Persistent homology analysis of osmolyte molecular aggregation and their hydrogen-bonding networks

Abstract

Two types of osmolytes, i.e., trimethylamin N-oxide (TMAO) and urea, demonstrate dramatically different properties in a protein folding process. Even with the great progresses in revealing the potential underlying mechanism of these two osmolyte systems, many problems still remain unsolved. In this paper, we propose to use the persistent homology, a newly-invented topological method, to systematically study the osmolytes molecular aggregation and their hydrogen-bonding network from a global topological perspective. It has been found that, for the first time, TMAO and urea show two extremely different topological behaviors, i.e., extensive network and local cluster. In general, TMAO forms highly consistent large loop or circle structures in high concentrations. In contrast, urea is more tightly aggregated locally. Moreover, the resulting hydrogen-bonding networks also demonstrate distinguishable features. With the concentration increase, TMAO hydrogen-bonding networks vary greatly in their total number of loop structures and large-sized loop structures consistently increase. In contrast, urea hydrogen-bonding networks remain relatively stable with slight reduce of the total loop number. Moreover, the persistent entropy (PE) is, for the first time, used in characterization of the topological information of the aggregation and hydrogen-bonding networks. The average PE systematically increases with the concentration for both TMAO and urea, and decreases in their hydrogen-bonding networks. But their PE variances have totally different behaviors. Finally, topological features of the hydrogen-bonding networks are found to be highly consistent with those from the ion aggregation systems, indicating that our topological invariants can characterize intrinsic features of the "structure making" and "structure breaking" systems.

arXiv comment19 pages; 9 figures; 1 table

References in corpus (11)

• Statistical topological data analysis using persistence landscapes
• Hierarchical structures of amorphous solids characterized by persistent homology
• TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
• Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
• Persistent homology analysis of protein structure, flexibility and folding
• Persistent Homology of Complex Networks
• Topological Methods for Exploring Low-density States in Biomolecular Folding Pathways
• A statistical approach to persistent homology
• Topological classifier for detecting the emergence of epileptic seizures
• Persistent homology analysis of ion aggregation and hydrogen-bonding network
• Algebraic graph learning of protein-ligand binding affinity

Cited by in corpus (5)

• Obey validity limits of data-driven models
• A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations
• Persistent Homology for Structural Characterization in Disordered Systems
• Weighted persistent homology for osmolyte molecular aggregation and hydrogen-bonding network analysis
• Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design