Sándor Vajda

This paper shows that focusing on complementarity-determining regions (CDRs) in antibody sequences improves binding affinity prediction using region-aware pretraining strategies.

This study evaluates AlphaFold3's ability to predict cryptic pockets and their ligand binding, showing it can generate accurate structural ensembles when provided with ligands.

This paper defines the molecular basis for TCR peptide recognition by the CIA targeting complex using binding measurements, bioinformatics, and structural modeling, revealing a hierarchy of physicochemical determinants.

The paper uses FTMap to analyze 50 antibody-antigen complexes, finding that paratopes are hot spot-rich and that Trp/Tyr residues drive hot spot formation.

This study evaluates AlphaFold protein models for ligand docking, showing they outperform unbound structures and match ligand-bound structures in fragment docking.

The paper fine-tunes AlphaFold using high-resolution MHC-peptide crystal structures to improve prediction accuracy for MHC-peptide complex structures, outperforming existing methods in RMSD and lDDT scores.

This study enhances AlphaFold for predicting phosphopeptide-protein complex structures, achieving higher accuracy in lDDT scores for peptides.

The paper fine-tunes protein language models on peptide-MHC sequences to predict MHC-peptide binding, outperforming the existing NetMHCpan4.1 method.

Splitting AlphaFold2 complex models, re-docking with ClusPro, AlphaFold2 refinement and predicted-aligned-error ranking raised successful docking-benchmark targets from 88 (five AlphaFold2 models) to 123 (top 10) of 204.