Florian Kaiser

This study characterizes how aminoacyl-tRNA synthetases distinguish amino acids using computational analysis of crystallographic structures, revealing the role of binding site composition, non-covalent interactions, error correction, and steric effects.

The paper predicts off-targets of a MAPK14 inhibitor using in silico methods and validates them experimentally, achieving a 46% hit rate.

The paper uses AI-based drug screening and biophysics to identify novel PDE2 inhibitors from a chemical library, achieving a 6% hit rate with compounds showing micromolar affinities.

The paper applies GMLVQ to predict early folding residues in proteins, achieving 76.6% AUC and highlighting the importance of secondary structure and hydrophobic networks.

The paper identifies two distinct ATP binding motifs in aminoacyl tRNA synthetases (aaRS) and links their structural features to evolutionary hypotheses about the origin of these enzymes.