<i>PyCPET</i>─Computing Heterogeneous 3D Protein Electric Fields and Their Dynamics

Electrostatic preorganization is an exciting mode to understand the catalytic function of enzymes, yet limited tools exist to computationally analyze it. In particular, no methods exist to interpret the geometry, dynamics, and fundamental components of 3D electric fields, <i>E</i>⃗(<i>r</i>), in protein active sites. To address this, we present <i>PyCPET</i> (Python Computation of Electric Field Topologies), a comprehensive, open-source toolbox to analyze <i>E</i>⃗(<i>r</i>) in enzymes. We designed it around computational efficiency and user friendliness with both CPU- and GPU-accelerated codes. Our aim is to provide a set of functions for rich, descriptive analysis of enzyme systems including dynamics, benchmarking, distribution of streamlines analysis in 3D <i>E</i>⃗(<i>r</i>), computation of point <i>E</i>⃗(<i>r</i>), principal component analysis, and 3D <i>E</i>⃗(<i>r</i>) visualization. Finally, we demonstrate its versatility by exploring the nature of electrostatic preorganization and dynamics in three cases: Cytochrome C, Co-substituted Liver Alcohol Dehydrogenase, and HIV Protease. These test systems, along with previous work, establish <i>PyCPET</i> as an essential toolkit for the in-depth analysis and visualization of electric fields in enzymes, unlocking new avenues for understanding electrostatic contributions to enzyme catalysis.