2024/06/02 by Simon Dürr, Ursula Röthlisberger · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Protein Structure and Dynamics #RNA and protein synthesis mechanisms #Computational Drug Discovery Methods
paper · pdf · doi:10.1101/2024.05.28.596236
openalex publication_date 2024/06/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14
Abstract Metals play important roles for enzyme function and many therapeutically relevant proteins. Despite the fact that the first drugs developed via computer aided drug design were metalloprotein inhibitors, many computational pipelines for drug discovery still discard metalloproteins due to the difficulties of modelling them computationally. New “cofolding” methods such as AlphaFold3 (AF3) ( Abramson et al., 2024 ) and RoseTTAfold-AllAtom (RFAA) ( Krishna et al., 2024 ) promise to improve this issue by being able to dock small molecules in presence of multiple complex cofactors including metals or covalent modifications. Here, we analyze the current status for metal ion prediction using these methods. We find that currently only AF3 provides realistic predictions for metal ions, RFAA in contrast does perform worse than more specialized models such as AllMetal3D in predicting the location of metal ions accurately. We find that AF3 predictions are consistent with expected physico-chemical trends/intuition whereas RFAA often also predicts unrealistic metal ion locations.