Here we introduce Z-lock, an optogenetic approach for reversible, light-controlled steric inhibition of protein active sites. The LOV domain and Zdk, a small protein that binds LOV selectively in the dark, are appended to the protein of interest where they sterically block the active site. Irradiation causes LOV to change conformation and release Zdk, exposing the active site. Computer-assisted protein design was used to optimize linkers and Zdk-LOV affinity, for both effective binding in the dark, and effective light-induced release of the intramolecular interaction. Z-lock cofilin was shown to have actin severing ability in vitro, and in living cancer cells it produced protrusions and invadopodia. An active fragment of the tubulin acetylase αTAT was similarly modified and shown to acetylate tubulin upon irradiation.

Cell migration refers to the ability of cells to translocate across a substrate or through a matrix. To achieve net movement requires spatiotemporal regulation of the actin cytoskeleton. Computational approaches are necessary to identify and quantify the regulatory mechanisms that generate directed cell movement. To address this need, we developed computational tools, based on stochastic modeling, to analyze time series data for the position of randomly migrating cells. Our approach allows parameters that characterize cell movement to be efficiently estimated from cell track data. We applied our methods to analyze the random migration of Mouse Embryonic Fibroblasts (MEFS) and HeLa cells. Our analysis revealed that MEFs exist in two distinct states of migration characterized by differences in cell speed and persistence, whereas HeLa cells only exhibit a single state. Further analysis revealed that the Rho-family GTPase RhoG plays a role in determining the properties of the two migratory states of MEFs. An important feature of our computational approach is that it provides a method for predicting the current migration state of an individual cell from time series data. Finally, we applied our computational methods to HeLa cells expressing a Rac1 biosensor. The Rac1 biosensor is known to perturb movement when expressed at overly high concentrations; at these expression levels the HeLa cells showed two migratory states, which correlated with differences in the spatial distribution of active Rac1.

Cell migration refers to the ability of cells to translocate across a substrate or through a matrix. To achieve net movement requires spatiotemporal regulation of the actin cytoskeleton. Computational approaches are neceary to identify and quantify the regulatory mechanisms that generate directed cell movement. To address this need, we developed computational tools, based on stochastic modeling, to analyze time series data for the position of randomly migrating cells.Our approach allows parameters that characterize cell movement to be efficiently estimated from time series data. We applied our methods to analyze the random migration of Mouse Embryonic Fibroblasts (MEFS). Our analysis revealed that these cells exist in two distinct states of migration characterized by differences in cell speed and persistence. Further analysis revealed that the Rho-family GTPase RhoG plays a role in establishing these two states. An important feature of our computational approach is that it provides a method for predicting the current migration state of an individual cell from time series data. Using this feature, we demonstrate that HeLa cells also exhibit two states of migration, and that these states correlate with differences in the spatial distribution of active Rac1.

In the version of this article originally published, several images were incorrectly set. In Supplementary Fig. 1d, the gels for cofilin and actin were switched. The associated full gels, correct in the original submission, are in Supplementary Fig. 3. In Fig. 4b, the bands for "αTAT core" and for "DN αTAT" were stretched when copied from the original gel to the figure. The original and corrected versions of Fig. 4b are shown below. The errors have been corrected in the HTML and PDF versions of the paper and in the Supplementary Information file.