Alessandro Pandini

BARMIE predicts chemical binding affinities to steroid receptors across species to identify environmentally relevant chemicals and vulnerable species.

This paper introduces a deep learning method to identify conformational binding signatures in protein dynamics from molecular simulations.

This paper introduces DyNoPy, a method combining coevolution analysis and molecular dynamics to predict functionally important residues in proteins, demonstrating its application on beta-lactamases.

The paper investigates how phosphorylation in CaMKII dimer formation drives conformational changes and allosteric signaling, revealing how mutations disrupt these processes and affect learning.