  Ligand diffusion through proteins is a fundamental process governing
biological signaling and enzymatic catalysis. The complex topology of protein
tunnels results in difficulties with computing ligand escape pathways by
standard molecular dynamics (MD) simulations. Here, two novel methods for
searching of ligand exit pathways and cavity exploration are proposed: memory
random acceleration MD (mRAMD), and memetic algorithms (MA). In mRAMD, finding
exit pathways is based on a non-Markovian biasing that is introduced to
optimize the unbinding force. In MA, hybrid learning protocols are exploited to
predict optimal ligand exit paths. The methods are tested on three proteins
with increasing complexity of tunnels: M2 muscarinic receptor, nitrile
hydratase, and cytochrome P450cam. In these cases, the proposed methods
outperform standard techniques that are used currently to find ligand egress
pathways. The proposed approach is general and appropriate for accelerated
transport of an object through a network of protein tunnels.
