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

ABSTRACT: Metal ions are essential cofactors for many proteins and play a crucial role in 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. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.7

SUBMITTER: Durr SL 

PROVIDER: S-EPMC10175565 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature