Metal ions or cofactors are crucial to a broad range of functions within proteins, and a number of research groups have been working to design or modify metal binding sites in proteins to tune the properties of a metal site, discover structure-function relationships, or to introduce new functions. Recently, a number of computational tools have been developed to identify sequences and/or structures favorable for metal binding, but that are not designed specifically to be applied in the context of metalloprotein engineering . This study has performed an analysis on twelve different metal binding sites within the protein data bank, and discovered distinguishing structural features on the basis of metal-ligating amino acid backbone structure alone that can be incorporated into machine-learning algorithms. Clustering analysis has been employed to distinguish different structural classes of the same metal binding site, while classification algorithms can distinguish between non-metal sites and a range of different metal-binding sites. The analyses could easily be employed in metalloprotein design applications.

Applying machine-learning to classify metal-binding sites in protein structures

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.