A database overview of metal-coordination distances in metalloproteins.

Metalloproteins are ubiquitous in all living organisms and take part in a very wide range of biological processes. For this reason, their experimental characterization is crucial to obtain improved knowledge of their structure and biological functions. The three-dimensional structure represents highly relevant information since it provides insight into the interaction between the metal ion(s) and the protein fold. Such interactions determine the chemical reactivity of the bound metal. The available PDB structures can contain errors due to experimental factors such as poor resolution and radiation damage. A lack of use of distance restraints during the refinement and validation process also impacts the structure quality. Here, the aim was to obtain a thorough overview of the distribution of the distances between metal ions and their donor atoms through the statistical analysis of a data set based on more than 115 000 metal-binding sites in proteins. This analysis not only produced reference data that can be used by experimentalists to support the structure-determination process, for example as refinement restraints, but also resulted in an improved insight into how protein coordination occurs for different metals and the nature of their binding interactions. In particular, the features of carboxylate coordination were inspected, which is the only type of interaction that is commonly present for nearly all metals.

Hunting down zinc(II)-binding sites in proteins with distance matrices.

MOTIVATION: In recent years, high-throughput sequencing technologies have made available the genome sequences of a huge variety of organisms. However, the functional annotation of the encoded proteins often still relies on low-throughput and costly experimental studies. Bioinformatics approaches offer a promising alternative to accelerate this process. In this work, we focus on the binding of zinc(II) ions, which is needed for 5%-10% of any organism's proteins to achieve their physiologically relevant form. RESULTS: To implement a predictor of zinc(II)-binding sites in the 3D structures of proteins, we used a neural network, followed by a filter of the network output against the local structure of all known sites. The latter was implemented as a function comparing the distance matrices of the Cα and Cß atoms of the sites. We called the resulting tool Master of Metals (MOM). The structural models for the entire proteome of an organism generated by AlphaFold can be used as input to our tool in order to achieve annotation at the whole organism level within a few hours. To demonstrate this, we applied MOM to the yeast proteome, obtaining a precision of about 76%, based on data for homologous proteins. AVAILABILITY AND IMPLEMENTATION: Master of Metals has been implemented in Python and is available at https://github.com/cerm-cirmmp/Master-of-metals.

A Comparison of Bonded and Nonbonded Zinc(II) Force Fields with NMR Data.

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.

Metal-induced structural variability of mononuclear metal-binding sites from a database perspective.

Metalloproteins are ubiquitous in all kingdoms of life. Their role and function are tightly related to the local structure of the metal-binding site. In this regard, the MetalPDB database is an invaluable tool since it stores the 3D structure of metal-binding sites and of their corresponding apo forms. In this work, we exploited MetalPDB to compute extensive statistics over >3000 clusters of mononuclear sites about the rearrangements occurring upon change in metalation state. For each cluster, we matched the holo and apo sites so that it was possible to average the distances between all possible pairs of Cα and donor atoms and thus quantitatively assess structural variations by computing the Δ values (mean apo distance - mean holo distance). For most of the structures the backbone is rigid with little to no rearrangement, while donor atoms experience significant changes of their relative position when the metal is removed. Sodium and potassium sites are an exception to this general observation. This is most likely caused by their preference for coordination by the main-chain oxygen atoms, making the rearrangement of donor atoms superimposable to that of the backbone. Magnesium and calcium show a different behavior, despite their chemical similarity: calcium sites undergo a larger reorganization upon metalation although both metals have similar percentage of backbone oxygen as donor atoms. We ascribe this observation to the structural and energetic factors regulating the selectivity for calcium over magnesium.