Sebastian Bittrich

This paper introduces a scalable method for searching 3D protein structure similarity across entire proteomes using deep learning and vector databases.

The paper introduces an inverted index method for real-time structural motif searching in the PDB, enabling efficient analysis of 3D protein and nucleic acid structures.

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 introduces a Structural Relevance (SR) score to assess contact importance in protein structure prediction, improving resilience to false positives and reconstruction fidelity.

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 features of Early Folding Residues (EFR) in proteins using structural data, showing they differ from functional residues and provide insights into protein folding and design.

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.