Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used hereto provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

Membrane proteins mediate processes that are fundamental for the flourishing of biological cells. Membrane-embedded transporters move ions and larger solutes across membranes, receptors mediate communication between the cell and its environment and membrane-embedded enzymes catalyze chemical reactions. Understanding these mechanisms of action requires knowledge of how the proteins couple to their fluid, hydrated lipid membrane environment. We present here current studies in computational and experimental membrane protein biophysics, and show how they address outstanding challenges in understanding the complex environmental effects on the structure, function and dynamics of membrane proteins.

Cholesterol-phospholipid bilayers continue to be the current state of the art in membrane models and serve as representative systems for studying the effect of cholesterol on the cell membrane. As the mixing of different lipid species requires long spatio-temporal scales, coarse-grained models have gained increasing popularity in modeling such membrane systems. In this paper, a systematic study of the MARTINI coarse-grained model for the DPPC-cholesterol binary system has been performed. We construct the phase diagram of DPPC lipid bilayers in the presence of different cholesterol concentrations and at different temperatures using coarse-grained Molecular Dynamics (MD) simulations with the MARTINI force field. The phase diagram based on the condensation effect is directly comparable to available experimental data and demonstrates qualitative agreement over all cholesterol concentrations. Self-assembled bilayers quantitatively reproduce experimental observables, such as lateral diffusion of lipids, electron density, area per lipid and lipid order parameters. The phase diagram of the DPPC-cholesterol binary system also reveals the profound effect of cholesterol on the physical properties of phospholipid bilayers such lipid order, diffusion, and fluidity. Cholesterol induces the liquid-ordered phase, which increases the fluidity of the phospholipid hydrocarbon chains above the gel to liquid-crystalline phase transition temperature and decreases it below the phase transition. The present study suggests that the MARTINI force field can be successfully used to obtain molecular level insights into cholesterol-DPPC model membranes.

Aminoadamantane derivatives, such as amantadine and rimantadine, have been reported to block the M2 membrane protein of influenza A virus (A/M2TM), but their use has been discontinued due to reported resistance in humans. Understanding the mechanism of action of amantadine derivatives could assist the development of novel potent inhibitors that overcome A/M2TM resistance. Here, we use Free Energy Perturbation calculations coupled with Molecular Dynamics simulations (FEP/MD) to rationalize the thermodynamic origin of binding preference of several aminoadamantane derivatives inside the A/M2TM pore. Our results demonstrate that apart from crucial protein-ligand intermolecular interactions, the flexibility of the protein, the water network around the ligand, and the desolvation free energy penalty to transfer the ligand from the aqueous environment to the transmembrane region are key elements for the binding preference of these compounds and thus for lead optimization. The high correlation of the FEP/MD results with available experimental data (R(2) = 0.85) demonstrates that this methodology holds predictive value and can be used to guide the optimization of drug candidates binding to membrane proteins.