Multilevel frameworks for studying protein energy landscapes

Proteins, which constitute over 50% of dry cell mass, are essential to nearly all biochemical processes, with their functionality depending on correct folding into native structures. Accurately simulating their structural dynamics over biologically relevant timescales remains beyond the reach of quantum mechanical methods. To address this problem, mechanical force fields and coarse-grained models have been developed to reduce the number of interaction sites. While advances like AlphaFold have improved native structure prediction, understanding thermodynamic stability and folding pathways remains a major challenge. This thesis introduces multilevel computational frameworks that integrate diverse theoretical approaches (Chapter 2) to analyse protein energy landscapes. A key development is the lwONIOM library (Chapter 3), a freely accessible, multilevel, multicentre tool enabling accurate and efficient analysis of complex proteins over extended timescales. Applied to the bovine pancreatic trypsin inhibitor (BPTI), lwONIOM achieves a balance between computational cost and precision, providing deep insights into structural stability and dynamic behaviour. Energy landscapes described by the AMBER and UNRES potentials are also analysed. In parallel, the UNRES coarse-grained potential was integrated into the Cambridge energy landscape software, enabling efficient modelling of large biomolecular structures (Chapter 4). This method preserves accuracy while substantially reducing computational cost, yielding results consistent with experiment and all-atom models. UNRES includes a dynamic disulphide bond potential, allowing bond formation and breakage to be explored within a single landscape, and has recently been extended to model explicit lipids within the UNICORN framework. Additionally, we applied machine learning potential, coarse-grained energy landscape searches, and structural analysis to study amyloid monomers associated with Alzheimer’s disease (Chapter 5). These multilevel frameworks thus provide robust tools for the study of complex biomolecular systems, with potential applications in drug discovery and health-related research.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2025

Author dc:creator

• Wesolowski, Patryk

Advisor dc:contributor.advisor

• Wales, David

• energy landscapes
• multilevel frameworks
• proteins

Licence

• https://www.repository.cam.ac.uk/bitstreams/658df38f-16ff-4c7a-9a78-5fa9e6829029/download
• https://creativecommons.org/licenses/by/4.0/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.122277

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/390946