Signaling from JAK (Janus kinase) protein kinases to STAT (signal transducers and activators of transcription) transcription factors is key to many aspects of biology and medicine, yet the mechanism by which cytokine receptors initiate signaling is enigmatic. We present a complete mechanistic model for activation of receptor-bound JAK2, based on an archetypal cytokine receptor, the growth hormone receptor. For this, we used fluorescence resonance energy transfer to monitor positioning of the JAK2 binding motif in the receptor dimer, substitution of the receptor extracellular domains with Jun zippers to control the position of its transmembrane (TM) helices, atomistic modeling of TM helix movements, and docking of the crystal structures of the JAK2 kinase and its inhibitory pseudokinase domain with an opposing kinase-pseudokinase domain pair. Activation of the receptor dimer induced a separation of its JAK2 binding motifs, driven by a ligand-induced transition from a parallel TM helix pair to a left-handed crossover arrangement. This separation leads to removal of the pseudokinase domain from the kinase domain of the partner JAK2 and pairing of the two kinase domains, facilitating trans-activation. This model may well generalize to other class I cytokine receptors.

Multiscale modeling of polymers exchanges information between coarse and fine representations of molecules to capture material properties over a wide range of spatial and temporal scales. Restoring details at a finer scale requires us to generate information following embedded physics and statistics of the models at two different levels of description. Techniques designed to address this persistent challenge balance among accuracy, efficiency, and general applicability. In this work, we present an image-based approach for structural backmapping from coarse-grained to atomistic models with cis-1,4 polyisoprene melts as an illustrative example. Through machine learning, we train conditional generative adversarial networks on the correspondence between configurations at the levels considered. The trained model is subsequently applied to provide predictions of atomistic structures from the input coarse-grained configurations. The effect of different data representation schemes on training and prediction quality is examined. Our proposed backmapping approach shows remarkable efficiency and transferability over different molecular weights in the melt based on training sets constructed from oligomeric compounds. We anticipate that this versatile backmapping approach can be readily extended to other complex systems to provide high-fidelity initial configurations with minimal human intervention.

The inclusion of a nanoparticle into a polymer matrix is studied by efficient Monte Carlo simulations. The resulting structural changes in the melt and glass exhibit a strong dependence on the strength of the polymer attraction to the surface of the filler. The mechanical properties of the nanocomposite are analyzed in detail through a formalism that permits calculation of local elastic constants. The average shear and Young’s modulus of the nanocomposite are higher than those of the pure polymer for neutral or attractive nanoparticles. For repulsive particles, these moduli are lower. Simulation of local properties reveals that a glassy layer is formed in the vicinity of the attractive filler, contributing to the increased strength of the composite material. In contrast, a region of negative moduli emerges around repulsive fillers, which provides a mechanism for frustration relief and a lowering of the glass transition temperature.

A study is presented on the effects of smooth nanoparticles on the structure and elastic moduli of a polymer matrix. Structural changes between the unfilled polymer matrix and the nanocomposite give rise to the formation of a glassy layer that surrounds the nanoparticles. Results for the effects of particle size and concentration on the local and overall mechanical properties of the polymer are consistent with experimental macroscopic observations. At the molecular level, it is found that dispersed, attractive nanoparticles alter the nonaffine displacement fields that arise in the polymer glass upon deformation, thereby rendering the nanocomposite glass less fragile.