Disentangling Intra-chain Folding from Inter-chain Assembly through Multidimensional Visualization

Characterizing the conformational landscapes of intrinsically disordered proteins is essential for elucidating their roles in health and disease, but remains challenging due to their structural heterogeneity. In this study, we introduce an enhanced algorithmic framework for the Energy Landscape Visualization Method (ELViM) that enables the simultaneous mapping of monomeric and oligomeric conformational spaces within a unified metric space. Unlike traditional reaction-coordinate-based approaches, this extended ELViM implementation employs a distance-based similarity metric and force projection embedding to generate low-dimensional representations that preserve high-dimensional structural relationships without predefined bias. We expanded the standard workflow by integrating a density-guided Local Conformational Signature analysis coupled with a new consensus inter-chain contact mapping protocol. This development allows for the systematic disentanglement of intra-chain folding dynamics from the inter-chain assembly interactions that drive protein aggregation. Using replica exchange molecular dynamics data for a 19-residue fragment of the tau protein, we demonstrate the method's utility by comparing the conformational landscapes of the wild type and the aggregation-associated P301L mutant. ELViM effectively captures continuous conformational transitions and highlights key structural motifs, including the aggregation-prone PHF6 segment (VQIVYK). The analysis reveals how the P301L mutation acts as a structural stabilizer, shifting conformational preferences toward compact, pre-organized ensembles with more persistent inter-chain contacts around PHF6. By providing a quantitative yet intuitive framework for exploring heterogeneous ensembles, this enhanced ELViM workflow offers broad applicability to studying folding landscapes, protein-protein interactions, and complex aggregation pathways.