TY  - UNPB
ID  - discovery10191295
UR  - https://discovery.ucl.ac.uk/id/eprint/10191295/
EP  - 194
N2  - The work presented in this thesis focuses on the use of molecular dynamics (MD) and enhanced sampling methods for investigating ligand binding poses and determining protein-ligand binding affinity. Good pre diction of the arrangement of these complexes and their strength is crucial for successful structure based drug design (SBDD) efforts so this thesis makes a significant contribution in furthering the use of computational tools in SBDD. First, chapter 3 presents OpenBPMD, an open-source Python re implementation of binding pose metadynamics (BPMD), a MD-based tool for ranking ligand poses from a set of candidates derived from dock ing. The role of accurate water positioning on the performance of the algorithm is also investigated, showed how the combination with a grand canonical Monte Carlo algorithm improves the accuracy of the predic tions. Then chapter 4 explains how the funnel metadynamics (fun-metaD) algorithm was implemented on a high-performance MD engine, OpenMM. This implementation was validated on host-guest systems. Afterwards a larger data set is interrogated, examining the effects on host-guest bind ing by varying the water model (TIP3P, OPC3 and OPC) and the partial atomic charge assignment methods, AM1-BCC and RESP. Finally, chapter 5 investigates the binding of fragment-like ligands in three different protein targets by applying fun-metaD. Advancements are made on funnel-shaped restraint automation and a new set of collective variables (CV) is tested as well. However, a lack of convergence due to an excess of metadynamic bias and missing slow degrees of freedom is observed. In order to address these issues, chapter 6 delves into apply ing a neural network-based CV, called Deep-LDA, and a novel enhanced sampling algorithm, termed on-the-fly probability-enhanced sampling. Although smooth converging, some issues in pose discrimination still re main.
AV  - public
TI  - Calculating Absolute Ligand Binding Free Energies and Ranking Ligand Binding Poses Using Metadynamics enabled Molecular Dynamics Simulations
M1  - Doctoral
Y1  - 2024/04/28/
A1  - Lukauskis, Dominykas
PB  - UCL (University College London)
N1  - Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author?s request.
ER  -