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Xiaoyong Zou

Publications and source records attributed to Xiaoyong Zou.

3 recordsLinked to original sources

Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network.

Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area.

Algorithms↗

An amperometric cholesterol biosensor based on multiwalled carbon nanotubes and organically modified sol-gel/chitosan hybrid composite film.

A new type of amperometric cholesterol biosensor based on sol-gel chitosan/silica and multiwalled carbon nanotubes (MWCNTs) organic-inorganic hybrid composite material was developed. The hybrid composite film was used to immobilize cholesterol oxidase on the surface of Prussian blue-modified glass carbon electrode. Effects of some experimental variables such as enzyme loading, concentration of Triton X-100, pH, temperature, and applied potential on the current response of the biosensor were investigated. Analytical characteristics and dynamic parameters of the biosensors with and without MWCNTs in the hybrid film were compared, and the results show that analytical performance of the biosensor can be improved greatly after introduction of the MWCNTs. Response time, sensitivity, linear range, limit of detection (S/N=3), and apparent Michaelis-Menten constant Km are 25s, 0.54 microA mM(-1), 8.0 x 10(-6) to 4.5 x 10(-4) M, 4.0 x 10(-6) M, and 0.41 mM for the biosensor without MWCNTs and 13 s, 1.55 microA mM(-1), 4.0 x 10(-6) to 7.0 x 10(-4) M, 1.0 x 10(-6) M, and 0.24 mM for the biosensor with MWCNTs, respectively. The activation energy of the enzyme-catalyzed reaction was measured to be 42.6 kJ mol(-1). This method has been used to determine the free cholesterol concentration in real human blood samples.

Biosensing Techniques↗

Prediction of transmembrane proteins based on the continuous wavelet transform.

A novel method based on continuous wavelet transform (CWT) for predicting the number and location of helices in membrane proteins is presented. Two bacteria proteins are chosen as examples to describe the prediction of transmembrane helices (HTM) by using this method. Selections of an appropriate dilation and hydrophobicity data types are discussed in the text. The results indicate that CWT is a promising approach for the prediction of HTM.

Algorithms↗