Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein−protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy bynhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM- GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.
CITATION STYLE
Zulmi, R. A., Suparyanto dan Rosad (2015, Puspitarini, Publikasi, N., Kesehatan, F. I., Nugroho, A., … Diputra, R. (2018). Nanotechnology : Science and Computation. Jurnal SPORTIF : Jurnal Penelitian Pembelajaran (Vol. 2, pp. 24–29). Retrieved from https://www.ptonline.com/articles/how-to-get-better-mfi-results%0Amuhammadkahfi16060474066@mhs.unesa.ac.id
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