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ABSTRACT: We report the successful high-yield expression of Candida utilis uricase in Escherichia coli and the establishment of an efficient three-step protein purification protocol. The purity of the recombinant protein, which was confirmed to be C. utilis uricase by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometer analysis, was >98% and the specific activity was 38.4 IU/mg. Crystals of C. utilis uricase were grown at 18°C using 25% polyethylene glycol 3350 as precipitant. Diffraction by the crystals extends to 1.93 Å resolution, and the crystals belong to the space group P2(1)2(1)2(1) with unit cell parameters a = 69.16 Å, b = 139.31 Å, c = 256.33 Å, and α = β = γ = 90°. The crystal structure of C. utilis uricase shares a high similarity with other reported structures of the homologous uricases from other species in protein database, demonstrating that the three-dimensional structure of the protein defines critically to the catalytic activities.
Applied Microbiology and Biotechnology 05/2011; 92(3):529-37. · 3.42 Impact Factor
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ABSTRACT: The adhesive domain of SdrE from Staphylococcus aureus was recombinantly expressed in Escherichia coli. The purified protein was identified by SDS-PAGE and MALDI-TOF MS. The protein was crystallized using the vapour-diffusion method in hanging-drop mode with PEG 8000 as the primary precipitating agent. X-ray diffraction data were collected to 1.8 A resolution from a single crystal of the protein. Preliminary X-ray analysis indicated that the crystal belonged to space group P1, with unit-cell parameters a = 40.714, b = 66.355, c = 80.827 A, alpha = 111.19, beta = 93.99, gamma = 104.39 degrees.
Acta Crystallographica Section F Structural Biology and Crystallization Communications 07/2010; 66(Pt 7):858-61. · 0.51 Impact Factor
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ABSTRACT: The adhesive domain of SdrD from Staphylococcus aureus was solubly expressed in Escherichia coli in high yield. After a series of purification steps, the purified protein was >95% pure, which was SdrD from S. aureus identified by SDS-PAGE and MALDI-TOF MS. Crystals were grown at 18 degrees C using 25% polyethylene glycol 3350 as precipitant. Diffraction by the crystal extends to 1.65A resolution, and the crystal belongs to the space group C2, with the unit cell parameters a=133.3, b=58.3, c=112.3A, alpha=90.00, beta=111.14, gamma=90.00.
Protein Expression and Purification 09/2009; 69(2):204-8. · 1.59 Impact Factor
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ABSTRACT: This paper proposes a novel grouping decision approach for blind source estimation of FIR (finite impulse response) channels with binary sources. First, solvability is discussed for single-input systems and multi-input systems. Necessary and sufficient conditions for recoverability are derived. For single-input systems, a new deterministic algorithm based on grouping and decision is proposed to recover the source up to a delay. The algorithm is easy to implement and has several advantages. For instance, when the solvability conditions are satisfied, it can be applied to cases in which: (i) the channel has zeros on the unit circle or outside of the unit circle; (ii) there are fewer sensors than sources; (iii) the source is temporarily dependent. To improve noise tolerance and reduce computational cost, the algorithm is further elaborated for highly noisy channels and high-order FIR channels, respectively. For the channels with high unimodal noise, fewer peaks appear in the probability density function (pdf) of the outputs compared to the pdf of the outputs of channels with a higher SNR. After the peaks representing cluster centers are estimated using a maximum likelihood (ML) approach, the deterministic algorithm can be used. Similar to highly noisy channels, the algorithm is also effective for high-order, exponentially decaying channels after fewer cluster centers are estimated. Furthermore, blind source estimation for multi-input systems also can be carried out as with the case of single input systems. Two deflation algorithms are presented for temporarily dependent sources and i.i.d. sources. Based on the source estimation and deflation algorithms, the sources can be obtained one by one. Finally, the validity and performance of the algorithms are illustrated by several simulation examples.
Signal Processing. 01/2004;
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ABSTRACT: In this paper, a tensor-based scheme is introduced for single trial electroencephalogram (EEG) classification in brain computer interfacing (BCI). Firstly, EEG signals are represented as third order tensors in the spatial–spectral–temporal domain by wavelet transform. Then, a regularized tensor discriminant analysis (RTDA) algorithm is proposed for a multi-way discriminative subspace extraction from tensor-represented EEG data. Unlike the conventional wavelet transform method, the proposed scheme includes the structural information in multi-channel time-varying EEG spectrums endorsed by tensor representation, and improves the performance for EEG classification. Compared with the common spatial pattern (CSP, the most successful algorithm in BCI) in the applications to two classes of datasets, the proposed scheme has the following advantages: (1) an optimal multi-way discriminative subspace can be extracted, obtaining significant spatial–spectral–temporal patterns for EEG classification; (2) the proposed scheme can identify discriminative characteristics robustly, and works well without prior neurophysiologic knowledge. This is a valuable property for developing new paradigms in BCI whose discriminative neural correlates are not known and (3) the proposed scheme is able to find the most significant channels for classification, and can be applied to channel selection in BCI. Computer simulations show that the number of used channels can be reduced to 2 in two datasets with very little loss in performance. Therefore, it has great potential for the practical application of BCI.
Pattern Recognition Letters.
