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Generation, Amplification, and Titration of Recombinant Respiratory Syncytial Viruses
Published on: April 4, 2019
Revealing the binding mode between respiratory syncytial virus fusion protein and benzimidazole-based inhibitors
Dingjue Ji1, Wei Ye, HaiFeng Chen
1State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, College of Life Sciences and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China. haifengchen@sjtu.edu.cn.
Insights
Benzimidazole inhibitors targeting the RSV fusion protein show strong interactions, leading to reduced viral entry. Developed 3D-QSAR models predict and guide the design of more potent antiviral compounds for respiratory syncytial virus.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Virology
Background:
- Human respiratory syncytial virus (HRSV) poses a significant threat to infants, the elderly, and immunocompromised individuals.
- The RSV fusion (RSVF) protein is a key target for developing antiviral therapies and vaccines.
- Benzimidazole derivatives have emerged as potential inhibitors of RSVF protein, aiming to block viral entry.
Purpose of the Study:
- To elucidate the binding interactions between benzimidazole inhibitors and the core domain of the RSVF protein.
- To develop robust 3D-QSAR models for predicting the bioactivity of novel RSVF inhibitors.
- To identify structural features that enhance the inhibitory potential against HRSV.
Main Methods:
- Molecular docking and molecular dynamics (MD) simulations were employed to investigate inhibitor-protein interactions.
- MD simulations analyzed key interactions such as π-π stacking and cation-π.
- MM/GBSA calculations determined binding free energies.
- Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were used to build 3D-QSAR models.
Main Results:
- Active benzimidazole inhibitors exhibited stronger π-π stacking and cation-π interactions with the RSVF protein compared to less active ones.
- Significantly lower binding free energy was observed for active inhibitors using MM/GBSA.
- CoMFA and CoMSIA models demonstrated high predictive power with q(2) values of 0.821 and 0.795, and r(2) values of 0.973 and 0.961, respectively.
- Validation with 92 test compounds confirmed the robustness and predictive ability of the 3D-QSAR models.
Conclusions:
- The study successfully revealed the binding modes of benzimidazole inhibitors with the RSVF protein.
- The developed 3D-QSAR models are reliable tools for predicting and designing novel, potent inhibitors.
- These findings provide a basis for optimizing existing compounds and developing new strategies against HRSV infections.
Abstract:
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.
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