eprintid: 10191295
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/12/95
datestamp: 2024-05-17 09:13:32
lastmod: 2024-05-17 09:13:32
status_changed: 2024-05-17 09:13:32
type: thesis
metadata_visibility: show
sword_depositor: 699
creators_name: Lukauskis, Dominykas
title: Calculating Absolute Ligand Binding Free Energies and Ranking Ligand Binding Poses Using Metadynamics enabled Molecular Dynamics Simulations
ispublished: unpub
divisions: UCL
divisions: B04
divisions: C06
divisions: F56
note: 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.
abstract: 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.
date: 2024-04-28
date_type: published
oa_status: green
full_text_type: other
thesis_class: doctoral_open
thesis_award: Ph.D
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2270584
lyricists_name: Lukauskis, Dominykas
lyricists_id: DLUKA80
actors_name: Lukauskis, Dominykas
actors_name: Jayawardana, Anusha
actors_id: DLUKA80
actors_id: AJAYA51
actors_role: owner
actors_role: impersonator
full_text_status: public
pages: 194
institution: UCL (University College London)
department: Chemistry
thesis_type: Doctoral
citation:        Lukauskis, Dominykas;      (2024)    Calculating Absolute Ligand Binding Free Energies and Ranking Ligand Binding Poses Using Metadynamics enabled Molecular Dynamics Simulations.                   Doctoral thesis  (Ph.D), UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10191295/1/Lukauskis_Thesis.pdf