Graph-based analysis of H-bond networks and unsupervised learning reveal conformational coupling in prion peptide segments

Publication Type

Journal Article

Publication Date (Issue Year)

2024

Journal Name

Physical Chemistry Chemical Physics

Abstract

In this study, we employed a comprehensive computational approach to investigate the physical chemistry of the water networks surrounding hydrated peptide segments, as derived from molecular dynamics simulations. Our analysis uncovers a complex interplay of direct and water-mediated hydrogen bonds that intricately weave through the peptides. We demonstrate that these hydrogen bond networks encode critical information about the peptides' conformational behavior, with the dimensionality of these networks showing sensitivity to the peptides' conformations. Additionally, we estimated the free-energy landscape of the peptides across various conformations, revealing that their structures are predominantly characterized by unfolded, partially folded, and folded configurations, resulting in broad and rugged free-energy surfaces due to the numerous degrees of freedom contributed by the surrounding solvent. Importantly, the structured nature of this free-energy landscape becomes obscured when conventional collective variables, such as the number of hydrogen bonds, are used. Our findings provide new insights into the molecular mechanisms that couple protein and solvent degrees of freedom, highlighting their significance in the functioning of biological systems.

Keywords

Graph-based analysis, H-bond networks, unsupervised learning, conformational coupling, prion peptide segments

Grantee Name(s)

Robinson Musembi

Project Title

Self-cleaning solar module for enhanced electrical output

Type of Grant

Research Award

Thematic Area

Energy including Renewables

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