Jakub Lála

The paper validates and analyzes 12,000 AI-designed CAR-T binders from a global competition, identifying successful designs and factors improving their performance.

The paper reports on a CAR-T therapy binder design competition where their BAGEL-CAR designs achieved high enrichment in experimental screening, highlighting the importance of validating design pipeline outputs.

The paper introduces a hybrid method using a protein language model and Monte Carlo sampling to generate enzyme mutants that preserve structural features while varying overall sequence, providing sequences for experimental testing.

The paper uses a PLM-guided energy landscape approach to design compact enzymes with preserved catalytic site structures, validated through structure prediction and molecular dynamics simulations.

BAGEL enables flexible protein design by exploring an energy landscape through a modular framework, supporting diverse design goals and integrating deep learning models.

This paper designs peptides that bind to intrinsically disordered epitopes by inducing structured domains through a protein-folding algorithm and Monte Carlo optimization.