription in fusion-driven cancers. HighlightsO_LIBAF degradation does not alter SS18::SSX-activated transcriptional programs C_LIO_LIDirect SS18::SSX transcriptional activation is independent of BAF interaction C_LIO_LIThe SS18 C-terminus engages the co-activator EP300 to promote gene expression C_LIO_LISmall molecule degraders of EP300/CREBBP abolish SS18::SSX-mediated transcription C_LI

cancer biology↗

bioRxiv2025

Disease-associated genetic variants can cause mutations in tissue-specific protein isoforms

Genetic variants can cause protein-coding mutations that result in disease. Variants are typically interpreted using the reference transcript for a gene. However, most human multi-exon genes encode alternative isoforms. Here, we show that coding exons in alternative isoforms harbour more population variants than exons of reference isoforms, consistent with their reduced evolutionary constraint, and that these variants are more likely to cause nonsynonymous coding mutations. Common and rare disease-associated variants mapping to alternative transcripts can lead to amino acid substitutions predicted to be structurally damaging in the corresponding protein isoform. The alternative transcripts to which disease-associated variants map demonstrate high tissue-specific expression, with many unannotated in reference human genomes, revealed only by long-read RNA-sequencing. As an example, we report an unannotated alternative transcript of the inflammasome regulator DPP9 that is lung epithelium-specific and which harbours a common genetic variant associated with severe COVID-19 and lung fibrosis. The variant causes a p.Leu8Pro missense mutation in an alternative first exon, predicted to disrupt the encoded alpha helix. These findings highlight the importance of considering alternative isoforms, their tissue-specific expression, and full-length transcripts in variant interpretation, with implications for uncovering underappreciated mechanisms of both common and rare disease.

genomics↗

bioRxiv2025

Epitope Effect Prevalence in Affinity-based pQTL studies

Affinity-based proteomics platforms Olink and SomaScan have enabled population-wide human proteogenomic studies, linking genetic variants to protein abundances. However, sequence alterations in the binding site of the detection reagent may introduce platform-specific measurement bias unrelated to protein levels, referred to as the epitope effect. In this study, we investigated the prevalence of epitope effects using cis protein quantitative trait loci (pQTL) discovered in three of the largest proteogenomic studies: UK Biobank, deCODE, and Fenland. Across 5,817 protein targets assayed in these studies, cis-pQTL were identified for 914 proteins by both platforms, 301 (33%) of which were linked to a missense variant. We identified 37 proteins with opposing effect directions in two platforms for the same missense pQTL, and 85 proteins where a missense pQTL was significant in only one platform. We present examples where such discrepancies reflect differences in isoform or proteoform targeting, as well as examples where the discordance appears to result from true platform-specific detection bias. Further structural analyses reveal that missense cis-pQTL are more likely to be detected when they alter residues located on accessible protein surfaces - regions most likely to interfere with reagent binding in affinity-based assays. Overall, our findings suggest that missense-mediated epitope effects influence only a minority (12% or less) of cis-pQTL results. We also highlight the need for detailed assay annotations and structural context to improve result interpretation in proteogenomic studies.

genomics↗

bioRxiv2025

acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework

SummaryA genome-wide variant effect calibration method was recently developed under the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP), following ClinGen recommendations for variant classification. While genome-wide approaches offer clinical utility, emerging evidence highlights the need for gene- and context-specific calibration to improve accuracy. Building on previous work, we have developed an algorithm tailored to converting functional scores from both multiplexed assays of variant effects (MAVEs) and computational variant effect predictors (VEPs) into ACMG/AMP evidence strengths. Our method is designed to deliver consistent performance across different genes and score distributions, with all variables adaptively determined from the input data, preventing selective adjustments or overfitting that could inflate evidence strengths beyond empirical support. To facilitate adoption, we introduce acmgscaler, a lightweight R package and a plug-and-play Google Colab notebook for the calibration of custom datasets. This algorithmic framework bridges the gap between MAVEs/VEPs and clinically actionable variant classification. AvailabilityThe package and the Colab notebook are available on GitHub: https://github.com/badonyi/acmgscaler. Supplementary dataSupplementary data can be downloaded from the OSF repository https://osf.io/7hjnm.

bioinformatics↗

bioRxiv2025

Prevalence of loss-of-function, gain-of-function and dominant-negative mechanisms across genetic disease phenotypes

Molecular disease mechanisms caused by mutations in protein-coding regions are diverse, but they can be broadly categorised into loss-of-function (LOF), gain-of-function (GOF), and dominant-negative (DN) effects. Accurately predicting these mechanisms is a pressing clinical need, as therapeutic strategies must align with the underlying disease mechanism. Moreover, computational predictors tend to perform less well at the identification of pathogenic GOF and DN variants. Here, we develop a protein structure-based missense LOF (mLOF) likelihood score that can separate recessive LOF and dominant LOF from alternative disease mechanisms. Using mLOF scores, we estimated the prevalence of molecular mechanisms across 2,837 phenotypes in 1,979 Mendelian disease genes, finding that DN and GOF mechanisms account for 48% of phenotypes in dominant genes. Applying mLOF scores to genes with multiple phenotypes revealed widespread intragenic mechanistic heterogeneity, with 43% of dominant and 49% of mixed-inheritance genes harbouring both LOF and non-LOF mechanisms. Furthermore, we show that combining mLOF scores with phenotype semantic similarity enables the prioritisation of DN mechanisms in mixed-inheritance genes. Our structure-based approach, accessible via a Google Colab notebook, offers a scalable tool for predicting disease mechanisms and advancing personalised medicine.

bioinformatics↗

bioRxiv2024

Structural extension of the human exocyst is enabled by a minimal interface

In multicellular organisms, the machinery responsible for polarized trafficking directs constitutive cargo secretion at distinct sites of the plasma membrane, cilia, and junctional structures. Central to this machinery is the exocyst complex, which tethers cargo vesicles to their destination membrane, alongside other intracellular membrane tethering roles. Precisely how the exocyst spatially integrates membranes and membrane resident binding partners is unclear. Here, we address the structural morphology and formation of the human exocyst complex. Through structural approaches coupled to predictive models, we determined that the exocyst and its subcomplexes have extended arm-like structures that help maximize its reach. Moreover, we demonstrate minimal intersubunit interaction, in contrast to prior models. Nucleation of the holocomplex occurs through a single site, explaining its spatial extension. Our results provide the biochemical basis for exocyst complex assembly, suggesting an ornate extended architecture. SignificanceCargo transport to the eukaryotic cell plasma membrane predominantly relies on the exocyst as a central, signal-integrating polarized trafficking complex. How this single cargo vesicle tethering complex can target diverse vesicles to destination membrane is poorly understood, partly due to inconsistent structural models. Here, we show an extended architecture of the human exocyst complex, revealing a minimal nucleation interface between its two subcomplexes. The distinct morphology of human exocyst, compared to yeast models, suggests a novel mechanism that supports its versatile role in membrane trafficking.

biochemistry↗

bioRxiv2024

Pervasive ancestry bias in variant effect predictors

Variant effect predictors (VEPs) - computational tools that assess the potential impact of genetic variants - have become increasingly vital for clinical variant interpretation. Currently, most VEPs used in variant prioritisation have been trained on datasets of clinically curated or population-derived variants. These datasets, however, disproportionately represent individuals of European descent. We hypothesised that this bias may lead to unequal VEP performance across different populations. To test this, we evaluated the scoring patterns of 52 VEPs for missense variants across 14 ancestry groups. We observe striking disparities: some VEPs predict a markedly higher proportion of damaging variants in underrepresented populations, such as those of Malay descent, compared to individuals of European ancestry. In contrast, VEPs that do not rely on clinical or population data predict more consistent pathogenicity burdens across ancestry groups. Moreover, we could closely link these discrepancies across methods to biases in training data. Our findings underscore the urgent need to adopt tools that minimise ancestry bias to ensure fairer and more accurate variant effect prediction and genetic diagnoses for all populations.

bioinformatics↗

bioRxiv2024

Protein structural context of cancer mutations reveals molecular mechanisms and identifies novel candidate driver genes

Advances in structure determination and computational modelling are enabling us to study the protein structural context of human genetic variants at an unprecedented scale. Here, we investigate millions of human cancer-associated missense mutations in terms of their structural locations and predicted perturbative effects. We find that, while cancer-driving mutations have properties similar to other known disease-causing mutations, this is obscured by the abundance of passenger mutations in cancer sequencing datasets. Nevertheless, by considering the collective properties of mutations at the level of individual proteins, we identify distinct mutational signatures associated with tumour suppressors and oncogenes. Tumour suppressors are enriched in structurally damaging mutations, consistent with loss-of-function mechanisms. In contrast, oncogene mutations tend to be structurally mild, reflecting selection for gain-of-function driver mutations and against loss-of-function mutations. Although oncogenes are difficult to distinguish from genes with no role in cancer using only structural damage, we find that an alternate metric based on the clustering of mutations in three-dimensional space is highly predictive of oncogenes, particularly when mutation recurrence is considered. These observations allow us to identify novel candidate driver genes and speculate about their molecular roles, which we expect to have general utility in the analysis of cancer sequencing data.

bioinformatics↗

bioRxiv2023

ALDH1A3-acetaldehyde re-wires neural crest stem cell and high metabolism states to potentiate melanoma heterogeneity

Cancer cellular heterogeneity and therapy resistance arise substantially from metabolic and transcriptional adaptations, but how these are interconnected is poorly understood. Here, we show that in melanoma, the cancer stem cell marker aldehyde dehydrogenase 1A3 (ALDH1A3) forms an enzymatic partnership with acetyl-CoA synthetase 2 (ACSS2) in the nucleus to couple high glucose metabolic flux with acetyl-histone H3 modification of neural crest lineage and glucose metabolism genes. Importantly, we show acetaldehyde is a metabolite source for acetyl-histone H3 modification in an ALDH1A3, dependent manner providing a physiologic function for this highly volatile and toxic metabolite. In a zebrafish model of melanoma residual disease, a subpopulation of ALDH1-high cells emerges following BRAF inhibitor treatment and targeting these with an ALDH1 suicide inhibitor, nifuroxazide, delays or prevents BRAF inhibitor drug-resistant relapse. Our work reveals that the ALDH1A3-ACSS2 couple directly coordinates nuclear acetaldehyde-acetyl-CoA metabolism with specific chromatin-based gene regulation and represents a potential therapeutic vulnerability in melanoma. HighlightsO_LIALDH1A3-high melanomas are in a high glucose metabolic flux and neural crest stem cell dual state. C_LIO_LINuclear ALDH1A3 partners with ACSS2 to promote selective acetyl-histone H3. C_LIO_LIAcetaldehyde is an acetyl source for ALDH1A3 dependent histone H3 acetylation. C_LIO_LIALDH1A3 is a master regulator and pharmaceutical target for melanoma heterogeneity. C_LI

cancer biology↗

bioRxiv2023

Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases

Many dominant genetic disorders result from protein-altering mutations, acting primarily through dominant-negative (DN), gain-of-function (GOF), and loss-of-function (LOF) mechanisms. Deciphering the mechanisms by which dominant diseases exert their effects is often experimentally challenging and resource intensive, but is essential for developing appropriate therapeutic approaches. Diseases that arise via a LOF mechanism are more amenable to be treated by conventional gene therapy, whereas DN and GOF mechanisms may require gene editing or targeting by small molecules. Moreover, pathogenic missense mutations that act via DN and GOF mechanisms are more difficult to identify than those that act via LOF using nearly all currently available variant effect predictors. Here, we introduce a tripartite statistical model made up of support vector machine binary classifiers trained to predict whether human protein-coding genes are likely to be associated with DN, GOF, or LOF molecular disease mechanisms. We test the utility of the predictions by examining biologically and clinically meaningful properties known to be associated with the mechanisms. Our results strongly support that the models are able to generalise on unseen data and offer insight into the functional attributes of proteins associated with different mechanisms. We hope that our predictions will serve as a springboard for researchers studying novel variants and those of uncertain clinical significance, guiding variant interpretation strategies and experimental characterisation. Predictions for the human UniProt reference proteome are available at https://osf.io/z4dcp/.

genetics↗

bioRxiv2022

Buffering of genetic dominance by allele-specific protein complex assembly

Protein complex assembly often begins while at least one of the subunits is still in the process of being translated. When such cotranslational assembly occurs for homomeric complexes, made up of multiple copies of the same subunit, this will result in complexes whose subunits were translated off of the same mRNA in an allele-specific manner. It has therefore been hypothesised that cotranslational assembly may be able to counter the assembly-mediated dominant-negative effect, whereby the co-assembly of mutant and wild-type subunits "poison" the activity of a protein complex. Here, we address this, showing first that subunits that undergo cotranslational assembly are much less likely to be associated with autosomal dominant relative to recessive disorders. Moreover, we observe that subunits with dominant-negative disease mutations are significantly depleted in cotranslational assembly compared to those associated with loss-of-function mutations. Consistent with this, we also find that complexes with known dominant-negative effects tend to expose their interfaces late during translation, lessening the likelihood of cotranslational assembly. Finally, by combining protein complex properties with other protein-level features, we trained a computational model for predicting proteins likely to be associated with dominant-negative or gain-of-function molecular mechanisms, which we believe will be of considerable utility for protein variant interpretation.

genetics↗

bioRxiv2022

Cell overgrowth during G1 arrest triggers an osmotic stress response and chronic p38 activation to promote cell cycle exit

Cell size and the cell cycle are intrinsically coupled and abnormal increases in cell size are associated with senescence. The mechanism by which overgrowth primes cells to exit the cell cycle remains unclear. We investigate this using CDK4/6 inhibitors that arrest cell cycle progression in G0/G1 and are used to treat ER+/HER2-metastatic breast cancer. We demonstrate that long-term CDK4/6 inhibition promotes cellular overgrowth during the G0/G1 arrest, causing widespread proteome remodeling and p38-p53-p21-dependent cell cycle exit. Cell cycle exit is triggered by two waves of p21 induction. First, overgrowth during a G0/G1 arrest induces an osmotic stress response, producing the first wave of p21 induction. Second, when CDK4/6 inhibitors are removed, a fraction of cells escape G0/G1 arrest and enter S-phase where overgrowth-driven replication stress results in a second wave of p21 induction that causes cell cycle withdrawal from G2, or the subsequent G1. This could explain why cellular hypertrophy is associated with senescence and why CDK4/6 inhibitors have long-lasting anti-proliferative effects in patients.

cell biology↗

bioRxiv2021

Interface size drives cotranslational assembly of protein complexes

Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromers in bacterial, yeast, and human complexes. We detect a clear preference in N-terminal interfaces, which will be translated first and thus more likely to form cotranslationally, to be larger than C-terminal interfaces, suggesting that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding.

molecular biology↗