Recent Progress in Treating Protein–Ligand Interactions with Quantum-Mechanical Methods

Abstract

We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.

Disease-associated missense mutations in GluN2B subunit alter NMDA receptor ligand binding and ion channel properties

“…3b, c ; Supplementary Fig 4a ). DFT was selected as it is a quantum mechanical modelling method that is more accurate than molecular docking for predicting stable binding modes for ions (which are small and spherical with minimal geometric constraints that docking software normally relies upon 18 ). This analysis revealed that four asparagine residues (N616), donated by each GluN1 and GluN2B subunit, are directly involved in the coordination of Mg 2+ along with two water molecules (Fig.…”

“…The asparagine Cα atoms were also substituted for fixed atom methyl groups during the geometric optimisation to ensure that their position did not move from their original conformation in the hybrid model and to maintain plausible bond angles and dihedral angles between the side-chain and backbone. DFT calculations were performed on the asparagine–Mg 2+ complex, using the Gaussian 09 package 18 . All calculations were performed using the hybrid functional ωB97X-D 64 with a Pople triple-zeta basis set with polarisation (6–311G**) and the conductor-like polarisable continuum model (CPCM) to account for solvation 65 .…”

Machine learning classification can reduce false positives in structure-based virtual screening

“…While vScreenML does incorporate a broad and distinct set of features, these have been largely collected from other approaches: there is nothing particularly unique or special about the features it includes. There are also numerous potential contributions to protein-ligand interactions that are not captured in this collection of features, ranging from inclusion of tightly-bound interfacial waters [16,89,90] to explicit polarizability and quantum effects [91,92]. In this vein, ongoing research in ligandbased screening has led to new approaches that learn optimal molecular descriptors (and thus the representation that directly leads to the features themselves) at the same time as the model itself is trained [93,94]: these might similarly be used as a means to improve the descriptors used in structure-based screening as well.…”

Computational chemistry in drug lead discovery and design

“…Until recently, the field of macromolecular simulation—and in particular, binding free energy calculation—has been mainly dominated by methods based on classical mechanics, but in recent years there has been a surge in the development of QM‐based methods aimed at structure‐based drug design. It should be stressed that the QM formulation is, in principle, theoretically exact, since it includes all contributions to the energy, including those effects usually missing in FFs, such as electronic polarization, charge transfer, halogen bonding, and covalent‐bond formation; moreover, it avoids system‐dependent parameterizations, so that all elements and interactions are considered on equal footing (recent theoretical developments and applications of QM‐based methods to ligand binding and virtual screening can be found elsewhere).…”