Machine-learning prediction of protein function from the portrait of its intramolecular electric field

We introduce a machine learning framework designed to predict enzyme functionality directly from the heterogeneous electric fields inherent to protein active sites. We apply this method to a curated dataset of Heme-Iron Oxidoreductases, spanning three enzyme classes: monooxygenases, peroxidases, and catalases. Conventional analysis, focused on simplistic, point electric fields along the Fe-O bond, are shown to be inadequate for accurate activity prediction. Our model demonstrates that the enzyme's heterogenous 3-D electric field, alone, can accurately predict its function, without relying on additional protein-specific information. Through feature selection, we uncover key electric field components that not only validate previous studies but also underscore the crucial role of multiple components beyond the traditionally emphasized electric field along the Fe-O bond in heme enzymes. Further, by integrating protein dynamics, principal component analysis, clustering, and QM/MM calculations, we reveal that while dynamic complexities in protein structures can complicate predictions, accounting for this increased dynamic variability can substantially enhance model performance. This research significantly advances our understanding of how protein scaffolds possess signature electric fields that are tailored to their functions at the active site. Moreover, it presents a novel electrostatics-based tool to harness these signature electric fields for predicting enzyme function.