Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins

Abstract

Background Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. Results Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. Conclusions Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.

HLA-Arena: A Customizable Environment for the Structural Modeling and Analysis of Peptide-HLA Complexes for Cancer Immunotherapy

“…Default values were used for other DINC parameters. 23 Results (Appendix Fig A1A ) show that, in every single run, HLA-Arena sampled a near-native peptide conformation (ie, a conformation with an all-heavy-atom root mean square deviation [RMSD] to the crystal structure < 2.5 Å).…”

“…It is important to note that HLA-Arena provides efficient solutions to sampling challenges associated with pHLA modeling 8 , 23 and facilitates the integration of these solutions with other tools for structural analysis. However, the accuracy of structure-based peptide ranking is limited by existing scoring functions.…”

“… 9 , 22 Both APE-Gen and DINC have been validated in previous publications. 8 , 10 , 23 In this report, we present a unified environment that facilitates the use of APE-Gen, DINC, and other tools for various research applications.…”

“… 9 We recently showed that it performs a more exhaustive sampling than other docking approaches. 23 In that study, DINC was benchmarked using 5 public data sets including large ligands; it reproduced many crystal structures on which other docking tools had failed. 23 For example, it has been used to study the inhibition of the Src homology 2 domain of STAT3 by peptidomimetics.…”

“… 23 In that study, DINC was benchmarked using 5 public data sets including large ligands; it reproduced many crystal structures on which other docking tools had failed. 23 For example, it has been used to study the inhibition of the Src homology 2 domain of STAT3 by peptidomimetics. 22 We also showed that DINC could reproduce a diverse set of pHLA structures encompassing 10 HLA alleles and peptides with diverse binding modes; it achieved an average all-heavy-atom RMSD of 1.92 Å.…”