An ant colony optimization approach to flexible protein–ligand docking

Abstract

The prediction of the complex structure of a small ligand with a protein, the socalled protein-ligand docking problem, is a central part of the rational drug design process. For this purpose, we introduce the docking algorithm PLANTS (Protein-Ligand ANT System), which is based on ant colony optimization, one of the most successful swarm intelligence techniques. We study the effectiveness of PLANTS for several parameter settings and present a direct comparison of PLANTS's performance to a state-of-the-art program called GOLD, which is based on a genetic algorithm and frequently used in the pharmaceutical industry for this task. Last but not least, we also show that PLANTS can make effective use of protein flexibility giving example results on cross-docking and virtual screening experiments for protein kinase A.

Isomerization of the Phytohormone Precursor 12-Oxophytodienoic Acid (OPDA) in the Insect Gut

“…During the refinement and energy optimization of the model, YASARA automatically merged glutathione from the template protein (2imi) into the protein model, resulting in an optimal position for the co-factor. Using this structure, docking studies were performed with PLANTS (30,31). In each case, 30 different docking poses were calculated and subsequently analyzed.…”

A new scaffold of topoisomerase I inhibitors: Design, synthesis and biological evaluation

“…Docking calculations were performed with the software PLANTS [33] . The search space was defined by using the crystallographic ligand center of mass coordinates as the binding site center (x = 49.3764, y = 46.9081 and z = 48.2818) and the binding site radius was set to 14 Å, corresponding to the ligand radius of gyration augmented by 6 Å.…”

Increasing the virtual screening quality to identify adrenergic β2 receptor ligands using classification trees

“…Istyastono and Setyaningsih [18] has performed SBVS using PLANTS1.2 as the molecular docking software [26,27] and PyPLIF to identify the PLIF bitstrings of each docked pose [28,29]. The constructed SBVS protocol was retrospectively validated using ADRB2 ligands and decoys from DUD-e [5], which consisted of 231 ADRB2 ligands and 15,000 decoys [5,18].…”

“…The constructed SBVS protocol was retrospectively validated using ADRB2 ligands and decoys from DUD-e [5], which consisted of 231 ADRB2 ligands and 15,000 decoys [5,18]. Together with the ChemPLP scores resulted from the docking software [26,27], the PLIF bitstrings were used as the predictors to build classification trees using RPART method [23]. Notably, the RPART method could result in a significantly better classification between ligands and decoy compared to previously retrospective SBVS campaign [18].…”