Author

Hedvig Tordai

Semmelweis UniversityORCID 0000-0002-0875-5569Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:protein sequence· 2 Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Organism:E. coli· 1

Frequent Co-Authors

Tamás HegedűsiD· 3 shared Andrea Balogh-Molnar· 1 shared Bálint Kiss· 1 shared Bence FehériD· 1 shared Dominik SziklaiiD· 1 shared Erda QorriiD· 1 shared Erzsébet SuhajdaiD· 1 shared Gergely L. LukácsiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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3 shared papersBioinformatics

Tamás HegedűsORCID

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Author profile →

4 papers

Recent·Most cited

pLM-SAV: A Δ-Embedding Approach for Predicting Pathogenic Single Amino Acid Variants

Orsolya Gereben, Hedvig Tordai, Lana Khamisi, Erda Qorri, Tamás Hegedűs

pLM-SAV predicts pathogenic SAVs using delta-embeddings from protein language models, achieving high performance on labeled data and improving ambiguous predictions over AlphaMissense.

Bioinformatics Advances · Jan 1, 2026Peer-reviewedBioinformatics

Flexible Motion of T7 bacteriophage Tail Fibers Suggest a Dynamic Viral Infection Mechanism

Luca Elizabet Kosik, Miklós Cervenak, Dominik Sziklai, Andrea Balogh-Molnar, Negar Rahimi, Bence Fehér, Soma Yamamoto, Hiroki Konno, Noriyuki Kodera, Heinz Wilfried Amenitsch, Hedvig Tordai, Levente Herényi, Miklós Kellermayer, Bálint Kiss

This study uses HS-AFM and MD simulations to show that T7 bacteriophage tail fibers have flexible regions enabling dynamic host recognition and surface exploration.

bioRxiv · Oct 22, 2025 · PreprintBiophysics

Lightway access to AlphaMissense data that demonstrates a balanced performance of this missense mutation predictor

Hedvig Tordai, Odalys Torres, Máté Csepi, Rita Padányi, Gergely L. Lukács, Tamás Hegedűs

The paper evaluates AlphaMissense's performance in predicting pathogenicity of missense variants across diverse protein groups and introduces a web resource for access.

bioRxiv · Oct 30, 2023 · PreprintBioinformatics

Comprehensive collection and prediction of ABC transmembrane protein structures in the AI era of structural biology

Hedvig Tordai, Erzsébet Suhajda, Ian Sillitoe, Sreenath Sasidharan Nair, Mihály Váradi, Tamás Hegedűs

The paper collects and predicts structures of ABC transmembrane proteins using AlphaFold2 and presents them via a web application.

International Journal of Molecular Sciences · Aug 9, 2022Peer-reviewedBioinformatics