Richard Dustin Schaeffer

The paper classifies archaeal proteomes at the domain level, revealing that structural diversity is conserved across archaea and that unclassified proteins are due to classification limitations, not structural novelty.

The paper develops TriCyP, a model to predict cysteine states in protein structures, achieving high accuracy and identifying new biological patterns and protein families.

The paper classifies over 542,000 Swiss-Prot protein structure predictions into ECOD domains, expanding domain coverage and revealing novel evolutionary groups.

This paper evaluates progress in predicting protein complex structures in CASP16, finding moderate improvements but ongoing challenges in model ranking, stoichiometry prediction, and handling difficult targets.

This paper assesses CASP16 monomer structure prediction, showing near-solution for single-domain folds but highlighting challenges in truncated sequences and model ranking.

This paper uses AI to predict how post-translational modifications affect drug binding by modeling PTM-modified protein structures and ligand interactions.

DrugDomain 2.0 maps protein domain-ligand interactions across the Protein Data Bank, integrating experimental and AI-predicted data for over 37,000 ligands and 7,560 drugs.

DrugDomain provides a database of interactions between DrugBank molecules and ECOD domains of human proteins, using experimental structures and AlphaFold models.

The paper classifies proteins from Salmonella enterica pangenome, identifies potentially pathogenic proteins, and reveals their clustering in the genome.

The paper classifies domains in 48 proteomes from the AlphaFold Database using DPAM, finding differences in domain composition between eukaryotes and bacteria.

DPAM automatically parses AlphaFold models to recognize and classify protein domains, outperforming existing methods in accuracy and boundary assignment.

The paper introduces a 3-track network for accurate protein structure and interaction prediction, achieving performance close to DeepMind's CASP14 results and enabling rapid modeling of protein complexes from sequence data.