TL;DR
This chapter briefly explains how amino acids influence protein structure, then describes the hierarchical levels of protein organization, and illustrates how tertiary and potential quaternary structures form from energy landscapes that direct proper folding.
This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.
Structural biology is undergoing a transformative era driven by advances in artificial intelligence (AI)-based protein structure prediction and cryo-electron microscopy. Predictive approaches have dramatically expanded structural coverage across proteomes and are increasingly integrated into experimental workflows. However, protein function frequently depends on dynamic molecular processes including ligand-dependent conformational remodeling, transient interactions, cooperative assembly, and transport-state transitions that remain difficult to define from static computational models alone. These challenges are particularly evident in plants, where signaling pathways often involve environmentally responsive receptor complexes, lineage-expanded regulatory proteins, and transient assemblies. Here, we discuss how experimental structural biology continues to advance plant biology by revealing mechanisms underlying hormone perception, immune receptor activation, transporter function, and enzymatic regulation. From early landmark discoveries such as the crystallization of urease to recent cryo-electron microscopy studies of dynamic signaling complexes, plant systems have repeatedly uncovered molecular architectures, chemically modified intermediates, and regulatory principles that require direct structural and biochemical characterization. Plant proteins also remain markedly underrepresented in structural databases, leaving many plant-specific pathways structurally unresolved. Together, these observations highlight the continuing importance of experimental structural biology for defining biologically relevant molecular states and enabling structure-guided strategies for crop improvement and agricultural biotechnology.
The authors develop a machine learning classifier through the integration of 25,000 proteomics experiments to construct a wiring diagram of human cells, which enables structural modeling of disease-relevant complexes and establishes a highly accurate protein wiring diagram of the cell.
Recent developments in integrative modeling of protein-RNA complexes are reviewed, highlighting advances in in-cell, 4D and condensate structural biology, and how these approaches shape the understanding of RNP assembly, regulation, and function in physiologically relevant environments are discussed.
It is concluded that the early emergence of the Rossmann fold reflects the chemical and physical constraints of protein folding, explaining both its profound antiquity and sustained longevity.
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