Ligand diffusion through a protein interior is a fundamental process governing biological signaling and enzymatic catalysis. A complex topology of channels in proteins leads often to difficulties in modeling ligand escape pathways by classical molecular dynamics simulations. In this paper two novel memetic methods for searching the exit paths and cavity space exploration are proposed: Memory Enhanced Random Acceleration (MERA)Molecular Dynamics and Immune Algorithm (IA ). In MERA, a pheromone concept is introduced to optimize an expulsion force. In IA , hybrid learning protocols are exploited to predict  ligand exit paths. They are tested on three protein channels with increasing complexity  : M2 muscarinic GPCR receptor, enzyme nitrile hydrataseand heme-protein cytochrome P450cam. In these cases, the memetic methods outperform Simulated Annealing and Random Acceleration Molecular Dynamics . The proposed algorithms are general and appropriate in all problems where an accelerated transport of an object through a network of channels is studied .
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 .
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