Dynamic, Weighted, Hierarchical Graph Analysis for Predicting Peptide Aggregate Instability and Identifying Molecular Determinants

Abstract: Understanding the stability and dynamics of peptide self-assemblies is crucial for designing functional biomaterials, yet predicting aggregate instability and identifying the specific molecular interactions that govern it remains a significant challenge. Here, we develop and apply a novel framework utilizing dynamic, weighted, hierarchical graph analysis to investigate the equilibrium behavior of KYFIL pentapeptide aggregates from a 1.3 $\mu$s molecular dynamics simulation. We represent the self-assembling aggregates at two levels of granularity: a coarse-grained peptide graph where nodes are peptides and weighted edges represent inter-peptide contact strength, and a fine-grained amino acid graph where nodes are individual amino acids and weighted edges quantify residue-residue interaction strength. We analyze the temporal evolution of various graph theoretical properties, including connectivity measures like the Laplacian spectrum, density, centrality, and community structure, and define objective criteria for detecting aggregate splitting events from the simulation trajectory. Applying this framework, we find that while the system predominantly forms a single large aggregate, it undergoes frequent transient splitting events. Crucially, we demonstrate that dynamic changes in graph properties serve as predictive signatures for impending splitting events within a nanosecond timescale; specifically, decreases in coarse-grained aggregate connectivity (Fiedler value) and density, and a significant decline in the weighted sum of fine-grained residue-residue contacts bridging future fragments, precede fragmentation. Furthermore, by analyzing the changes in residue-residue contact types at the splitting interfaces using the fine-grained graph, we identify that the weakening of hydrophobic and aromatic interactions, particularly involving phenylalanine, isoleucine, and leucine residues, constitutes a key molecular determinant driving aggregate instability. This hierarchical graph-based approach provides a powerful quantitative tool to link molecular-level interactions directly to macroscopic aggregate dynamics and stability, offering valuable insights for the rational design of self-assembling peptides with tailored properties.

Subjects:	q-bio.BM; physics.chem-ph
Cite as:	PX:2508.00030