Towards a Precise Atomic Classification of Biological Entities: Critiquing the Overgeneralization in Protein Nomenclature and Proposing "Imposter Pseudo-Proteins" for Prion-like Entities in Alzheimer's Disease

This conceptual manuscript critically evaluates the pervasive overgeneralization in biological nomenclature, specifically the term "protein," which amalgamates entities with disparate structural, functional, and dynamic profiles, ranging from passive scaffolds to autonomous, self-replicating informational propagons. Analogous to the atomic precision of the periodic table, we introduce a multidimensional classificatory system predicated on configurational energy landscapes, informational entropy measures, structural degradation kinetics, and emergent systemic behaviors. Applied to Alzheimer's disease (AD), this taxonomy distinguishes "imposter pseudo-proteins"—prion-like aggregates including misfolded amyloid-beta (A\( \beta \)), hyperphosphorylated tau, and canonical prions—from canonical proteins based on their proteolytic recalcitrance, templated autocatalysis, and cybernetic antagonism. The framework is rigorously validated via analytical derivations of an augmented susceptible-infectious-removed (SIR) epidemiological model, empirically parameterized Python simulations, multifaceted sensitivity analyses (local perturbations, global explorations, and Sobol variance decomposition), statistical quantifications, Bayesian parameter inference through bespoke Markov Chain Monte Carlo (MCMC) algorithms, and delineations of falsifiability. Reproducibility is assured via explicit code, datasets, and output-derived visualizations. Reframing AD as informational pathogenesis rather than stochastic biochemical aberration, this paradigm posits cybernetic interventions, such as targeted molecular disruptors, superseding traditional degradative therapies. Terminological refinements are advocated (e.g., "conformational optimization" supplanting "folding"; "bond scission" replacing "cleavage"), advancing an information-centric molecular biology lexicon. Integration of Bayesian neuroscience models facilitates individualized predictive diagnostics.