# Prediction of Protein–Ligand Binding Affinities Using Atomic Surface Site Interaction Points

## TL;DR

This paper introduces a new method to predict how tightly proteins bind to molecules using atomic surface interactions, achieving good accuracy on a benchmark dataset.

## Contribution

Extending the AIP method from host–guest systems to protein–ligand complexes with a novel graph-based substructure matching and electrostatic potential analysis.

## Key findings

- The AIP method achieved a Pearson correlation coefficient of 0.76 for binding affinity predictions.
- The method had an RMSD of 11 kJ mol–1 for absolute free energies of binding in the CASF dataset.
- AIP descriptors were successfully applied to protein–ligand complexes using X-ray crystal structures.

## Abstract

Atom surface site Interaction Points (AIP) which were
previously
used to predict association constants for synthetic host–guest
systems has been extended to protein–ligand complexes. AIP
descriptions of protein binding sites were obtained by combining a
library of precomputed AIP descriptors for all protein functional
groups with a graph-based substructure matching algorithm. The corresponding
AIP description of ligands was obtained directly by footprinting the
molecular electrostatic potential surface calculated using density
functional theory. These AIP descriptions were projected onto X-ray
crystal structures of protein–ligand complexes to identify
pairs of AIPs that were sufficiently close in space to constitute
an intermolecular interaction. The overall free energy of binding
was calculated by summing the contributions of each AIP contact and
associated desolvation. Application to the 94 complexes involving
uncharged ligands in CASF benchmark data set showed that the method
achieves a Pearson correlation coefficient of 0.76 and an RMSD of
11 kJ mol–1 for absolute free energies of binding.