eprintid: 35153
rev_number: 12
eprint_status: archive
userid: 8309
dir: disk0/00/03/51/53
datestamp: 2024-07-26 08:03:03
lastmod: 2024-07-26 08:03:30
status_changed: 2024-07-26 08:03:03
type: doctoralThesis
metadata_visibility: show
creators_name: Riedmiller, Kai
title: Predicting Hydrogen Atom Transfer in Collagen
divisions: i-160001
adv_faculty: af-19
abstract: Molecular Dynamics (MD) simulation is an established method for studying bi-
ological systems and materials at the molecular level. For example, it can predict
the effects of mutations on the folding behavior of proteins, or elucidate the binding
mechanisms of drugs to receptors. To achieve this, a molecular system is modeled as
beads connected via springs or sticks. Goal of this thesis is to develop a method to
deepen our understanding of mechanoradicals undergoing hydrogen atom transfer
(HAT) reactions. Usually, no new chemical bonds can be formed during an MD
simulation, prohibiting the analysis of HAT and other reactions.
In this thesis, a method called KIMMDY is implemented, enabling reactive MD
through the use of kinetic Monte Carlo steps. While it has previously been used
for homolysis reactions, here its scope is extended to also allow HAT during an MD
simulation. First, a data set of HAT reaction barriers is crafted using quantum
mechanical (QM) calculations. Then, a graph neural network (GNN) is trained on
this data set to predict HAT reaction barriers. The inputs to the GNN are structures
sampled from MD simulations, requiring no prior optimization. Thus, the model
offers a significant speed-up compared to traditional methods, like reactive force
fields, or direct QM calculations.
Furthermore, the KIMMDY 2.0 software package is presented, which can perform
kinetic Monte Carlo driven reactive MD simulations in a flexible and user-friendly
manner. It supports HAT and homolysis reactions, and can be easily extended to any
reaction for which rates are available. For HAT, they are obtained using the afore-
mentioned GNN. However, this approach is not limited to HAT. Through the use of
machine learning models, reaction rates for arbitrary reactions become accessible,
only requiring a limited amount of QM calculations during training. In KIMMDY,
these models enable combining the accuracy and flexibility of QM calculations with
the efficiency of MD.
date: 2024
id_scheme: DOI
id_number: 10.11588/heidok.00035153
own_urn: urn:nbn:de:bsz:16-heidok-351534
date_accepted: 2024-06-21
advisor: HASH(0x561a627f0068)
language: eng
bibsort: PREDICTING
full_text_status: public
place_of_pub: Heidelberg
citation:   Riedmiller, Kai  (2024) Predicting Hydrogen Atom Transfer in Collagen.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/35153/1/main.pdf