SAR-Guided Scoring Function and Mutational Validation Reveal the Binding Mode of CGS-8216 at the α1+/γ2– Benzodiazepine Site

Abstract

The structural resolution of a bound ligand-receptor complex is a key asset to efficiently drive lead optimization in drug design. However, structural resolution of many drug targets still remains a challenging endeavor. In the absence of structural knowledge, scientists resort to structure-activity relationships (SARs) to promote compound development. In this study, we incorporated ligand-based knowledge to formulate a docking scoring function that evaluates binding poses for their agreement with a known SAR. We showcased this protocol by identifying the binding mode of the pyrazoloquinolinone (PQ) CGS-8216 at the benzodiazepine binding site of the GABA receptor. Further evaluation of the final pose by molecular dynamics and free energy simulations revealed a close proximity between the pendent phenyl ring of the PQ and γ2D56, congruent with the low potency of carboxyphenyl analogues. Ultimately, we introduced the γ2D56A mutation and in fact observed a 10-fold potency increase in the carboxyphenyl analogue, providing experimental evidence in favor of our binding hypothesis.

Demystifying the Molecular Basis of Pyrazoloquinolinones Recognition at the Extracellular α1+/β3- Interface of the GABAA Receptor by Molecular Modeling

“…The optimized BM I revealed two p-p interactions with the residues a1H101 and a1Y209, and a backbone hydrogen bond interaction with the residue a1Y159, that might elucidate the high potency of 4 in the mutant. Importantly, these were the same set of interactions that were previously described by Siebert et al for PQ CGS-9895 at the a1+/g2interface (Siebert et al, 2018b) indicating strong coherence between the two BMs at the homologous ligand binding interfaces. In addition, BM I showed good overlap to the binding orientation of flumazenil at the a1 +/g2-interface of the a1b2g2 GABA A receptor further signifying the reliability of BM I.…”

“…The ring C is involved in a strong hydrogen bond interaction with b3Q64 that appears to be the main force driving the affinity apart from the contributions through hydrophobic interactions ( Figure 8A). Notably, BM I showed the absence of backbone hydrogen bond interaction of the quinoline nitrogen which seems to be an essential interaction to gain affinity as suggested by Siebert et al (2018b) In combination with the loss of backbone interaction and diminished hydrophobic interactions, this altogether explains the overall low affinity of PQs at the a1+/b3interface in comparison to the a1+/g2interface. Importantly, b3D43 is revealed as a crucial residue hindering the binding of PQs at the a1+/b3interface due to the electrostatic repulsion between the carboxyl group of D43 and the electron-rich areas of the ligand.…”

“…Incongruent SAR patterns, as well as an inadequate SAR-hypersurface, may be considered as limiting factors that impede the proposed approach. For example, a flat SAR without any discontinuity would not carry any discriminative potential for pose prioritization (Siebert et al, 2018b). Here, the calculation of the R SAR scores might provide a quick suitability assessment.…”

“…The scoring scheme for evaluating the 100 PZ-II-028 (p1-p100) poses consists of three steps: (i) analogs pose expansion using post-docking derivatization tool, (ii) energy minimization and binding energy calculations of the derivatized protein-ligand complexes, and (iii) SAR congruency assessment (i.e., calculation of correlation coefficient between the MM-GBSA scores and experimental data) and ranking of the poses according to the scores (see also Figure 2). The first preparatory step utilizes the previously published post-docking derivation tool (Siebert et al, 2018b) that results in the generation of the poses of related analogs (or 'analog expansion'). Here, based on the 3D coordinates of every PZ-II-028 docking pose, an array of 18 ligand-receptor complexes for analogs 1-13, and 15-19 (Table 1) is derived by adding substituents to the PQ scaffold of each docking pose of 14.…”

“…Also, this information can assist in identifying molecular determinants leading, for instance, to the agonist and antagonist behaviors of the ligands (Warne et al, 2011). However, studies have shown that docking programs are capable of reproducing the correct binding orientations, but the scoring functions often struggle to rank the correct orientations on top of the graded list (Siebert et al, 2018b). Hence, there is a need to identify new protocols and scoring techniques that increase the reliability of the binding hypotheses by assessing the congruency between the predicted and experimental binding affinity of the compounds.…”