Accurate prediction of transition metal ion location via deep learning

Abstract

Metal ions are essential cofactors for many proteins. In fact, currently, about half of the structurally characterized proteins contain a metal ion. Metal ions play a crucial role for many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties e.g. as Lewis acid. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc that can often not be accurately described using a classical force field. In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the identification and localization of zinc and other metal ions in experimental and computationally predicted protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate metal ion location predictor to date outperforming geometric predictors including Metal1D by a wide margin using a single structure as input. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. The predicted metal ion locations for Metal3D are within 0.70 +- 0.64 Å of the experimental locations with half of the sites below 0.5 Å. Metal3D predicts a global metal density that can be used for annotation of structures predicted using e.g. AlphaFold2 and a per residue metal density that can be used in protein design workflows for the location of suitable metal binding sites and rotamer sampling to create novel metalloproteins. Metal3D is available as easy to use webapp, notebook or commandline interface.

Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins

“…Code is available under https://github.com/lcbc-epfl/metal-siteprediction 86 and also on Zenodo under https://doi.org/10.5281/ zenodo.7015849 87 . EVOLVE v0.2 code is available on https://doi.org/ 10.5281/zenodo.5713801 88 .…”

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design

“… 76 DL has also been applied for finding potential location sites of transition metals in proteins (Metal1D and Metal3D). 77 The coevolution based MetalNet pipeline has also been recently created to predict potential metal-binding sites. 78 …”