Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel
(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the b...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/14/7558 |
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| author | Silvia Gervasoni Carmine Talarico Candida Manelfi Alessandro Pedretti Giulio Vistoli Andrea R. Beccari |
| author_facet | Silvia Gervasoni Carmine Talarico Candida Manelfi Alessandro Pedretti Giulio Vistoli Andrea R. Beccari |
| author_sort | Silvia Gervasoni |
| collection | DOAJ |
| description | (1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations. |
| first_indexed | 2024-03-09T10:18:00Z |
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| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T10:18:00Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-1b1e589f24d743a5b6f432baf9c96a6a2023-12-01T22:13:58ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-07-012314755810.3390/ijms23147558Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 ChannelSilvia Gervasoni0Carmine Talarico1Candida Manelfi2Alessandro Pedretti3Giulio Vistoli4Andrea R. Beccari5Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli, 25, I-20133 Milano, ItalyDompé Farmaceutici SpA, EXSCALATE Labs, Via Tommaso De Amicis, 95, I-80131 Napoli, ItalyDompé Farmaceutici SpA, EXSCALATE Labs, Via Tommaso De Amicis, 95, I-80131 Napoli, ItalyDipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli, 25, I-20133 Milano, ItalyDipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli, 25, I-20133 Milano, ItalyDompé Farmaceutici SpA, EXSCALATE Labs, Via Tommaso De Amicis, 95, I-80131 Napoli, Italy(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.https://www.mdpi.com/1422-0067/23/14/7558virtual screeningsystematic samplingMD simulationsconsensus modelsLiGen softwareEFO algorithm |
| spellingShingle | Silvia Gervasoni Carmine Talarico Candida Manelfi Alessandro Pedretti Giulio Vistoli Andrea R. Beccari Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel International Journal of Molecular Sciences virtual screening systematic sampling MD simulations consensus models LiGen software EFO algorithm |
| title | Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel |
| title_full | Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel |
| title_fullStr | Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel |
| title_full_unstemmed | Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel |
| title_short | Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel |
| title_sort | extensive sampling of molecular dynamics simulations to identify reliable protein structures for optimized virtual screening studies the case of the htrpm8 channel |
| topic | virtual screening systematic sampling MD simulations consensus models LiGen software EFO algorithm |
| url | https://www.mdpi.com/1422-0067/23/14/7558 |
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