# Binding Site Vectors Enable Mapping of Cytochrome P450 Functional Landscapes

## TL;DR

This paper introduces a new method to compare protein binding sites, revealing functional relationships in cytochrome P450 enzymes that traditional methods miss.

## Contribution

The novel contribution is the development of binding site vectors that integrate structural and electrostatic features for high-resolution comparisons.

## Key findings

- Binding site vectors reveal structural-functional relationships in CYP enzymes missed by sequence-based methods.
- Analysis of over 600 CYP structures and MD ensembles shows the method's robustness in functional classification.
- Including conformational ensembles enhances the detection of mechanistic insights in macromolecular systems.

## Abstract

Understanding similarities between protein binding sites
has long
been of great interest, as such comparisons can reveal functional
relationships that transcend sequence or fold. However, systematic
comparison remains challenging due to the difficulty of defining active
sites consistently and developing descriptors that are both general
and discriminative. We present binding site vectors, a computational framework for a high-resolution comparison of macromolecular
binding sites that integrates both structural and electrostatic properties.
The vectors extend spherically from the center of the pocket, terminating
at its surface to capture shape and electrostatic features in a multidimensional
manner. Geometrically anchored, they enable a systematic comparison
of binding sites across diverse systems. We applied this approach
to cytochrome P450 (CYP) enzymes, analyzing over 600 human and plant
CYP structures and a subset of 23 extensive structural ensembles obtained
through molecular dynamics (MD) simulation. Comparisons based on binding
site vectors reveal structural–functional relationships missed
by sequence- or backbone-based groupings, particularly when full conformational
ensembles are included. This demonstrates that binding site vectors
provide a robust framework for both functional classification and
deep mechanistic insights into macromolecular systems.