Memetic Algorithms for Ligand Expulsion from Protein Cavities

TL;DR

This paper introduces two innovative computational methods, mRAMD and memetic algorithms, to efficiently identify ligand exit pathways in proteins, surpassing traditional molecular dynamics simulations in complex tunnel systems.

Contribution

The paper presents novel algorithms, mRAMD and memetic algorithms, for exploring ligand egress pathways, improving accuracy and efficiency over existing methods.

Findings

Methods outperform standard techniques in complex tunnel systems

Effective in three different proteins with increasing tunnel complexity

General approach applicable to accelerated transport in protein networks

Abstract

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.