Nanopore sequencing is one of only a few methods that can potentially determine the amino acid sequence of individual protein molecules as these are passed through a pore sensor. However, mechanisms for unfolding and translocation of proteins are still unavailable to date. Here we describe a general approach for realizing unidirectional transport of full-length proteins through nanopores. We combine a chemically resistant biological nanopore platform with a high concentration guanidinium chloride buffer to achieve unidirectional, single-file protein transport that is propelled by a giant electro-osmotic effect, as revealed by molecular dynamics simulations and confirmed experimentally. Remarkably, we observed that protein velocities are uniform regardless of the protein sequence, which allows the identification and discrimination among proteins based on their electrical signatures, as well as to distinguish protein signatures by their threading orientation (N-to-C vs. C-to-N terminus). With average transport velocities of 10 µs per amino acid, our method can enable direct, enzyme-free protein fingerprinting and protein sequencing when combined with a high-resolution pore and high-speed nanopore readout.

The current research investigated whether employees' self-construals moderated the effects of supplementary fit and complementary fit on their work-related outcomes (i.e. affective commitment and citizenship behavior). An organisational sample of 317 Chinese employees provided evidence that the relations between supplementary fit and these two work-related outcomes were stronger among employees with a higher interdependent self-construal. Conversely, the relations between complementary fit and work-related outcomes were stronger among employees with a higher independent self-construal. Theoretical and practical implications of these findings are discussed

An echo-state network (ESN) is an effective alternative to gradient methods for training recurrent neural network. However, it is difficult to determine the structure (mainly the reservoir) of the ESN to match with the given application. In this paper, a growing ESN (GESN) is proposed to design the size and topology of the reservoir automatically. First, the GESN makes use of the block matrix theory to add hidden units to the existing reservoir group by group, which leads to a GESN with multiple subreservoirs. Second, every subreservoir weight matrix in the GESN is created with a predefined singular value spectrum, which ensures the echo-sate property of the ESN without posterior scaling of the weights. Third, during the growth of the network, the output weights of the GESN are updated in an incremental way. Moreover, the convergence of the GESN is proved. Finally, the GESN is tested on some artificial and real-world time-series benchmarks. Simulation results show that the proposed GESN has better prediction performance and faster leaning speed than some ESNs with fixed sizes and topologies.

In this paper, a self-organizing cascade neural network (SCNN) with random weights is proposed for nonlinear system modeling. This SCNN is constructed via simultaneous structure and parameter learning processes. In structure learning, the units, which lead to the maximal error reduction of the network, are selected from the candidates and added to the existing network one by one. A stopping criterion based on the training and validation errors is introduced to select the optimal network size to match with a given application. In parameter learning, the weights connected with the output units are incrementally updated without gradients or generalized inverses, while the other weights are randomly assigned and no need to be tuned. Then, the convergence of SCNN is analyzed. Finally, the proposed SCNN is tested on two benchmark nonlinear systems and an actual municipal sewage treatment system. The experiment results show that the proposed SCNN has better performance on nonlinear system modeling than other similar methods.

Protein kinases play central roles in cellular regulation by catalyzing the phosphorylation of target proteins. Kinases have inherent structural flexibility allowing them to switch between active and inactive states. Quantitative characterization of kinase conformational dynamics is challenging. Here, we use nanopore tweezers to assess the conformational dynamics of Abl kinase domain, which is shown to interconvert between two major conformational states where one conformation comprises three sub-states. Analysis of kinase-substrate and kinase-inhibitor interactions uncovers the functional roles of relevant states and enables the elucidation of the mechanism underlying the catalytic deficiency of an inactive Abl mutant G321V. Furthermore, we obtain the energy landscape of Abl kinase by quantifying the population and transition rates of the conformational states. These results extend the view on the dynamic nature of Abl kinase and suggest nanopore tweezers can be used as an efficient tool for other members of the human kinome.

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Nanopore technology offers long, accurate sequencing of an DNA or RNA strand via enzymatic ratcheting of the strand through a nanopore in single nucleotide steps, producing stepwise modulations of the nanopore ion current. In contrast to nucleic acids, their daughter molecules, proteins, have neutral peptide backbones and side chains of varying charges. Further, proteins have stable secondary and higher order structures that obstruct protein linearization required for single file nanopore transport. Here, we describe a general approach for realizing unidirectional transport of proteins through a nanopore that neither requires the protein to be uniformly charged nor a pull from a biological enzyme. At high concentrations of guanidinium chloride, we find fulllength proteins to translocate unidirectionally through an a-hemolysin nanopore in a polymer-based membrane, provided that one of the protein ends is decorated with a short anionic peptide. Molecular dynamics simulations show that such surprisingly steady protein transport is driven by a giant electro-osmotic effect caused by binding of guanidinium cations to the inner surface of the nanopore. We show that ionic current signals produced by protein passage can be used to distinguish two biological proteins and the global orientation of the same protein (N-to-C vs. C-to-N terminus) during the nanopore transport. With the average transport rate of one amino acid per 10 μs, our method may enable direct enzyme-free protein fingerprinting or perhaps even sequencing when combined with a high-speed nanopore reader instrument.

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