Memetic Algorithms for Ligand Expulsion from Protein Cavities
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.