Revealing the binding mode between respiratory syncytial virus fusion protein and benzimidazole-based inhibitors

• Published in: Molecular bioSystems 2015-07, Vol.11 (7), p.1857-1866
• Main Authors: Ji, Dingjue, Ye, Wei, Chen, HaiFeng
• Format: Article
• Language: English
• Subjects: Antiviral Agents - chemistry; Benzimidazoles - chemistry; Binding Sites; Human respiratory syncytial virus; Hydrogen Bonding; Molecular Docking Simulation; Molecular Dynamics Simulation; Protein Binding; Protein Structure, Secondary; Quantitative Structure-Activity Relationship; Respiratory syncytial virus; Respiratory Syncytial Viruses; Viral Fusion Proteins - antagonists & inhibitors; Viral Fusion Proteins - chemistry
• ISSN: 1742-206X; 1742-2051
• DOI: 10.1039/c5mb00036j

Human respiratory syncytial virus (HRSV) is a major respiratory pathogen in newborn infants and young children and can also be a threat to some elderly and high-risk adults with chronic pulmonary disease and the severely immunocompromised. The RSV fusion (RSVF) protein has been an attractive target for vaccine and drug development. Experimental results indicate a series of benzimidazole-based inhibitors which target RSVF protein to inhibit the viral entry of RSV. To reveal the binding mode between these inhibitors and RSVF protein, molecular docking and molecular dynamics simulations were used to investigate the interactions between the inhibitors and the core domain of RSVF protein. MD results suggest that the active molecules have stronger π-π stacking, cation-π, and other interactions than less active inhibitors. The binding free energy between the active inhibitor and RSVF protein is also found to be significantly lower than that of the less active one using MM/GBSA. Then, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to construct three dimensional quantitative structure-activity (3D-QSAR) models. The cross-validated q(2) values are found to be 0.821 and 0.795 for CoMFA and CoMSIA, respectively. And the non-cross-validated r(2) values are 0.973 and 0.961. Ninety-two test set compounds validated these models. The results suggest that these models are robust with good prediction abilities. Furthermore, these models reveal possible methods to improve the bioactivity of inhibitors.