Allostery is the process by which biological macromolecules transmit the effect of binding at one site to another, often distal, functional site, allowing for regulation of activity. Recent experimental observations have shown that allostery can be facilitated by dynamic and intrinsically disordered proteins, leading to a new paradigm focusing on the conformational ensemble and statistical interactions. Analysis of allosteric ensembles reveals diverse regulatory strategies and a framework to unify allosteric mechanisms across systems. Allostery, first described over 50 years ago, remains central in biology due to its role in cellular signaling and disease. Despite its importance, allosteric mechanisms remain a biophysical enigma. Allosteric concepts have evolved over decades as experimental technologies improved. Early models based on static structures were influenced by static images, but recent studies have shown that dynamic and disordered systems are crucial for allostery. Recent discoveries highlight how nature uses conformational heterogeneity to facilitate allostery, challenging traditional understanding. Allostery can involve changes in dynamics and large-scale conformational disorder. The ensemble nature of allostery allows for a rich repertoire of regulatory strategies and illuminates key principles for describing allosteric mechanisms. The dynamic continuum of allostery includes rigid body structural changes to intrinsically disordered proteins. NMR spectroscopy has provided insights into protein dynamics and thermodynamics, revealing the role of conformational entropy in allostery. The CAP system and PDZ domain examples demonstrate how conformational entropy influences allosteric responses. Local unfolding and intrinsic disorder play significant roles in allostery. Examples include the enzyme AAC and the tetracycline repressor, where local unfolding and intrinsic disorder mediate allostery. Protein intrinsic disorder challenges the structure-function paradigm, as IDPs lack stable tertiary structure yet function. IDPs are hyper-abundant in transcription factors and signaling pathways, with features suitable for allosteric regulation. Allosteric ligands remodel the energy landscape of proteins, affecting conformational states and interactions. The ensemble view of allostery highlights the statistical nature of allosteric coupling, with different states contributing to overall behavior. The ensemble model suggests that allosteric mechanisms are more statistical than deterministic, with the potential for positive or negative coupling between binding sites. The ensemble representation of allostery is crucial for understanding the statistical nature of allosteric coupling. It highlights the importance of considering multiple states and their probabilities, rather than a single structure. This approach has implications for protein design and evolution, as it suggests that manipulating domain equilibria and coupling can lead to new allosteric functions. The ensemble model also provides a framework for interpreting long-time molecular dynamics simulations and electron microscopy data, revealing multiple conformational states of macromolecules. Integrating computation, structure, and different models of allostery and function is critical for understanding signaling in large macromolecular complexes. The ensemble natureAllostery is the process by which biological macromolecules transmit the effect of binding at one site to another, often distal, functional site, allowing for regulation of activity. Recent experimental observations have shown that allostery can be facilitated by dynamic and intrinsically disordered proteins, leading to a new paradigm focusing on the conformational ensemble and statistical interactions. Analysis of allosteric ensembles reveals diverse regulatory strategies and a framework to unify allosteric mechanisms across systems. Allostery, first described over 50 years ago, remains central in biology due to its role in cellular signaling and disease. Despite its importance, allosteric mechanisms remain a biophysical enigma. Allosteric concepts have evolved over decades as experimental technologies improved. Early models based on static structures were influenced by static images, but recent studies have shown that dynamic and disordered systems are crucial for allostery. Recent discoveries highlight how nature uses conformational heterogeneity to facilitate allostery, challenging traditional understanding. Allostery can involve changes in dynamics and large-scale conformational disorder. The ensemble nature of allostery allows for a rich repertoire of regulatory strategies and illuminates key principles for describing allosteric mechanisms. The dynamic continuum of allostery includes rigid body structural changes to intrinsically disordered proteins. NMR spectroscopy has provided insights into protein dynamics and thermodynamics, revealing the role of conformational entropy in allostery. The CAP system and PDZ domain examples demonstrate how conformational entropy influences allosteric responses. Local unfolding and intrinsic disorder play significant roles in allostery. Examples include the enzyme AAC and the tetracycline repressor, where local unfolding and intrinsic disorder mediate allostery. Protein intrinsic disorder challenges the structure-function paradigm, as IDPs lack stable tertiary structure yet function. IDPs are hyper-abundant in transcription factors and signaling pathways, with features suitable for allosteric regulation. Allosteric ligands remodel the energy landscape of proteins, affecting conformational states and interactions. The ensemble view of allostery highlights the statistical nature of allosteric coupling, with different states contributing to overall behavior. The ensemble model suggests that allosteric mechanisms are more statistical than deterministic, with the potential for positive or negative coupling between binding sites. The ensemble representation of allostery is crucial for understanding the statistical nature of allosteric coupling. It highlights the importance of considering multiple states and their probabilities, rather than a single structure. This approach has implications for protein design and evolution, as it suggests that manipulating domain equilibria and coupling can lead to new allosteric functions. The ensemble model also provides a framework for interpreting long-time molecular dynamics simulations and electron microscopy data, revealing multiple conformational states of macromolecules. Integrating computation, structure, and different models of allostery and function is critical for understanding signaling in large macromolecular complexes. The ensemble nature