Dynamic recognition and linkage specificity in K63 di-ubiquitin and TAB2 NZF domain complex

Ubiquitination drives COPI priming and Golgi SNARE localization

Deciphering mechanisms controlling SNARE localization within the Golgi complex is crucial to understanding protein trafficking patterns within the secretory pathway. SNAREs are also thought to prime coatomer protein I (COPI) assembly to ensure incorporation of these essential cargoes into vesicles, but the regulation of these events is poorly understood. Here, we report roles for ubiquitin recognition by COPI in SNARE trafficking and in stabilizing interactions between Arf, COPI, and Golgi SNAREs in Saccharomyces cerevisiae. The ability of COPI to bind ubiquitin, but not the dilysine motif, through its N-terminal WD repeat domain of β'-COP or through an unrelated ubiquitin-binding domain is essential for the proper localization of Golgi SNAREs Bet1 and Gos1. We find that COPI, the ArfGAP Glo3, and multiple Golgi SNAREs are ubiquitinated. Notably, the binding of Arf and COPI to Gos1 is markedly enhanced by ubiquitination of these components. Glo3 is proposed to prime COPI-SNARE interactions; however, Glo3 is not enriched in the ubiquitin-stabilized SNARE-Arf-COPI complex but is instead enriched with COPI complexes that lack SNAREs. These results support a new model for how posttranslational modifications drive COPI priming events crucial for Golgi SNARE localization.

Multiscale Enhanced Sampling Using Machine Learning

Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace generated by a machine learning approach, the variational autoencoder (VAE). The molecular dynamic (MD) trajectories of the ribose-binding protein (RBP) in both the closed and open forms were used as the input by extracting the inter-residue distances as the structural features in order to train the VAE model, allowing the encoded latent layer to characterize the difference in the structural dynamics of the closed and open forms. The interpolated data characterizing the RBP structural change in between the closed and open forms were thus efficiently generated in the low-dimensional latent space of the VAE, which was then decoded into the time-series data of the inter-residue distances and was useful for driving the structural sampling at an atomistic resolution via the MSES scheme. The free energy surfaces on the latent space demonstrated the refinement of the generated data that had a single basin into the simulated data containing two closed and open basins, thus illustrating the usefulness of the MD simulation together with the molecular mechanics force field in recovering the correct structural ensemble.