Locating large flexible ligands on proteins

TL;DR

This paper introduces a computational method to locate large, flexible, charged ligands on proteins by scanning ligand fragments around the protein and converting energy grids into probability distributions, which align well with experimental and MD data.

Contribution

The method efficiently locates large flexible ligands on proteins using grid scanning and FFT-accelerated energy calculations, accommodating ligand and protein flexibility.

Findings

Energy grids match experimental data and MD simulations

Approach works on diverse protein-ligand complexes

Method is computationally efficient for large ligands

Abstract

Many biologically important ligands of proteins are large, flexible, and often charged molecules that bind to extended regions on the protein surface. It is infeasible or expensive to locate such ligands on proteins with standard methods such as docking or molecular dynamics (MD) simulation. The alternative approach proposed here is the scanning of a spatial and angular grid around the protein with smaller fragments of the large ligand. Energy values for complete grids can be computed efficiently with a well-known Fast Fourier Transform accelerated algorithm and a physically meaningful interaction model. We show that the approach can readily incorporate flexibility of protein and ligand. The energy grids (EGs) resulting from the ligand fragment scans can be transformed into probability distributions, and then directly compared to probability distributions estimated from MD simulations and experimental structural data. We test the approach on a diverse set of complexes between proteins and large, flexible ligands, including a complex of Sonic Hedgehog protein and heparin, three heparin sulfate substrates or non-substrates of an epimerase, a multi-branched supramolecular ligand that stabilizes a protein-peptide complex, and a flexible zwitterionic ligand that binds to a surface basin of a Kringle domain. In all cases the EG approach gives results that are in good agreement with experimental data or MD simulations.