Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation

Abstract

Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13-15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13-15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.

Keywords: 2D-similarity searching; MM-PBSA calculation; absorption; descriptor analysis; distribution; excretion and toxicity (ADMET) prediction; metabolism; molecular docking; molecular dynamics simulation; pharmacophore modelling; tumor necrosis factor–α.

Figure 1

The schematic presentation of computational workflow.

Figure 2

Superimposed view of 2AZ5 and 5MU8. The ligands are shown in green and magenta sticks. The active site residues are labelled. The structure-based pharmacophore model (SBPM) and ligand-based pharmacophore models (LBPM1 and LBPM2) are shown. Hydrophobic feature (HYD), hydrogen bond donor (HBD) and hydrogen bond acceptors (HBA) are shown in green, magenta and cyan spheres, respectively.

Figure 3

The 3D-structure of TNF-α is presented in dimeric form in complex with SPD (A) and JNJ525 (B). The ligand binding residues at dimer interface are highlighted. Chain A and B are presented in orange and green color, respectively. The binding site residues are depicted in stick model in their respective chain colors. The co-crystallized ligands (SPD and JNJ525) are shown in magenta color (stick model). SPD is predominantly bound with hydrophobic interactions, however JNJ525 is bound with hydrophobic interactions as well as H-bonding with Ser60 and Tyr151. Hydrogen bonds are depicted in black lines.

Figure 4

Comparative plots of root mean square deviation (a), radius of gyration (b), and root mean square fluctuation (c) based on Cα atoms of TNF-a dimer and its various complexes with ZINC ligands.

Figure 5

The binding interactions of compounds 4, 5, 7 and 9. The interacting residues of chain A and B are depicted in Coral and Yellow color, respectively. The compounds are shown in magenta color (stick model). Hydrogen bonds are presented in green lines. The H-bond distances are labelled in red color. Compound’s numbers are written in parenthesis.