Bonding as a swarm: applying bee nest-site selection behaviour to protein docking

The identification of protein binding sites and the prediction of protein-ligand complexes play a key role in the pharmaceutical drug design process and many domains of life sciences. Computational approaches for protein-ligand docking (or molecular docking) have received increased attention over the last years as they allow inexpensive and fast prediction of protein-ligand complexes. Here we introduce the principle of Bee Nest-Site Selection Optimisation (BNSO), which solves optimisation problems using a novel scheme inspired by the nest-site selection behaviour found in honeybees. Moreover, the first BNSO algorithm -- Bee-Nest -- is proposed and applied to molecular docking. The performance of Bee-Nest is tested on 173 docking instances from the PDBbind core set and compared to the performance of three reference algorithms. The results show that Bee-Nest could find ligand poses with very small energy levels. Interestingly, the reference Particle Swarm Optimization (PSO) produces results that are qualitatively closer to wet-lab experimentally derived complexes but have higher energy levels than the results found by Bee-Nest. Our results highlight the superior performance of Bee-Nest in semi-local optimization for the molecular docking problem and suggests Bee-Nest's usefulness in a hybrid strategy.