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

Abstract

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% to 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 Cβ atoms of the sites. We called the resulting tool Master of Metals. 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 Master of Metals to the yeast proteome, obtaining a precision of about 76%, based on data for homologous proteins. Availability Master of Metals has been implemented in Python and is available at https://github.com/cerm-cirmmp/Master-of-metals. Supplementary information Supplementary data are available at Bioinformatics online.

Predicting metal-protein interactions using cofolding methods: Status quo

“…Deep learning based tools can now also predict metal ion location from single structures ( Dürr et al, 2023 ) and are sensitive even to small side chain rearrangments ( Laveglia et al, 2023 ).…”