Evolutionary Drivers and Functional Consequences of Plant Protein Dynamics

Plant proteins orchestrate vital functions through intricate conformational dynamics, governing processes from photosynthesis to stress resilience. Understanding how these dynamic properties evolve is fundamental to deciphering plant adaptation and domestication. This Research Topic focuses explicitly on the co-evolution of plant protein sequence, structure, and dynamics, driven by genomic changes and shaped by natural selection. We aim to bridge the gap between genomics, cutting-edge protein structure prediction, and experimental characterization of dynamics. By leveraging the explosion of plant genomic data and revolutionary AI-driven structure prediction tools, we seek to uncover how evolutionary pressures reshape protein flexibility, allosteric networks, and conformational landscapes, ultimately enabling novel or optimized physiological functions crucial for plant survival and productivity.

This Research Topic aims to bridge the critical gap between plant genome evolution, protein structures, and the experimental/computational characterization of protein dynamics. We seek contributions that elucidate how evolutionary forces, acting on genomic sequences, drive changes in protein structure and – crucially – dynamics, leading to functional adaptation. Leveraging recent transformative advances in plant genomics, deep learning-based structure prediction, and techniques for probing dynamics, we encourage studies that integrate these domains to uncover the principles governing the evolution of conformational flexibility, allostery, and functional landscapes in plant proteins, linking them to phenotypes like stress tolerance or yield.

We welcome original research, reviews, methods, and perspectives that integrate genomics, structural bioinformatics, and protein dynamics to study evolution in plant systems. Key themes include:

• Studies linking WGD, gene family expansion/contraction, positive selection signals (from genomic data) to predicted or experimentally observed differences in protein structure and dynamics among orthologs/paralogs.

• Research combining phylogenetics, structural prediction/comparison, and experimental/computational (MD) dynamics characterization to reveal how sequence changes alter dynamics and enable functional adaptation (e.g., in photosynthesis, hormone signaling, stress sensors, defense proteins).

• Studies exploiting evolutionary insights, predicted structures, and dynamic models for protein engineering, optimizing enzyme catalysis, or designing crops with enhanced resilience/traits.