Development and application of grand canonical methods for molecular dynamics simulations

Abstract

The work presented in this thesis focuses on the use of grand canonical Monte Carlo (GCMC) sampling during molecular dynamics (MD) simulations (referred to as GCMC/MD), which is used in this work with the aim of enhancing the sampling of water molecules at buried protein-ligand interfaces. Several developments in both the methodology and implementation of GCMC are presented, as well as insights into the binding of drugs to an influenza protein. First, a Python module (grand) is presented in chapter 3, which was developed during this work to allow GCMC sampling of water molecules to be carried out with the OpenMM software package. This implementation of GCMC was thoroughly tested in terms of reproduction of bulk water densities, as well as a rigorous statistical validation. In chapter 4, GCMC/MD simulations are applied to the M2 protein, which is an influenza drug target, where water is thought to play a key role in ligand binding. Insights are provided into how water affects the binding of different ligand enantiomers to the M2 channel, as well as the possible role of water networks in the resistance of M2 to some drugs, which may aid in the design of future inhibitors. In chapter 5, it is shown that nonequilibrium candidate Monte Carlo (NCMC) can be used to drastically increase the acceptance rates of GCMC moves — referred to as grand canonical nonequilibrium candidate Monte Carlo (GCNCMC) — by allowing the environment to relax in response to a proposed water insertion or deletion. Whilst these moves are more expensive, they can be up to five times more efficient than traditional GCMC. In chapter 6, it is shown that this improvement greatly facilitates grand canonical sampling of molecules larger than water, indicating that GCNCMC sampling of molecular fragments could have applications in computer-aided drug design.

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Identifiers

ORCID for Marley Samways: orcid.org/0000-0001-9431-8789

ORCID for Jonathan Essex: orcid.org/0000-0003-2639-2746

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