Empirical Scoring Functions for Advanced Protein−Ligand Docking with PLANTS

TLDR

The scoring functions relate to components of existing scoring functions and force fields. The study introduces two empirical scoring functions, PLANTSCHEMPLP and PLANTSPLP, for the PLANTS docking algorithm based on ant colony optimization. A parametrization procedure identified multiple settings that achieved excellent pose‑prediction performance on two test sets of 298 complexes. The optimized scoring functions reproduced up to 87 % of the Astex diverse set and 77 % of the CCDC/Astex clean list complexes with RMSD < 2 Å, outperformed GOLD on the drug‑like Astex set, and identified search‑algorithm parameters that balance reliability and speed.

Abstract

In this paper we present two empirical scoring functions, PLANTSCHEMPLP and PLANTSPLP, designed for our docking algorithm PLANTS (Protein−Ligand ANT System), which is based on ant colony optimization (ACO). They are related, regarding their functional form, to parts of already published scoring functions and force fields. The parametrization procedure described here was able to identify several parameter settings showing an excellent performance for the task of pose prediction on two test sets comprising 298 complexes in total. Up to 87% of the complexes of the Astex diverse set and 77% of the CCDC/Astex clean listnc (noncovalently bound complexes of the clean list) could be reproduced with root-mean-square deviations of less than 2 Å with respect to the experimentally determined structures. A comparison with the state-of-the-art docking tool GOLD clearly shows that this is, especially for the druglike Astex diverse set, an improvement in pose prediction performance. Additionally, optimized parameter settings for the search algorithm were identified, which can be used to balance pose prediction reliability and search speed.