Antonín Kunka

This study uses systematic K-to-Q mutagenesis to probe the energy landscape of α-Synuclein aggregation, identifying core residue mutations that alter free energy pathways and fibril polymorphism.

This study shows that terminal truncations of α-synuclein significantly alter phase separation, while familial mutations have minimal effects, influencing amyloid fibril formation and condensate behavior.

This study designs polymorph-specific binders for α-synuclein fibrils using a computational pipeline, demonstrating their utility as selective labeling agents without interfering with fibril dynamics.

This study shows that the thermodynamic stability of alpha-synuclein fibrils affects their disaggregation by the chaperone system HSP70, DNAJB1, and Apg2, with more stable fibrils being less efficiently disaggregated.

This paper enhances fluorinase catalytic efficiency by engineering mutants and optimizing reaction conditions, achieving a 60-fold increase in turnover rate.

This paper develops a deep neural network predictor, AggreProt, to identify aggregation-prone regions in proteins and demonstrates its utility in designing mutations to suppress aggregation.

The paper investigates how the C112R mutation in ApoE4 causes aggregation and shows that tramiprosate reduces this aggregation.