In the drug development process, predicting the complex structure of a small ligand with a protein, the so-called protein-ligand-docking-problem, is extensively used in virtual screening of large databases and in lead optimization. Given a protein structure, a ligand structure and a scoring function, the goal is to find a low energy ligand conformation in the protein’s binding site that corresponds to the global minimum of the scoring function. The docking algorithm PLANTS is based on a class of stochastic optimization algorithms called ant colony optimization (ACO). ACO is inspired by the behavior of real ants finding a shortest path between their nest and a food source. The ants use indirect communication in the form of pheromone trails which mark paths between the nest and a food source. In the case of protein-ligand docking, an artificial ant colony is employed to find a minimum energy conformation of the ligand in the binding site. These ants are used to mimic the behavior of real ants and mark low energy ligand conformations with pheromone trails. The artificial pheromone trail information is modified in subsequent iterations to generate low energy conformations with a higher probability.
ACO-based search engine
two scoring functions (PLANTSCHEMPLP and PLANTSPLP)
flexible protein side-chains
rigid-body docking of multiconformer libraries into rigid and flexible receptors
docking with selected explicit, displaceable water molecules
fully automatic ligand setup (rotatable bond identification, atom typing …)
To run the basic docking example, download a PLANTS version, unzip simple_dock.zip and run:
./PLANTS –mode screen plantsconfig
The docking output will be stored in the directory results
It is recommended to preprocess the protein and ligand input structures used for docking with SPORES.