Covalent hitchhikers guide proteins to the nucleus.

In this issue of Cell Chemical Biology, Peng and Weerapana1 report the combination of chemoproteomic and proximity-based labeling approaches to identify cysteines in nuclear proteins that are reactive toward electrophilic probe compounds. They apply this novel technology to identify proteins that are localized to the nucleus and chromatin upon probe labeling.

Stimulus-Induced Relief of Intentionally Incorporated Frustration Drives Refolding of a Water-Soluble Biomimetic Foldamer.

Frustrated, or nonoptimal, interactions have been proposed to be essential to a protein's ability to display responsive behavior such as allostery, conformational signaling, and signal transduction. However, the intentional incorporation of frustrated noncovalent interactions has not been explored as a design element in the field of dynamic foldamers. Here, we report the design, synthesis, characterization, and molecular dynamics simulations of the first dynamic water-soluble foldamer that, in response to a stimulus, exploits relief of frustration in its noncovalent network to structurally rearrange from a pleated to an intercalated columnar structure. Thus, relief of frustration provides the energetic driving force for structural rearrangement. This work represents a previously unexplored design element for the development of stimulus-responsive systems that has potential application to materials chemistry, synthetic biology, and molecular machines.

A model of proto-object based saliency.

Organisms use the process of selective attention to optimally allocate their computational resources to the instantaneously most relevant subsets of a visual scene, ensuring that they can parse the scene in real time. Many models of bottom-up attentional selection assume that elementary image features, like intensity, color and orientation, attract attention. Gestalt psychologists, however, argue that humans perceive whole objects before they analyze individual features. This is supported by recent psychophysical studies that show that objects predict eye-fixations better than features. In this report we present a neurally inspired algorithm of object based, bottom-up attention. The model rivals the performance of state of the art non-biologically plausible feature based algorithms (and outperforms biologically plausible feature based algorithms) in its ability to predict perceptual saliency (eye fixations and subjective interest points) in natural scenes. The model achieves this by computing saliency as a function of proto-objects that establish the perceptual organization of the scene. All computational mechanisms of the algorithm have direct neural correlates, and our results provide evidence for the interface theory of attention.