The
construction of artificial multienzyme nanodevices with desired
spatial arrangements have shown great promise for improving the overall
performance of targeted enzyme cascades. However, it is a challenge
to rationally design and construct multiple oligomeric enzyme assemblies
that can be used as stable and reusable catalysts. Herein, we report
a novel approach to rapidly achieve ultrastable multimeric enzyme
nanoclusters (MENCs) based on enzymes property of oligomerization
and the SpyTag/SpyCatcher system of orthogonally reactive split peptides.
The SpyCatcher peptide and its binding partner SpyTag were fused to
a dimeric cytochrome P450 monooxygenase mutant (P450BM3m) and a tetrameric
glucose dehydrogenase (GDH), respectively. The fusion proteins self-assembled
into the MENCs, forming a covalently coupled supramolecular multienzyme
nanodevices that facilitated NADPH regeneration and converted indole
into a pigment indigo. We investigated the morphology of the MENCs
and found these multimeric enzymes assembled into two-dimensional
layerlike nanoscale architecture, ranging from a few to several hundred
square microns in size. Importantly, enzymatic analysis revealed that
the MENCs not only increased the initial rate by more than three times
for the indigo synthesis, but also achieved significant improvements
on stability and reusability compared to unassembled enzyme mixtures.
This work demonstrates a versatile and efficient strategy to construct
stable and multifunctional biocatalysts with potential applications
in metabolic engineering and synthetic biology.
Aggregating crowd wisdoms takes multiple labels from various sources and infers true labels for objects. Recent research work makes progress by learning source credibility from data and roughly form three kinds of modeling frameworks: weighted majority voting, trust propagation, and generative models. In this paper, we propose a novel framework named Label-Aware Autoencoders (LAA) to aggregate crowd wisdoms. LAA integrates a classifier and a reconstructor into a unified model to infer labels in an unsupervised manner. Analogizing classical autoencoders, we can regard the classifier as an encoder, the reconstructor as a decoder, and inferred labels as latent features. To the best of our knowledge, it is the first trial to combine label aggregation with autoencoders. We adopt networks to implement the classifier and the reconstructor which have the potential to automatically learn underlying patterns of source credibility. To further improve inference accuracy, we introduce object ambiguity and latent aspects into LAA. Experiments on three real-world datasets show that proposed models achieve impressive inference accuracy improvement over state-of-the-art models.
In this paper, we notice that sparse and low-rank structures arise in the context of many collaborative filtering applications where the underlying graphs have block-diagonal adjacency matrices. Therefore, we propose a novel Sparse and Low-Rank Linear Method (LorSLIM) to capture such structures and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a sparse and low-rank aggregation coefficient matrix W is learned from LorSLIM by solving an 1-norm and nuclear norm regularized optimization problem. We also develop an efficient alternating augmented Lagrangian method(ADMM) to solve the optimization problem. A comprehensive set of experiments is conducted to evaluate the performance of LorSLIM. The experimental results demonstrate the superior recommendation quality of the proposed algorithm in comparison with current state-of-the-art methods.
Sparse regression techniques have been popular in recent years because of their ability in handling high dimensional data with built-in variable selection.The lasso is perhaps one of the most well-known examples. Despite intensive work in this direction, how to provide valid inference for sparse regularized methods remains a challenging statistical problem. We take a unique point of view of this problem and propose to make use of stochastic variational inequality techniques in optimization to derive confidence intervals and regions for the lasso. Some theoretical properties of the procedure are obtained. Both simulated and real data examples are used to demonstrate the performance of the method.
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