A well-regulated metabolite concentration in biological fluids is essential for maintaining a normal physiological homeostasis. However, under conditions of metabolic imbalance, certain metabolites can accumulate and undergo self-assembly into amyloid-like supramolecular structures that may disrupt cellular functions. Understanding the aggregation behavior of such metabolites is therefore important for elucidating the molecular mechanisms underlying metabolite-associated disorders. In this study, we investigate the self-assembly properties of phenylalanine-derived metabolites, namely, phenylacetic acid (PA), phenyllactic acid (PL), and phenylpyruvic acid (PP). Experimental characterization revealed that these metabolites form diverse supramolecular assemblies near physiological pH. The amyloid-like nature of these structures was confirmed using established amyloid-binding dyes, including thioflavin T (ThT), Nile red, and Congo red. Density functional theory (DFT) calculations were employed to analyze the intermolecular interactions responsible for molecular association. In addition, 100 ns molecular dynamics simulations of multimonomer systems in explicit water demonstrated spontaneous aggregation of the metabolites, revealing distinct aggregation propensities. PP exhibited the strongest aggregation behavior, forming large supramolecular clusters (average cluster size = 18.4 ± 0.7 monomers at 100 ns), followed by PA (15.2 ± 0.9), whereas PL showed comparatively weaker association (9.7 ± 1.1). Cytotoxicity assays and flow cytometry (FACS) analysis further revealed that the self-assembled structures of PA and PP induce apoptotic cell death similar to that of phenylalanine aggregates, whereas PL displayed comparatively higher cellular compatibility even at elevated concentrations. These findings highlight distinct aggregation behaviors among phenylalanine-derived metabolites and provide new insights into their potential role in the molecular pathology of phenylketonuria (PKU), thereby contributing to a deeper understanding of metabolite-driven aggregation phenomena in neurochemical disorders.
Neelam et al. (Tue,) studied this question.