Allosteric regulation of proteins continues to be an engaging research topic for the scientific community. Models describing allosteric communication have evolved from focusing on conformation-based descriptors of protein structural changes to appreciating the role of internal protein dynamics as a mediator of allostery. Here, we explain a “violin model” for allostery as a contemporary method for approaching the Cooper–Dryden model based on redistribution of protein thermal fluctuations. Based on graph theory, the violin model makes use of community network analysis to functionally cluster correlated protein motions obtained from molecular dynamics simulations. This Review provides the theory and workflow of the methodology and explains the application of violin model to unravel the workings of protein kinase A.

The advent of AlphaFold and consumer large language models have elicited unprecedented development of artificial intelligence (AI). AI has had substantial impact in every area of research, including in molecular biology. This is principally in thanks to contributions to the Protein Data Bank and various genome sequence databases, providing an astronomical amount of data for model training. These databases contain evolutionary information explicitly and implicitly, allowing accurate predictions and deep insights into biological questions. Here, we describe recent state-of-the-art applications of AI that exploit evolutionary relationships. This includes structure prediction and design, conformational ensemble generation, and functional site identification. We present a brief snapshot of AI usage in studying protein structure and dynamics, a field that is advancing at breakneck speed.

Cyclic-AMP-dependent protein kinase A (PKA) is a critical enzyme involved in various signaling pathways that plays a crucial role in regulating cellular processes including metabolism, gene transcription, cell proliferation, and differentiation. In this study, the mechanisms of allostery in PKA were investigated by analyzing the vast repertoire of crystal structures available in the RCSB database. From existing structures of murine and human PKA, we elucidated the conformational ensembles and protein dynamics that are altered in a ligand-dependent manner. Distance metrics to analyze conformations of the G-loop were proposed to delineate different states of PKA and were compared to existing structural metrics. Furthermore, ligand-dependent flexibility was investigated through normalized B′-factors to better understand the inherent dynamics in PKA. The presented study provides a contemporary approach to traditional methods in engaging the use of crystal structures for understanding protein dynamics. Importantly, our studies provide a deeper understanding into the conformational ensemble of PKA as the enzyme progresses through its catalytic cycle. These studies provide insights into kinase regulation that can be applied to both PKA individually and protein kinases as a class.