Biology subjects

Dürr, S. L.

Publications and source records attributed to Dürr, S. L..

2 recordsLinked to original sources

bioRxiv2025

AllMetal3D: joint prediction of localization, identity and coordination geometry of common metal ions in proteins

Nature uses a variety of metal ions as cofactors for specific tasks. The three main classes of metals in biological systems are alkali ions (e.g. sodium and potassium), alkaline earth ions (e.g. magnesium and calcium) as well as transition metal ions (e.g. zinc or iron and many others). To date no selective predictor to localize each of these metals in a given protein structure exists. Current methods require either preselecting a specific ion (e.g MIB2 (Lu et al., 2022), ESMBind (Dai et al., 2024) or AlphaFold3 (Abramson et al., 2024) or a location (MIC Shub et al. (2024)). In this work, we describe an extension of the recently introduced Metal3D framework (Durr et al., 2023) that was originally trained on zinc sites to predict the location of all biologically relevant classes of metal ions as well as to classify their coordination geometry. Our model is the first of its kind and even outperforms Metal3D for the prediction of the location of Zn2+ and generalizes well to the other metals in terms of location prediction as well as identity classification. Comparing the model to several other available tools such as MetalSiteHunter, AlphaFold3, MIC, MIB2 and MetalHawk highlights important shortcomings in these tools with respect to data bias, prediction of negative sites and selectivity. However, concerning coordination geometry prediction, similar to other work, we find that our method cannot accurately make correct classifications beyond the most common classes in natural proteins i.e. tetrahedral and octahedral arrangements. AllMetal3D is available as ChimeraX extension, standalone web app as well as python package: https://github.com/lcbc-epfl/allmetal3d

bioinformatics↗

bioRxiv2022

Accurate prediction of transition metal ion location via deep learning

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 [A] of the experimental locations with half of the sites below 0.5 [A]. 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.

biophysics↗