Back

HemeFinder: a Computational Predictor for Heme-Binding Sites in Proteins

Tiessler-Sala, L.; Fernandez-Diaz, R.; Marechal, J.-D.

2025-04-19 bioinformatics
10.1101/2025.04.14.648773 bioRxiv
Show abstract

HemeFinder has been developed to predict heme-binding sites in natural and heme-dependent de novo enzymes. This tool relies on the structural and physicochemical characteristics of heme-binding sites, including shape, residue composition, and geometric descriptors. HemeFinder benchmarks more than 94% accuracy in identifying the correct heme location, considering the complete set of solutions, and 72% accuracy for the proper location with the correct iron-coordinating residues among the three best-ranked solutions. HemeFinder performs within seconds for monomeric systems and takes minutes for larger multimeric ones, demonstrating that its speed does not compromise its performance. An illustrative case of its potential is provided. HemeFinder is applied to the heme carrier protein 1 (HCP1), a transmembrane protein involved in heme recruitment in evolved organisms, for which no ligand-bound structures have been revealed. HemeFinder provides a relevant prediction of the binding of porphyrin and, when combined with protein-ligand docking, offers the first evidence of low-energy Heme-HCP1 complexes and unveils possible heme pathways. HemeFinder is an interesting, fast, and accurate tool for identifying heme-binding sites in proteins. Source code, documentation, and data are available at https://github.com/laura-tiessler/hemefinder and ESI.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.