Next-Generation Protein Sequencing: Nanopore and AI-Assisted Decoding of Posttranslational Modifications—A Narrative Review

BACKGROUND Proteins are versatile biomolecules whose functionality is intrinsically linked to their structural hierarchy—primary, secondary, and tertiary organization. Advances in structural genomics and integrative structural biology have significantly improved our ability to determine protein structures experimentally and computationally. However, challenges remain in translating structural novelty into functional understanding, particularly across diverse protein superfamilies. OBJECTIVE This narrative review synthesizes the state-of-the-art experimental and computational methodologies used in protein structure elucidation, highlighting their implications for understanding structure–function relationships, functional divergence, and the predictive challenges associated with structural genomics. METHODOLOGICAL SCOPE Primary structure: Classical sequencing techniques (Edman degradation, dansyl chloride assays) and modern mass spectrometry-based peptide mapping. SECONDARY STRUCTURE Circular dichroism spectroscopy for probing α-helices and β-sheets, monitoring conformational transitions, and evaluating thermostability. TERTIARY STRUCTURE High-resolution approaches, including X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, complemented by structural classification frameworks such as SCOP, CATH, and SFLD. ILLUSTRATIVE FRAMEWORKS Representative workflows are provided to demonstrate standard experimental pipelines. These highlight how structural data at multiple levels—from amino acid sequence to three-dimensional fold—inform the study of active-site geometry, protein–protein interactions, and functional divergence within superfamilies. CONCLUSION Integrating experimental methods with computational prediction creates a robust framework for annotating novel proteins, identifying structure-based determinants of specificity, and exploring evolutionary trajectories. Such insights are critical for drug discovery, understanding disease mechanisms, and advancing functional genomics. Continued development of AI-based prediction tools and curated functional databases (e.g., SFLD and PANTHER) will further enhance the translation of structural data into biological insight.