Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal

Abstract

The main protease (M^pro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure-activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized M^pro-inhibitors and validated on available literature data. By means of deep optimization both models' goodness and robustness reached final statistical values of r²/q² values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEP_PRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure-activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral M^pro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.

Keywords: 3-D QSAR; COMBINE; Ligand-based drug design; SARS-Cov-2; Structure-activity relationships; Structure-based drug design.

Fig. 1

Currently FDA-approved or authorized antivirals used for the treatment of mild-to-moderate COVID-19: remdesivir (A), molnupiravir (B), nirmatrelvir (C) and ritonavir (D)

Fig. 2

Model LB1 recalculated (blue dots) and internally predicted (orange dots, CV_LOO) pIC₅₀s versus experimental values (Table 2). pAct in the plot indicates the pIC₅₀ as the plot was generated within 3d-qsar.com

Fig. 3

AAC steric (A) and electrostatic (B) plots of model LB1. The most potent compound 3 is shown (light gray). Green and yellow polyhedrons depict areas were increased or decreased steric bulk may favor biological activity, respectively. Red and blue polyhedrons indicate regions where electronically involved groups are predicted to positively or negatively contribute to the activity, respectively. Hydrogen atoms are omitted for the sake of clarity. These plots are generated by means of USCF Chimera

Fig. 4

Recalculated (blue dots) and CV_LOO predicted (orange dots) pIC₅₀ values versus the experimental activities by model SB1_SAFS (Table 3). pAct in the plot indicates the pIC₅₀ as directly generated by 3d-qsar.com. The plot was generated within 3d-qsar.com

Fig. 5

MRAAC plot of model SB1_SAFS. The most relevant ligand/per-residue positive or negative energetic interactions are reported: steric (STE), electrostatic (ELE), desolvation (DRY) and hydrogen bond (HB). Aside the residue numbers in bracket are reported the indication of the enzyme’ pocket to which each residue belongs

Fig. 6

MRAC plots of the two most active TR compounds 3 (A) and 9 (B) and the two least active TR compounds 20 (C) and 21 (D) derived by model SB1_SAFS. The most relevant ligand/per-residue positive or negative energetic interactions are reported: steric (STE), electrostatic (ELE), desolvation (DRY) and hydrogen bond (HB). Aside the residue numbers in bracket are reported the indication of the enzyme’ pocket to which each residue belongs

Fig. 7

Graphical depiction of AAC and MRAAC plots in the binding site of compound 3 (gray)—M.^pro minimized complex (PDB code = 6XHM). Residues are colored depending on their higher activity contribution: green—STE positive, yellow—STE negative, red—HB positive, orange—DRY positive (see legend in Fig. 5). The image was prepared through USCF Chimera

Fig. 8.

3-D SAR derived model for M.^pro inhibitors. The most potent TR compound 3 is used as template. Circles are color-coded to represent the main associated steric (favorable green, unfavorable yellow) and HB (HD blue, HA red) features. Striped two-colored circles account for two features together

Fig. 9

TSMOD (A) and TSCRY (B) consensus model’s errors of prediction, SDEPPRED and AAEP, divided by scaffolds

Fig. 10

Classification metrics for LB1, SB1_SAFS and Consensus models before (A) and after (B) assessing the AD