Classical molecular dynamics (MD) simulations are widely used to inspect the behavior of zinc(II)-proteins at the atomic level, hence the need to properly model the zinc(II) ion and the interaction with its ligands. Different approaches have been developed to represent zinc(II) sites, with the bonded and nonbonded models being the most used. In the present work, we tested the well-known zinc AMBER force field (ZAFF) and a recently developed nonbonded force field (NBFF) to assess how accurately they reproduce the dynamic behavior of zinc(II)-proteins. For this, we selected as benchmark six zinc-fingers. This superfamily is extremely heterogenous in terms of architecture, binding mode, function, and reactivity. From repeated MD simulations, we computed the order parameter (S2) of all backbone N-H bond vectors in each system. These data were superimposed to heteronuclear Overhauser effect measurements taken by NMR spectroscopy. This provides a quantitative estimate of the accuracy of the FFs in reproducing protein dynamics, leveraging the information about the protein backbone mobility contained in the NMR data. The correlation between the MD-computed S2 and the experimental data indicated that both tested FFs reproduce well the dynamic behavior of zinc(II)-proteins, with comparable accuracy. Thus, along with ZAFF, NBFF represents a useful tool to simulate metalloproteins with the advantage of being extensible to diverse systems such as those bearing dinuclear metal sites.

Thirty-eight percent of protein structures in the Protein Data Bank contain at least one metal ion. However, not all these metal sites are biologically relevant. Cations present as impurities during sample preparation or in the crystallization buffer can cause the formation of protein-metal complexes that do not exist in vivo. We implemented a deep learning approach to build a classifier able to distinguish between physiological and adventitious zinc-binding sites in the 3D structures of metalloproteins. We trained the classifier using manually annotated sites extracted from the MetalPDB database. Using a 10-fold cross validation procedure, the classifier achieved an accuracy of about 90%. The same neural classifier could predict the physiological relevance of non-heme mononuclear iron sites with an accuracy of nearly 80%, suggesting that the rules learned on zinc sites have general relevance. By quantifying the relative importance of the features describing the input zinc sites from the network perspective and by analyzing the characteristics of the MetalPDB datasets, we inferred some common principles. Physiological sites present a low solvent accessibility of the aminoacids forming coordination bonds with the metal ion (the metal ligands), a relatively large number of residues in the metal environment (≥20), and a distinct pattern of conservation of Cys and His residues in the site. Adventitious sites, on the other hand, tend to have a low number of donor atoms from the polypeptide chain (often one or two). These observations support the evaluation of the physiological relevance of novel metal-binding sites in protein structures.