Mihaly Badonyi

The paper introduces Pinc, a simple probabilistic score to assess interface confidence in AlphaFold models, showing it matches or exceeds complex methods' performance.

The paper introduces MES, a metric to evaluate variant effect predictors and MAVEs based on clinical evidence yield, showing discrepancies with AUROC.

This study determines the structural morphology and formation of the human exocyst complex, revealing an extended architecture with minimal intersubunit interaction and a single nucleation site.

This study shows that SS18::SSX can drive transcription independently of BAF complex interaction and relies on EP300 for gene activation, suggesting new therapeutic targets for synovial sarcoma.

This study combines MAVEs and population-free VEPs to improve variant classification across ancestries in hereditary cancer genes, reclassifying over 90% of variants of uncertain significance.

The paper develops a protein structure-based mLOF score to predict loss-of-function mechanisms and estimates the prevalence of dominant-negative and gain-of-function mechanisms in Mendelian disease genes.

The paper introduces acmgscaler, an R package and Colab notebook for calibrating gene-level variant effect scores within the ACMG/AMP framework using MAVEs and VEPs.

The paper shows that disease-associated variants in alternative protein isoforms can cause structural mutations, emphasizing the need to consider tissue-specific expression and full-length transcripts in variant interpretation.

The paper investigates epitope effects in pQTL studies, finding that only 12% or less of cis-pQTL results are influenced by missense-mediated epitope effects.

This study evaluates 70 variant effect predictors for RyR1 missense variants and introduces a structural metric, SPDV, to assign ACMG/AMP evidence levels for clinical interpretation.

The paper introduces a statistical model to predict dominant-negative, gain-of-function, and loss-of-function mechanisms of protein-altering mutations in human genes and validates its performance on unseen data.

This study evaluates ancestry bias in variant effect predictors and finds disparities in their performance across different populations.

The paper analyzes cancer mutations in protein structures to identify novel driver genes by distinguishing mutational patterns in tumour suppressors and oncogenes.

This study investigates EZH2 variants linked to Weaver syndrome, showing they cause dominant negative effects on H3K27 methylation, altering chromatin states and contributing to developmental disorders.

The paper shows that cotranslational protein complex assembly is less common for subunits with dominant-negative mutations and develops a model to predict such mutations.

The paper shows that large protein interfaces evolve to facilitate cotranslational assembly by comparing interface sizes in bacterial, yeast, and human complexes.