Boltz-ABFE: Free Energy Perturbation without Crystal Structures

TL;DR

Boltz-ABFE combines advanced structure prediction with free energy calculations to estimate drug binding affinities without needing experimental crystal structures, thus broadening early drug discovery capabilities.

Contribution

This work introduces Boltz-ABFE, a novel pipeline integrating Boltz-2 structure prediction with FEP for absolute binding free energy estimation without crystal structures.

Findings

Boltz-ABFE accurately predicts binding free energies for four protein targets.

The pipeline improves structure quality for molecular dynamics simulations.

Boltz-ABFE extends FEP applicability to early-stage drug discovery.

Abstract

Free energy perturbation (FEP) is considered the gold-standard simulation method for estimating small molecule binding affinity, a quantity of vital importance to drug discovery. The accuracy of FEP critically depends on an accurate model of the protein-ligand complex as an initial condition for the underlying molecular dynamics simulation. This requirement has limited the impact of FEP in earlier stages of the discovery process, where appropriate experimental crystal structures are rarely available. The latest generation of structure prediction models, such as Boltz-2, promise to overcome this limitation by predicting protein-ligand complex structures. In this work, we combine Boltz-2 with our own absolute FEP protocol to build Boltz-ABFE, a robust pipeline for estimating the absolute binding free energies (ABFE) in the absence of experimental crystal structures. We investigate the quality of the structures predicted by Boltz-2, propose automated approaches to improve structures for use in molecular dynamics simulations, and demonstrate the effectiveness of the Boltz-ABFE pipeline for four protein targets from the FEP+ benchmark set. Demonstrating the feasibility of absolute FEP simulations without experimental crystal structures, Boltz-ABFE significantly expands the domain of applicability of FEP, paving the way towards accelerated early-stage drug discovery via accurate, structure-based affinity estimation.