The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?
Open accessAbstract
Molecular docking is the most frequently used computational method for studying the interactions between organic molecules and biological macromolecules. In this context, docking allows predicting the preferred pose of a ligand inside a receptor binding site. However, the selection of the “best” solution is not a trivial task, despite the widely accepted selection criterion that the best pose corresponds to the best energy score. Here, several rigid-target docking methods were evaluated on the same dataset with respect to their ability to reproduce crystallographic binding orientations, to test if the best energy score is a reliable criterion for selecting the best solution. For this, two experiments were performed: (A) to reconstruct the ligand-receptor complex by performing docking of the ligand in its own crystal structure receptor (defined as self-docking), and (B) to reconstruct the ligand-receptor complex by performing docking of the ligand in a crystal structure receptor that contains other ligand (defined as cross-docking). Root-mean square deviation (RMSD) was used to evaluate how different the obtained docking orientation is from the corresponding co-crystallized pose of the same ligand molecule. We found that docking score function is capable of predicting crystallographic binding orientations, but the best ranked solution according to the docking energy is not always the pose that reproduces the experimental binding orientation. This happened when self-docking was achieved, but it was critical in cross-docking. Taking into account that docking is typically used with predictive purposes, during cross-docking experiments, our results indicate that the best energy score is not a reliable criterion to select the best solution in common docking applications. It is strongly recommended to choose the best docking solution according to the scoring function along with additional structural criteria described for analogue ligands to assure the selection of a correct docking solution.
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A new paradigm for applying deep learning to protein–ligand interaction prediction
Wang, Z., Wang, S., Li, Y., Guo, J., Wei, Y., Mu, Y., Zheng, L., & Li, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae145 · Cited by 41
(2024) · doi:10.1007/s00203-024-04140-y · Cited by 2
Metadata
- Authors
- Ramírez, D., & Caballero, J.
- Venue
- Molecules
- Publisher
- MDPI AG
- Published
- 2018-04-28
- Volume
- 23
- Issue
- 5
- Pages
- 1038
- ISSN
- 1420-3049 (ISSN-L)
- OpenAlex
- W2800892557
- Cited by
- 624
- References
- 53
- FWCI
- 18.82
- Type
- journal article
- Language
- en
- Open access
- gold
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