Peixiang Cai

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.

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%).

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.

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.

Spline wavelet self-convolution in processing overlapped peaks in capillary electrophoresis

The normal distribution function was used as the resolving factor to act on the filters of the spline wavelet to produce the peak resoluter of spline wavelet self-convolution (SWSC). Several types of simulated overlapped peaks were processed by this peak resoluter and satisfactory results were obtained. Baseline separation can be achieved and overlapped peaks can be separated directly in the time domain. The relative errors of the peak positions and areas between the original peaks and the processed peaks are less than 5.0%. The overlapped peaks of capillary electrophoresis signals for amino acid mixed solutions and inorganic ion solutions were resolved by this method and satisfactory results were also obtained.

Application of spline wavelet self-convolution in processing capillary electrophoresis overlapped peaks with noise

A new method of spline wavelet self-convolution (SWSC), which is used to resolve the overlapped peaks with noise, is presented in this paper. The resolution of several kinds of overlapped peaks with noise simulated by computer has been discussed in details. It is made known that the overlapped peaks with noise can be separated (S/N>50) directly and the noise is removed at the same time in time domain. Base-line can be separated and the relative errors of peak area and position are <5.0%. The satisfactory results are also obtained by resolving the capillary electrophoresis (CE) overlapped signals with noise.

Wavelet fractal character of overlapping signal

A new method based on the combining of the wavelet theory with the fractal theory and named wavelet fractal peak position method (WFPPM) is introduced to extract the number of the components and the relevant peak positions from overlapping signals in chemistry. The overlapping signal is first transformed into continuous wavelet transform value of time domain in certain dilation range via continuous wavelet transform (CWT), and then changed into capacity dimensions (Dc). The number of the components and the relevant positions of overlapping peaks can be identified easily according to the change ofDc. An investigation concerning the influence of different dilation ranges on the peak positions extracted by WFPPM is also provided. Studies show that the WFPPM is an efficient tool for extracting the peak positions and identifying the number of peaks from unresolved signals, even when this kind of overlapping is significantly serious. Relative errors of less than 1.0% in peak positions are found when WFPPM is used in the processing of the cadmium(II)-indium(III) mixture system. The analytical results demonstrate that the desired peak positions can be extracted conveniently, accurately and rapidly from an unresolved signal via WFPPM. Tremendous developing and applications based on currently reported WFPPM in extracting overlapping signals would be expected in the near future.