MetalKB: Predicting Metal Binding Sites on Proteins with a Knowledge-Based Graph Framework

Metal ions play a crucial role in the function, regulation,
and
stability of proteins. Therefore, accurate prediction of metal ions’
binding sites is valuable to reveal the molecular mechanism of related
biological processes. Here, we propose MetalKB, a novel knowledge-based
framework for predicting the binding sites of metal ions on proteins
by using atomic-level statistical potentials and graph-theoretical
strategies. Specifically, possible donor atom clusters are first identified
using a clique detection algorithm, from which initial metal ion coordinates
are generated. These candidate coordinates are then evaluated and
locally refined using knowledge-based statistical potentials derived
from a protein-metal ion binding database. Redundant predictions are
subsequently removed by applying spatial distance thresholds. Evaluations
on diverse benchmark data sets provided by Metal3D and TEMSP show
that MetalKB demonstrates competitive performance compared with seven
representative methods in terms of precision, recall, and F1 score,
while exhibiting strong robustness and parameter stability. MetalKB
is capable of identifying complex coordination environments, including
multinuclear and bridging metal-binding sites, as illustrated in representative
structural examples. In addition, it also provides prediction of both
metal ion 3D coordinates and residue-level coordinating ligands.