Proteins are dynamic biomolecules in which structure, function, and regulation are shaped by a complex interplay of amino acid sequence and long-range intramolecular communication. Both reversible and permanent alterations to protein primary structure can impact evolutionary fitness, structural stability, and functional interactions. Quantitative frameworks designed to analyze these changes have provided foundational tools for understanding evolutionary patterns and guiding sequence analysis. Despite their utility, these models often lack mechanistic insight and fail to account for spatial and dynamic dependencies that influence residue interactions and functional outcomes. To address these limitations, modern protein science increasingly emphasizes allostery and molecular dynamics (MD) simulations as integrative frameworks for understanding protein behavior. Classical allosteric models, including the Monod–Wyman–Changeux (MWC) and Koshland–Némethy–Filmer (KNF) models, established fundamental concepts describing cooperative ligand binding and conformational transitions but remain limited in their ability to capture the full complexity of protein dynamics. Contemporary ensemble-based and network-driven models extend these theories by incorporating conformational heterogeneity, energy landscapes, and dynamic coupling between distal residues. MD simulations further enable atomistic characterization of protein motions across biologically relevant timescales, providing a computational approach to bridge structural and functional analyses. Together, these approaches offer a comprehensive framework for understanding protein evolution, regulation, and functional adaptability, while advancing applications in drug discovery and disease characterization.
White et al. (Fri,) studied this question.