Using the fragment molecular orbital method to investigate agonist–orexin-2 receptor interactions

Abstract

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity and is essential for an efficient structure-based drug discovery (SBDD) process. Clearly, to begin SBDD, a structure is needed, and although there has been fantastic progress in solving G-protein-coupled receptor (GPCR) crystal structures, the process remains quite slow and is not currently feasible for every GPCR or GPCR–ligand complex. This situation significantly limits the ability of X-ray crystallography to impact the drug discovery process for GPCR targets in ‘real-time’ and hence there is still a need for other practical and cost-efficient alternatives. We present here an approach that integrates our previously described hierarchical GPCR modelling protocol (HGMP) and the fragment molecular orbital (FMO) quantum mechanics (QM) method to explore the interactions and selectivity of the human orexin-2 receptor (OX2R) and its recently discovered nonpeptidic agonists. HGMP generates a 3D model of GPCR structures and its complexes with small molecules by applying a set of computational methods. FMO allows ab initio approaches to be applied to systems that conventional QM methods would find challenging. The key advantage of FMO is that it can reveal information on the individual contribution and chemical nature of each residue and water molecule to the ligand binding that normally would be difficult to detect without QM. We illustrate how the combination of both techniques provides a practical and efficient approach that can be used to analyse the existing structure–function relationships (SAR) and to drive forward SBDD in a real-world example for which there is no crystal structure of the complex available.

Rapid and accurate assessment of GPCR–ligand interactions Using the fragment molecular orbital‐based density‐functional tight‐binding method

“…The interactions detected by FMO‐DFTB are consistent with the experimental data and with those detected by FMO‐MP2 . The application of FMO‐DFTB will be of great utility for the design and evaluation of new compounds, providing a means of significantly decreasing the effort and cost of chemical synthesis needed for drug discovery programs . The high correlation between receptor‐ligand experimentally evaluated affinity and TIE FMO ‐DFTB indicates that FMO‐DFTB can be used to determine the binding affinities of new targets and, therefore, provides a means of accurately predicting experimental outcomes.…”

Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies

“…Recently, an approximate molecular orbital (MO) method called Fragment Molecular Orbital (FMO) was implemented into studies related to GPCR-ligand interactions [60][61][62][63][64][65][66][67][68]. FMO has been described in previous publications and review articles [60][61][62].…”

“…The theoretical characterization of ligand-binding recognition in GPCRs exhibited similar electrostatic and hydrophobic interactions across most GPCR complexes. In 2016, Heifetz and coworkers performed the FMO calculation on the complexes of agonist-orexin-2 receptor (OX 2 R) [64]. They considered all interactions with an absolute pair interaction energy (PIE) greater than or equal to 3.0 kcal/mol.…”

<b>Development of an automated fragment molecular orbital (FMO) calculation protocol toward construction of quantum mechanical </b><b>calculation database for large biomolecules </b>

“…The FMO method is already recognized as a useful drug design tool to analyze ligand binding interactions, incorporating electrostatic interactions such as hydrogen bonds and dispersion forces such as CH/π interactions, using the pair interaction energy decomposition analysis (PIEDA) [4,5] and fine fragmentation by the functional group unit, rather than the amino acid residue unit and the whole ligand [6][7][8]. Recently, the IFIE analysis and its energy decomposition analysis have been applied to the prediction of binding affinity for rational drug design [9][10][11][12][13][14][15][16][17][18]. Using FMO calculations of tens of complexes for one target protein, the essential and characteristic interactions of the ligand binding mode can be abstracted from the IFIE and PIEDA data by clustering methods [19][20] and singular value decomposition [21].…”