Dynamic Weighted Peptide Network Analysis for Characterizing and Predicting Aggregate Stability

Abstract: Peptide self-assembly is a complex dynamic process, and characterizing and predicting aggregate stability and transitions remain significant challenges often limited by traditional coarse-grained or binary metrics. We address this by representing the peptide system as a dynamic weighted graph where nodes are peptides and edges quantify inter-peptide noncovalent contacts, weighted by type (hydrophobic, aromatic, hydrogen bonds). We analyze the temporal evolution of this network using graph theoretical metrics. Using molecular dynamics simulations of KYFIL pentapeptides, we studied aggregate behavior from 100 ns onwards by constructing dynamic weighted and binary graphs and calculating metrics including weighted graph Laplacian spectral properties (Fiedler value), global properties (density, connected components, largest connected component or LCC size). We correlated these graph metrics with LCC physical properties such as radius of gyration and packing score, and compared results to binary graph analysis. Our analysis reveals significant dynamic fluctuations in aggregate structure and size. Weighted graph metrics, particularly the LCC Fiedler value and density, demonstrate greater sensitivity to interaction strengths compared to their binary counterparts. Both weighted and binary graph metrics correlate significantly with LCC physical properties, indicating that the network structure effectively captures aggregate compactness. System-level analysis confirms the presence of multiple dynamic clusters. A combined graph-based order parameter for the LCC was developed, showing potential for tracking aggregate state transitions. This dynamic weighted graph analysis provides a robust quantitative framework for characterizing peptide aggregates and identifies promising metrics that can serve as sensitive indicators and potential predictive order parameters for aggregate stability and fragmentation.

Subjects:	q-bio.BM; physics.chem-ph
Cite as:	PX:2508.00029