Xiaoyong Zou

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%).

Prediction of protein structural classes by Chou’s pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou’s pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246–255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

Construction of a Zinc Porphyrin-Fullerene-Derivative Based Nonenzymatic Electrochemical Sensor for Sensitive Sensing of Hydrogen Peroxide and Nitrite

Enzymatic sensors possess high selectivity but suffer from some limitations such as instability, complicated modified procedure, and critical environmental factors, which stimulate the development of more sensitive and stable nonenzymatic electrochemical sensors. Herein, a novel nonenzymatic electrochemical sensor is proposed based on a new zinc porphyrin-fullerene (C60) derivative (ZnP-C60), which was designed and synthesized according to the conformational calculations and the electronic structures of two typical ZnP-C60 derivatives of para-ZnP-C60 (ZnP(p)-C60) and ortho-ZnP-C60 (ZnP(o)-C60). The two derivatives were first investigated by density functional theory (DFT) and ZnP(p)-C60 with a bent conformation was verified to possess a smaller energy gap and better electron-transport ability. Then ZnP(p)-C60 was entrapped in tetraoctylammonium bromide (TOAB) film and modified on glassy carbon electrode (TOAB/ZnP(p)-C60/GCE). The TOAB/ZnP(p)-C60/GCE showed four well-defined quasi-reversible redox couples with extremely fast direct electron transfer and excellent nonenzymatic sensing ability. The electrocatalytic reduction of H2O2 showed a wide linear range from 0.035 to 3.40 mM, with a high sensitivity of 215.6 μA mM(-1) and a limit of detection (LOD) as low as 0.81 μM. The electrocatalytic oxidation of nitrite showed a linear range from 2.0 μM to 0.164 mM, with a sensitivity of 249.9 μA mM(-1) and a LOD down to 1.44 μM. Moreover, the TOAB/ZnP(p)-C60/GCE showed excellent stability and reproducibility, and good testing recoveries for analysis of the nitrite levels of river water and rainwater. The ZnP(p)-C60 can be used as a novel material for the fabrication of nonenzymatic electrochemical sensors.

Simultaneous detection of guanine, adenine, thymine and cytosine at choline monolayer supported multiwalled carbon nanotubes film

A rapid, convenient and accurate method for the simultaneous detection of guanine (G), adenine (A), thymine (T) and cytosine (C) was developed at a multiwalled carbon nanotube (MWCNT)/choline (Ch) monolayer-modified glassy carbon electrode (GCE). X-ray photoelectron spectroscopy data demonstrated that Ch was covalently immobilised on the surface of GCE through oxygen atom. The Ch monolayer provides a positively charged surface with -N(+)(CH(3))(3) polar groups, so that it can attract negatively charged MWCNTs to the surface. Consequently, the MWCNT/Ch film exhibited remarkable electrocatalytic activities towards the oxidation of G, A, T and C due to the advantages of high electrode activity, large surface area, prominent antifouling property, and high electron transfer kinetics. All purine and pyrimidine bases showed well-defined catalytic oxidation peaks at MWCNT/Ch/GCE. The peak separations between G and A, A and T, and T and C are 270, 200, and 190 mV, respectively, which are sufficiently large for their potential recognition and simultaneous detection in mixture. Under the optimum conditions, the designed MWCNT/Ch/GCE exhibited low detection limit, high sensitivity and wide linear range for simultaneous detection of G, A, T and C. Moreover, the proposed method was successfully applied to the assessment of G, A, T and C contents in a herring sperm DNA sample with satisfactory results.

A label-free and PCR-free electrochemical assay for multiplexed microRNA profiles by ligase chain reaction coupling with quantum dots barcodes

The profiling of microRNAs (miRNAs) is greatly significant for cellular events or disease diagnosis. Electrochemical methods for miRNAs analysis mostly can only measure one kind of miRNA, which is unambiguous to indicate the disease type and state. Here a label-free and PCR-free electrochemical method is presented for multiplexed evaluation of miRNAs in a single-tube experiment. The method is based on the combination of the high base-mismatch selectivity of ligase chain reaction (LCR) and the remarkable voltammetric signature of electrochemical QDs barcodes. Two reporting probes of RP1 and RP2 were labeled with PbS and CdS quantum dots (QDs) to prepare PbS-RP1 and CdS-RP2 conjugates, and two capture probes of CP1 and CP2 were co-immobilized on magnetic beads (MBs) to fabricate MB-CP1CP2 conjugate. The miRNAs samples were simply incubated with MB-CP1CP2, PbS-RP1, and CdS-RP2 conjugates, and then added with T4 DNA ligase. After release of the disjoined QDs barcodes from the MB-conjugates, two target miRNAs of miR-155 and miR-27b were simultaneously detected by square wave voltammetry with linear ranges of 50 fM-30 pM and 50 fM-1050 pM, and limits of detection (LODs) of 12 fM and 31 fM (S/N=3). The method fulfilled the assay in less than 70 min, and showed acceptable testing recoveries for the determination of miRNAs in biological matrix. Currently there are rare reports about electrochemical multiplexed quantification of miRNAs. The method is likely to provide a new platform for identification of multiple miRNAs in a simple way.

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 structure class by coupling improved genetic algorithm and support vector machine

Structural class characterizes the overall folding type of a protein or its domain. Most of the existing methods for determining the structural class of a protein are based on a group of features that only possesses a kind of discriminative information for the prediction of protein structure class. However, different types of discriminative information associated with primary sequence have been completely missed, which undoubtedly has reduced the success rate of prediction. We present a novel method for the prediction of protein structure class by coupling the improved genetic algorithm (GA) with the support vector machine (SVM). This improved GA was applied to the selection of an optimized feature subset and the optimization of SVM parameters. Jackknife tests on the working datasets indicated that the prediction accuracies for the different classes were in the range of 97.8–100% with an overall accuracy of 99.5%. The results indicate that the approach has a high potential to become a useful tool in bioinformatics.

A label-free electrochemical assay for quantification of gene-specific methylation in a nucleic acid sequence

A label-free electrochemical method is developed for simple and convenient quantification of gene-specific DNA hypermethylation in a DNA sequence without PCR amplification, bisulfite conversion or labeling processes.

Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm

BackgroundBecause a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their function is a quite meaningful topic in protein science. However, with the rapid increase of GPCRs sequences entering into databanks, the gap between the number of known sequence and the number of known function is widening rapidly, and it is both time-consuming and expensive to determine their function based only on experimental techniques. Therefore, it is vitally significant to develop a computational method for quick and accurate classification of GPCRs.ResultsIn this study, a novel three-layer predictor based on support vector machine (SVM) and feature selection is developed for predicting and classifying GPCRs directly from amino acid sequence data. The maximum relevance minimum redundancy (mRMR) is applied to pre-evaluate features with discriminative information while genetic algorithm (GA) is utilized to find the optimized feature subsets. SVM is used for the construction of classification models. The overall accuracy with three-layer predictor at levels of superfamily, family and subfamily are obtained by cross-validation test on two non-redundant dataset. The results are about 0.5% to 16% higher than those of GPCR-CA and GPCRPred.ConclusionThe results with high success rates indicate that the proposed predictor is a useful automated tool in predicting GPCRs. GPCR-SVMFS, a corresponding executable program for GPCRs prediction and classification, can be acquired freely on request from the authors.