Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine.

Protein secondary structure carries information about local structural arrangements. Significant majority of successful methods for predicting the secondary structure is based on multiple sequence alignment. However, the multiple alignment fails to achieve accurate results when a protein sequence is characterized by low homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation. The method is featured by employing a support vector machine (SVM) regressing system and adopting a different pseudo amino acid composition (PseAAC), which can partially take into account the sequence-order effects to represent protein samples. It was shown by both the self-consistency test and the independent-dataset test that the trained SVM has remarkable power in grasping the relationship between the PseAAC and the content of protein secondary structural elements, including alpha-helix, 3(10)-helix, pi-helix, beta-strand, beta-bridge, turn, bend and the rest random coil. Results prior to or competitive with the popular methods have been obtained, which indicate that the present method may at least serve as an alternative to the existing predictors in this area.

Predicting protein structural class based on multi-features fusion.

Structural class characterizes the overall folding type of a protein or its domain and the prediction of protein structural class has become both an important and a challenging topic in protein science. Moreover, the prediction itself can stimulate the development of novel predictors that may be straightforwardly applied to many other relational areas. In this paper, 10 frequently used sequence-derived structural and physicochemical features, which can be easily computed by the PROFEAT (Protein Features) web server, were taken as inputs of support vector machines to develop statistical learning models for predicting the protein structural class. More importantly, a strategy of merging different features, called best-first search, was developed. It was shown through the rigorous jackknife cross-validation test that the success rates by our method were significantly improved. We anticipate that the present method may also have important impacts on boosting the predictive accuracies for a series of other protein attributes, such as subcellular localization, membrane types, enzyme family and subfamily classes, among many others.

Glucose biosensors based on platinum nanoparticles-deposited carbon nanotubes in sol-gel chitosan/silica hybrid.

A new strategy for fabricating a sensitivity-enhanced glucose biosensor was presented, based on multi-walled carbon nanotubes (CNT), Pt nanoparticles (PtNP) and sol-gel of chitosan (CS)/silica organic-inorganic hybrid composite. PtNP-CS solution was synthesized through the reduction of PtCl(6)(2-) by NaBH(4) at room temperature. Benefited from the amino groups of CS, a stable PtNP gel was obtained, and a CNT-PtNP-CS solution was prepared by dispersing CNT functionalized with carboxylic groups in PtNP-CS solution. The CS/silica hybrid sol-gel was produced by mixing methyltrimethoxysilane (MTOS) with the CNT-PtNP-CS solution. Then, with the immobilization of glucose oxidase (GOD) into the sol-gel, the glucose biosensor of GOD-CNT-PtNP-CS-MTOS-GCE was fabricated. The properties of resulting glucose biosensor were measured by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). In phosphate buffer solutions (PBS, pH 6.8), nearly interference free determination of glucose was realized at low applied potential of 0.1V, with a wide linear range of 1.2x10(-6) to 6.0x10(-3)M, low detection limit of 3.0x10(-7)M, high sensitivity of 2.08microA mM(-1), and a fast response time (within 5s). The results showed that the biosensor provided the high synergistic electrocatalytic action, and exhibited good reproducibility, long-term stability. Subsequently, the novel biosensor was applied for the determination of glucose in human serum sample, and good recovery was obtained (in the range of 95-104%).

A novel glucose biosensor based on immobilization of glucose oxidase in chitosan on a glassy carbon electrode modified with gold-platinum alloy nanoparticles/multiwall carbon nanotubes.

A novel glucose biosensor was constructed, based on the immobilization of glucose oxidase (GOx) with cross-linking in the matrix of biopolymer chitosan (CS) on a glassy carbon electrode (GCE), which was modified with gold-platinum alloy nanoparticles (Au-PtNPs) by electrodeposition on multiwall carbon nanotubes (CNTs) in CS film (CNTs/CS). The properties of Au-PtNPs/CNTs/CS were characterized by scan electron microscopy (SEM), X-ray diffraction (XRD), cyclic voltammetry (CV), and electrochemical impedance spectra (EIS). Primary study indicated that Au-PtNPs/CNTs had a better synergistic electrocatalytic effect on the reduction of hydrogen peroxide than did AuNPs/CNTs or PtNPs/CNTs at a low applied potential window. With GOx as a model enzyme, a new glucose biosensor was fabricated. The biosensor exhibited excellent performances for glucose at a low applied potential (0.1V) with a high sensitivity (8.53 microA mM(-1)), a low detection limit (0.2 microM), a wide linear range (0.001-7.0 mM), a fast response time (<5s), and good reproducibility, stability, and selectivity. In addition, the biosensor was applied in the determination of glucose in human blood and urine samples, and satisfied results were obtained.

Electrochemical biosensor based on multi-walled carbon nanotubes and Au nanoparticles synthesized in chitosan.

A new biosensor is prepared by cross-linking glucose oxidase (GOD) with glutaradehyde at the electrode combining Au nanoparticles (AuNP) with multi-walled carbon nanotubes (MWCNTs). Au nanoparticles-doped chitosan (CS) solution (AuNP-CS) is prepared by treating the CS solution followed by chemical reduction of Au (III) with NaBH4. MWCNTs are then dispersed in AuNP-CS solution. TEM, FT-IR, and UV-Vis show that the AuNP-CS solution is highly dispersed and stable. The synergistic effect between AuNP and CNTs of the AuNP-CNTs-CS material has been investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and amperometric methods. The modified glassy carbon electrode (GCE) allows low-potential detection of H2O2 with high sensitivity and fast response time. With the immobilization of GOD, a biosensor has been constructed. In phosphate buffer solutions (PBS, pH 7.0), nearly free interference determination of glucose has been realized at 0.4 V(vs. Ag/AgCl/3.0 M KCI) with a wide linear range from 2.0 x 10(-5) to 1.5 x 10(-2) M and a fast response time within 5s. The biosensor has been used to determine glucose in human serum samples and the results are satisfactory.

A sensitive nonenzymatic glucose sensor in alkaline media with a copper nanocluster/multiwall carbon nanotube-modified glassy carbon electrode.

Copper (Cu) nanoclusters were electrochemically deposited on the film of a Nafion-solubilized multiwall carbon nanotube (CNTs)-modified glassy carbon electrode (CNTs-GCE), which fabricated a Cu-CNTs composite sensor (Cu-CNTs-GCE) to detect glucose with nonenzyme. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) were used for the characterization of the distribution of the Cu nanoclusters on the CNTs matrix. The composite of the Cu-CNTs was investigated by the electrochemical characterization of cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The preliminary study shows that the nonenzymatic sensor has synergistic electrocatalytic activity to the oxidation of glucose in alkaline media. A well applicable sensor was constructed to use for the analysis of the glucose in real blood serum samples due to the large number of electrons taking part in the oxidation process, the high apparent kinetic rate constant, and the stable operation of the electrode. The linear range for the detection of the glucose is 7.0 x 10(-7) to 3.5 x 10(-3) M with a high sensitivity of 17.76 microA mM(-1), a low detection limit of 2.1 x 10(-7) M, and a fast response time of within 5s. Experiment results also showed that the sensor has good reproducibility and long-term stability and is interference free.

Prediction of protein secondary structure content using support vector machine.

In this paper, the support vector machine was trained to grasp the relationship between the pair-coupled amino acid composition and the content of protein secondary structural elements, including alpha-helix, 3(10)-helix, pi-helix, beta-strand, beta-bridge, turn, bend and the rest random coil. Self-consistency and cross validation tests were made to assess the performance of our method. Results superior to or competitive with the popular theoretical and experimental methods have been obtained.

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.

Using pseudo-amino acid composition and support vector machine to predict protein structural class.

As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.

Simultaneous photocatalytic reduction of Cr(VI) and oxidation of phenol over monoclinic BiVO4 under visible light irradiation.

BiVO4 powder with monoclinic structure was prepared and used as a visible-light catalyst simultaneously for the photooxidation of phenol and the photoreduction of Cr(VI). The photocatalytic efficiency was found to be rather low for either single phenol solution or single Cr(VI) solution. However, the photocatalytic reduction of Cr(VI) and photocatalytic oxidation of phenol proceed more rapidly for the coexistence system of phenol and Cr(VI) than for the single process, showing synergetic effect between the oxidation and reduction reactions. For the simultaneous photocatalytic reduction-oxidation process, the first-order kinetic constant of phenol degradation was 0.0314 min-1, being about six times higher than that for the photocatalytic process of single phenol. This result reveals the feasibility of using Cr(VI) as the electron scavenger of mBiVO4-mediated photocatalytic process of phenol degradation, and gives us an enlightenment to employ other semiconductor with a better visible light response but with a more positive band edge to efficiently degrade organic pollutants. This is the first report for simultaneous photocatalytic reduction of Cr(VI) and removal of phenol under visible light irradiation using photocatalyst mBiVO4.

[Determination of cadmium, chromium and lead in Lycoris radiata with microwave digestion technique by graphite furnace atomic absorption spectrometry].

A new method using microwave digestion technique was developed for the determination of cadmium, chromium and lead in Lycoris radiata by graphite furnace atomic absorption spectrometry (GFAAS). Digestion solvents were discussed for sample preparation by microwave digestion technique. The optimum condition of determination by GFAAS was studied in the presence of NH4H2PO4 matrix modifier. The detection limits of Cd, Cr and Pb are 0.066 7, 1.22 and 0.810 microg x L(-1), respectively. The method was applied to the determination of Cd, Cr and Pb in Lycoris radiata samples with satisfactory results. The RSD of determination were lower than 3.1%. The recoveries were from 83.8% to 118%.

Glucose biosensor based on glucose oxidase immobilized in sol-gel chitosan/silica hybrid composite film on Prussian blue modified glass carbon electrode.

An improved amperometric glucose biosensor based on glucose oxidase immobilized in sol-gel chitosan/silica hybrid composite film, which was prepared from chitosan (CS) and methyltrimethoxysilane (MTOS), on the surface of Prussian blue (PB)-modified glass carbon electrode was developed. The film was characterized by FT-IR. Effects of some experimental variables such as ratio of CS to silica, buffer pH, temperature, and applied potential on the current response of the biosensor were investigated. The biosensor fabricated under optimal conditions had a linear response to glucose over the range 5.0 x 10(-5) to 2.6 x 10(-2) M with a correlation coefficient of 0.9948 and a detection limit of 8.0 x 10(-6) M based on S/N = 3. The biosensor had a fast response time of less than 10 s, a high sensitivity of 420 nA mM(-1), a long-term stability of over 60 days, and a good selectivity. The apparent Michaelis-Menten constant K(m) was found to be 3.2 x 10(-3) M. The activation energy for enzymatic reaction was calculated to be 21.9 kJ mol(-1). This method has been used to determine the glucose concentration in real human blood samples.

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.