Ligand-Binding Affinity Estimates Supported by Quantum-Mechanical Methods

Abstract

One of the largest challenges of computational chemistry is calculation of accurate free energies for the binding of a small molecule to a biological macromolecule, which has immense implications in drug development. It is well-known that standard molecular-mechanics force fields used in most such calculations have a limited accuracy. Therefore, there has been a great interest in improving the estimates using quantum-mechanical (QM) methods. We review here approaches involving explicit QM energies to calculate binding affinities, with an emphasis on the methods, rather than on specific applications. Many different QM methods have been employed, ranging from semiempirical QM calculations, via density-functional theory, to strict coupled-cluster calculations. Dispersion and other empirical corrections are mandatory for the approximate methods, as well as large basis sets for the stricter methods. QM has been used for the ligand, for a few crucial groups around the ligand, for all the closest atoms (200-1000 atoms), or for the full receptor-ligand complex, but it is likely that with a proper embedding it might be enough to include all groups within ∼6 Å of the ligand. Approaches involving minimized structures, simulations of the end states of the binding reaction, or full free-energy simulations have been tested.

Binding free energies in the SAMPL5 octa-acid host–guest challenge calculated with DFT-D3 and CCSD(T)

“…performing the MD simulations at the MM level and then performing perturbations or reweighting from MM to QM) [14, 72, 93, 94]. Unfortunately, the overlap between the MM and QM potential surfaces are so poor that very many QM calculations are needed to obtain converged results, e.g.…”

“…The binding free energy has contributions from a large number of interactions, such as bonded terms, dispersion, exchange-repulsion, electrostatics, polarisation, charge transfer, charge penetration, solvation, and entropy. As MM force fields have inherent limitations in treating several of these interactions, there has been a growing interest in using quantum–mechanical (QM) methods to improve binding-affinity calculations [7–14]. …”

An efficient protocol for obtaining accurate hydration free energies using quantum chemistry and reweighting from molecular dynamics simulations

“…The applicability of this approach is limited by the overlap between QM and MM potential energy surfaces, and the size of the system of interest, because of this, alternative free energy schemes may be preferable. Dybeck et al [2016]; Jia et al [2016]; Ryde and Söderhjelm [2016]; König and Brooks [2015]; Cave-Ayland et al [2014]; Sampson et al [2015]; Rodinger and Pomès [2005]; Heimdal and Ryde [2012]; Mikulskis et al [2014]; Genheden et al [2015]; Olsson et al [2016]; Hudson et al [2015a,b]; Beierlein et al [2011]; Fox et al [2013]…”

A QM protein–ligand investigation of antipsychotic drugs with the dopamine D2 Receptor (D2R)

“…The energies presented here are mostly ligand binding enthalpies and entropic contributions are not explicitly considered. Determination of protein-ligand free energies based on both MM and QM approaches has been discussed in detail in the literature (Ryde & Soderhjelm, 2016). The importance of large basis sets and inclusion of dispersion corrections, polar and non-polar solvation, and entropy in the calculations of protein-ligand interaction energies are highlighted (Ryde & Soderhjelm, 2016).…”

“…Determination of protein-ligand free energies based on both MM and QM approaches has been discussed in detail in the literature (Ryde & Soderhjelm, 2016). The importance of large basis sets and inclusion of dispersion corrections, polar and non-polar solvation, and entropy in the calculations of protein-ligand interaction energies are highlighted (Ryde & Soderhjelm, 2016). Recently, a new and faster (than HF-3c) semi-empirical method, PBEh-3c developed by Grimme's group, was employed to evaluate all the contributions to the energy such as interaction energy and solvation energy in the active site containing about 1000 atoms on activated serine protease factor X (FXa) and tyrosine-protein kinase 2 (TYK2) targets as a test case (Ehrlich, Göller, & Grimme, 2017).…”