Allosteric regulation, that is the regulation of a protein by binding an effector molecule at a site other than the active site, is a fundamental mechanism employed by cells to control critical processes such as signal transduction, catalysis, and gene regulation [1-3]. While studies of allosteric regulation have often focused on thermodynamic aspects, there has been an increasing realization of the role of conformational dynamics [4-5]. Recent advances in NMR [6], cryo electron microscopy [7-8] and biophysical techniques that enabled detailed investigations of large protein systems at atomic resolution have fueled the resurgence of computational and theoretical studies of allosteric regulation, leading to new conceptual outlooks of this long-standing biological phenomenon [9]. High-pressure NMR experiments can be used to detect of low-lying excited functional states, providing another tool to investigate the dynamic energy landscapes sculpted to regulate protein functions via allosteric mechanisms [10]. The resolution revolution in cryo-electron microscopy (cryo-EM) has unleashed unparalleled insight into structures, dynamics and function of complex bimolecular systems, including GPCRs, molecular chaperones and others. Single-particle cryo-EM enables the study of molecules in near-native environments, making it an essential tool for understanding how drugs and their target proteins may interact in the body [7-8]. Simulation-based computational approaches including network modeling and Markov state models as well as coevolutionary networks and machine learning allow substantive comparative studies of allosteric networks of regulation and are increasingly being combined with NMR and cryo-EM investigations. The advanced algorithms developed in the areas of machine learning and statistical approaches such as hidden Bayesian models are starting to be actively applied to facilitate cryo-EM data analysis and integrate molecular simulations to capture conformational dynamics and allostery [11-12]. The adaptation, evolution and innovation of cutting-edge machine learning approaches might also contribute to releasing the great potential of single-molecule cryo-EM approaches and expand the applicability of this technology to meet the challenges of protein conformational dynamics and allosteric regulation.
As allosteric regulation is increasingly being used to target “undruggable” pharmacological targets, the combined computational and experimental advances could greatly benefit drug discovery for complex diseases [13-14].
The overarching idea of this meeting is to build on the success of our previous CECAM Workshop on “Multiscale simulations of allosteric regulatory mechanisms…” (2018) and expand the successful discussion on the interactions between theory, computation and experiment with a particular focus including recent breakthroughs to understand regulatory processes in the cell and quantify allosteric effects. A significant emphasis will be given to the combination of emerging structural biology technologies (Cryo-EM, single molecule spectroscopy, high-pressure NMR) and advanced statistical and simulation approaches as well as to the development of strategies for leveraging artificial intelligence and machine learning tools to dissect the interplay between protein conformational landscapes, cellular regulatory processes and drug discovery.