Emel Timuçin

This study benchmarks AlphaFold2 and OpenFold3 on protein-peptide complexes, finding AlphaFold2 outperforms OpenFold3 in success rate and high-quality model proportion.

This study identifies ULK1-linked mitophagy as a mechanism enabling cardiac hypoxia tolerance in blind mole-rats through metabolic and mitochondrial adaptations.

This study identifies NMD-escape variants as a distinct class of disease-causing mutations, showing that altered C-terminal regions drive pathogenicity through phase separation.

This study maps aggregation-prone regions in the myocilin OLF domain using molecular dynamics simulations to identify potential therapeutic targets for POAG.

This paper evaluates AI methods for predicting antibody-antigen complexes, introduces AntiConf as a confidence metric, and shows it improves precision and recall for these predictions.

AFFIPred uses AlphaFold2 structures and machine learning to predict the pathogenicity of missense variants, achieving performance comparable to existing methods without relying on experimental structures.

This study uses computational modeling to investigate the structural dynamics of IL37 complexes and their interactions with IL18 signaling components, revealing key structural features that explain IL37's anti-inflammatory function.

This study identifies OipA interaction partners in H. pylori infection using proteomics and molecular modeling, revealing potential therapeutic targets for combating the infection.

This paper identifies five IL37 mutations that disrupt dimer formation while preserving monomer structure, aiding in the design of anti-inflammatory therapeutics.

This study identifies biases in protein stability datasets caused by concentrated neutral mutations and proposes under-sampling to balance ΔΔG distributions, improving predictor performance.