The archaeal ATPase PINA interacts with the helicase Hjm via its carboxyl terminal KH domain remodeling and processing replication fork and Holliday junction.

PINA is a novel ATPase and DNA helicase highly conserved in Archaea, the third domain of life. The PINA from Sulfolobus islandicus (SisPINA) forms a hexameric ring in crystal and solution. The protein is able to promote Holliday junction (HJ) migration and physically and functionally interacts with Hjc, the HJ specific endonuclease. Here, we show that SisPINA has direct physical interaction with Hjm (Hel308a), a helicase presumably targeting replication forks. In vitro biochemical analysis revealed that Hjm, Hjc, and SisPINA are able to coordinate HJ migration and cleavage in a concerted way. Deletion of the carboxyl 13 amino acid residues impaired the interaction between SisPINA and Hjm. Crystal structure analysis showed that the carboxyl 70 amino acid residues fold into a type II KH domain which, in other proteins, functions in binding RNA or ssDNA. The KH domain not only mediates the interactions of PINA with Hjm and Hjc but also regulates the hexameric assembly of PINA. Our results collectively suggest that SisPINA, Hjm and Hjc work together to function in replication fork regression, HJ formation and HJ cleavage

ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization

Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs in many domains, the optimization of GNNs is less well studied, and the performance on node classification heavily suffers from the long-tailed node degree distribution. This paper focuses on improving the performance of GNNs via normalization. In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization). The $scale$ operation of ResNorm reshapes the node-wise standard deviation (NStd) distribution so as to improve the accuracy of tail nodes (\textit{i}.\textit{e}., low-degree nodes). We provide a theoretical interpretation and empirical evidence for understanding the mechanism of the above $scale$. In addition to the long-tailed distribution issue, over-smoothing is also a fundamental issue plaguing the community. To this end, we analyze the behavior of the standard shift and prove that the standard shift serves as a preconditioner on the weight matrix, increasing the risk of over-smoothing. With the over-smoothing issue in mind, we design a $shift$ operation for ResNorm that simulates the degree-specific parameter strategy in a low-cost manner. Extensive experiments have validated the effectiveness of ResNorm on several node classification benchmark datasets

Scalable nonparametric multiway data analysis

Abstract Multiway data analysis deals with multiway arrays, i.e., tensors, and the goal is twofold: predicting missing entries by modeling the interactions between array elements and discovering hidden patterns, such as clusters or communities in each mode. Despite the success of existing tensor factorization approaches, they are either unable to capture nonlinear interactions, or computationally expensive to handle massive data. In addition, most of the existing methods lack a principled way to discover latent clusters, which is important for better understanding of the data. To address these issues, we propose a scalable nonparametric tensor decomposition model. It employs Dirichlet process mixture (DPM) prior to model the latent clusters; it uses local Gaussian processes (GPs) to capture nonlinear relationships and to improve scalability. An efficient online variational Bayes Expectation-Maximization algorithm is proposed to learn the model. Experiments on both synthetic and real-world data show that the proposed model is able to discover latent clusters with higher prediction accuracy than competitive methods. Furthermore, the proposed model obtains significantly better predictive performance than the state-of-the-art large scale tensor decomposition algorithm, GigaTensor, on two large datasets with billions of entries

Mechanistic insight into 3-methylmercaptopropionate metabolism and kinetical regulation of demethylation pathway in marine dimethylsulfoniopropionate-catabolizing bacteria

The vast majority of oceanic dimethylsulfoniopropionate (DMSP) is thought to be catabolized by bacteria via the DMSP demethylation pathway. This pathway contains four enzymes termed DmdA, DmdB, DmdC and DmdD/AcuH, which together catabolise DMSP to acetylaldehyde and methanethiol as carbon and sulfur sources, respectively. Whilst molecular mechanisms for DmdA and DmdD have been proposed, little is known of the catalytic mechanisms of DmdB and DmdC, which are central to this pathway. Here we undertake physiological, structural and biochemical analyses to elucidate the catalytic mechanisms of DmdB and DmdC. DmdB, a 3-methylmercaptopropionate (MMPA)-coenzyme A (CoA) ligase, undergoes two sequential conformational changes to catalyze the ligation of MMPA and CoA. DmdC, a MMPA-CoA dehydrogenase, catalyzes the dehydrogenation of MMPA-CoA to generate MTA-CoA with Glu435 as the catalytic base. Sequence alignment suggests that the proposed catalytic mechanisms of DmdB and DmdC are likely widely adopted by bacteria using the DMSP demethylation pathway. Analysis of the substrate affinities of involved enzymes indicates that Roseobacters kinetically regulate the DMSP demethylation pathway to ensure DMSP functioning and catabolism in their cells. Altogether, this study sheds novel lights on the catalytic and regulative mechanisms of bacterial DMSP demethylation, leading to a better understanding of bacterial DMSP catabolism

Sparse Matrix-variate t Process Blockmodels

We consider the problem of modeling binary interactions in networks, such as friendship networks and protein interaction networks, and identifying latent groups in the networks. This problem is challenging due to the facts i) that the data are interdependent instead of independent, ii) that the network data are very noise (e.g., missing edges), and iii) that the network interactions are often sparse. To address these challenges, we propose a Sparse Matrix-variate t process Blockmodel (SMTB). In particular, we generalize a matrix-variate t distribution to a t process on matrices with nonlinear covariance functions. Due to this generalization, our model can estimate latent memberships for individual network nodes. This separates our model from previous t distribution based relational models. Also, we introduce sparse prior distributions on the latent membership parameters, such tat the model selects group assignments for individual nodes. To learn the new model efficiently from data, we develop an efficient variational method. When compared with several state-of-the-art models, including the predictive matrix-variate t models and mixed membership stochastic blockmodels, our model achieved improved prediction accuracy on real world network datasets