Assembling a toolkit for computational dissection of dense protein systems
(2021)- Abstract
- The cellular interior is a dense environment. Understanding how such an environment impacts the properties of proteins and other macromolecules, as well as how weak, non-specific interactions drive processes such as protein droplet formation through liquid-liquid phase separation, is a major challenge in biological physics. The complexity of this environment often makes experimental studies extremely challenging, leaving an important niche to be filled by simulation studies.
Simulations do, however, have their own set of challenges, and to use them to their full potential, a suitable set of computational tools must be developed. Such a toolset must include accurate yet computationally affordable force fields, computationally... (More) - The cellular interior is a dense environment. Understanding how such an environment impacts the properties of proteins and other macromolecules, as well as how weak, non-specific interactions drive processes such as protein droplet formation through liquid-liquid phase separation, is a major challenge in biological physics. The complexity of this environment often makes experimental studies extremely challenging, leaving an important niche to be filled by simulation studies.
Simulations do, however, have their own set of challenges, and to use them to their full potential, a suitable set of computational tools must be developed. Such a toolset must include accurate yet computationally affordable force fields, computationally efficient simulation algorithms, and analysis tools that allow for the extraction of meaningful information from the simulation results.
In this thesis, a number of tools for all three areas are developed and/or evaluated. We present an atom level, implicit solvent force field, as well as a coarse-grained continuous HP model which we use for droplet formation studies. We investigate sampling issues in field theory simulations with the complex Langevin equation. We use finite-size scaling analysis to analyse simulations of liquid-liquid phase separation, and Markov state modeling to analyse crowding simulations.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ee153d7a-7d74-4ab5-ab2d-3e16408be59f
- author
- Nilsson, Daniel LU
- supervisor
- opponent
-
- Doctor Virnau, Peter, Johannes Gutenberg University Mainz, Germany
- organization
- publishing date
- 2021
- type
- Thesis
- publication status
- published
- subject
- keywords
- macromolecular crowding, liquid-liquid phase separation, Monte Carlo simulation, time-lagged independent component analysis, finite-size scaling, polymer field theory, protein force field, Fysicumarkivet A:2021:Nilsson
- pages
- 128 pages
- publisher
- Lund University (Media-Tryck)
- defense location
- Lundmarksalen, Astronomihuset. Join via zoom: https://www.atp.lu.se/calendar/phd-defence-daniel-nilsson
- defense date
- 2021-10-22 10:00:00
- ISBN
- 978-91-8039-019-4
- 978-91-8039-018-7
- language
- English
- LU publication?
- yes
- id
- ee153d7a-7d74-4ab5-ab2d-3e16408be59f
- date added to LUP
- 2021-09-22 16:01:56
- date last changed
- 2022-08-18 10:49:09
@phdthesis{ee153d7a-7d74-4ab5-ab2d-3e16408be59f,
abstract = {{The cellular interior is a dense environment. Understanding how such an environment impacts the properties of proteins and other macromolecules, as well as how weak, non-specific interactions drive processes such as protein droplet formation through liquid-liquid phase separation, is a major challenge in biological physics. The complexity of this environment often makes experimental studies extremely challenging, leaving an important niche to be filled by simulation studies. <br/><br/>Simulations do, however, have their own set of challenges, and to use them to their full potential, a suitable set of computational tools must be developed. Such a toolset must include accurate yet computationally affordable force fields, computationally efficient simulation algorithms, and analysis tools that allow for the extraction of meaningful information from the simulation results.<br/><br/>In this thesis, a number of tools for all three areas are developed and/or evaluated. We present an atom level, implicit solvent force field, as well as a coarse-grained continuous HP model which we use for droplet formation studies. We investigate sampling issues in field theory simulations with the complex Langevin equation. We use finite-size scaling analysis to analyse simulations of liquid-liquid phase separation, and Markov state modeling to analyse crowding simulations.<br/>}},
author = {{Nilsson, Daniel}},
isbn = {{978-91-8039-019-4}},
keywords = {{macromolecular crowding; liquid-liquid phase separation; Monte Carlo simulation; time-lagged independent component analysis; finite-size scaling; polymer field theory; protein force field; Fysicumarkivet A:2021:Nilsson}},
language = {{eng}},
publisher = {{Lund University (Media-Tryck)}},
school = {{Lund University}},
title = {{Assembling a toolkit for computational dissection of dense protein systems}},
url = {{https://lup.lub.lu.se/search/files/102773552/Daniel_Nilsson_komplett.pdf}},
year = {{2021}},
}