Assembly of a miRNA-modified QCM sensor for miRNA recognition through response patterns.

In this paper, a miRNA-based quartz crystal microbalance (QCM) biosensor was fabricated and used to the rapid and effective sensing of miRNA. The specific hybridization between probe miRNA and different selected miRNAs (miR-27a, miR-27b, and Let-7a) cause a different interaction mode, thus display different frequency change and response patterns in the QCM sensor, which were used to detect miR-27a and miR-27b. The selective sensing of miR-27a in mixed miRNA solution was also achieved. This miRNA-based QCM biosensor has the advantages of real-time, label-free, and short cycle detection.

Identification of drug-target interaction from interactome network with 'guilt-by-association' principle and topology features.

MOTIVATION: Identifying drug-target protein interaction is a crucial step in the process of drug research and development. Wet-lab experiment are laborious, time-consuming and expensive. Hence, there is a strong demand for the development of a novel theoretical method to identify potential interaction between drug and target protein. RESULTS: We use all known proteins and drugs to construct a nodes- and edges-weighted biological relevant interactome network. On the basis of the 'guilt-by-association' principle, novel network topology features are proposed to characterize interaction pairs and random forest algorithm is employed to identify potential drug-protein interaction. Accuracy of 92.53% derived from the 10-fold cross-validation is about 10% higher than that of the existing method. We identify 2272 potential drug-target interactions, some of which are associated with diseases, such as Torg-Winchester syndrome and rhabdomyosarcoma. The proposed method can not only accurately predict the interaction between drug molecule and target protein, but also help disease treatment and drug discovery. CONTACTS: zhanchao8052@gmail.com or ceszxy@mail.sysu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying and prioritizing disease-related genes based on the network topological features.

Identifying and prioritizing disease-related genes are the most important steps for understanding the pathogenesis and discovering the therapeutic targets. The experimental examination of these genes is very expensive and laborious, and usually has a higher false positive rate. Therefore, it is highly desirable to develop computational methods for the identification and prioritization of disease-related genes. In this study, we develop a powerful method to identify and prioritize candidate disease genes. The novel network topological features with local and global information are proposed and adopted to characterize genes. The performance of these novel features is verified based on the 10-fold cross-validation test and leave-one-out cross-validation test. The proposed features are compared with the published features, and fused strategy is investigated by combining the current features with the published features. And, these combination features are also utilized to identify and prioritize Parkinson's disease-related genes. The results indicate that identified genes are highly related to some molecular process and biological function, which provides new clues for researching pathogenesis of Parkinson's disease. The source code of Matlab is freely available on request from the authors.

A new QCM signal enhancement strategy based on streptavidin@metal-organic framework complex for miRNA detection.

Sensitive and selective detection of miRNA is of great significance for the early diagnosis of human diseases, especially for cancers. Quartz crystal microbalance (QCM) is an effective tool for detecting biological molecules; however, the application of QCM for miRNA detection is still very limited. One of the great needs for QCM detection is to further improve the QCM signal. Herein, for the first time, we promote a new signal enhancement strategy for the detection of miRNA by QCM. First, a hairpin biotin-modified DNA was used as a probe DNA, which exposes the biotin site when interacting with target miRNA. Then, a streptavidin@metal-organic framework (SA@MOF) complex formed by electrostatic attractions between SA and a MOF was introduced into the QCM detection system. The SA@MOF complexes serve as both a signal amplifier and a specific recognition element via specific biotin-SA interactions. The strategy was applied to the detection of a colorectal cancer marker, miR-221, by using a stable Zr(IV)-MOF, UiO-66-NH2. The detection linear range was 10 fM-1 nM, the detection limit was 6.9 fM, and the relative standard deviation (RSD) (n = 5) was lower than 10% in both simulated conditions and the real serum environment. Furthermore, the detection limit reached 0.79 aM when coupled with the isothermal exponential amplification reaction (EXPAR).

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.

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.

Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network.

Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target.

Identifying functions of protein complexes based on topology similarity with random forest.

Elucidating the functions of protein complexes is critical for understanding disease mechanisms, diagnosis and therapy. In this study, based on the concept that protein complexes with similar topology may have similar functions, we firstly model protein complexes as weighted graphs with nodes representing the proteins and edges indicating interaction between proteins. Secondly, we use topology features derived from the graphs to characterize protein complexes based on the graph theory. Finally, we construct a predictor by using random forest and topology features to identify the functions of protein complexes. Effectiveness of the current method is evaluated by identifying the functions of mammalian protein complexes. And then the predictor is also utilized to identify the functions of protein complexes retrieved from human protein-protein interaction networks. We identify some protein complexes with significant roles in the occurrence of tumors, vesicles and retinoblastoma. It is anticipated that the current research has an important impact on pathogenesis and the pharmaceutical industry. The source code of Matlab and the dataset are freely available on request from the authors.

Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm.

In the post-genome era, one of the most important and challenging tasks is to identify the subcellular localizations of protein complexes, and further elucidate their functions in human health with applications to understand disease mechanisms, diagnosis and therapy. Although various experimental approaches have been developed and employed to identify the subcellular localizations of protein complexes, the laboratory technologies fall far behind the rapid accumulation of protein complexes. Therefore, it is highly desirable to develop a computational method to rapidly and reliably identify the subcellular localizations of protein complexes. In this study, a novel method is proposed for predicting subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm. Protein complexes are modeled as weighted graphs containing nodes and edges, where nodes represent proteins, edges represent protein-protein interactions and weights are descriptors of protein primary structures. Some topological structure features are proposed and adopted to characterize protein complexes based on graph theory. Random forest is employed to construct a model and predict subcellular localizations of protein complexes. Accuracies on a training set by a 10-fold cross-validation test for predicting plasma membrane/membrane attached, cytoplasm and nucleus are 84.78%, 71.30%, and 82.00%, respectively. And accuracies for the independent test set are 81.31%, 69.95% and 81.00%, respectively. These high prediction accuracies exhibit the state-of-the-art performance of the current method. It is anticipated that the proposed method may become a useful high-throughput tool and plays a complementary role to the existing experimental techniques in identifying subcellular localizations of mammalian protein complexes. The source code of Matlab and the dataset can be obtained freely on request from the authors.

Dual-layer wavelet SVM for predicting protein structural class via the general form of Chou's pseudo amino acid composition.

A prior knowledge of protein structural class can provide useful information about its overall structure. So, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a dual-layer wavelet support vector machine (WSVM) is presented via the general form of Chou's pseudo amino acid composition, which is featured by introducing wavelet as a kernel and making decisions by the fusion from three individual classifiers. As a demonstration, the rigorous jackknife cross-validation tests were performed on two benchmark datasets, including the more challenging 25PDB dataset. Our success rates were reliable, and it has not escaped from our notice that the present method has specific ability to predict the most difficult case of α+ß class. The program developed can be acquired freely on request from the authors.

Identification of potential host proteins for influenza A virus based on topological and biological characteristics by proteome-wide network approach.

A proteome-wide network approach was performed to characterize significant patterns of influenza A virus (IAV)-human interactions, and to further identify potentially valuable targets for prophylactic and therapeutic interventions. Topological analysis demonstrated a strong tendency for IAV to interplay with highly connected and central proteins located in sparsely connected sub-networks. Additionally, functional analysis based on biological process revealed a number of functional groups overrepresented for IAV interactions, in which regulation of cell death and apoptosis, and phosphorus metabolic process is the most highly enriched. In order to investigate whether these topological and biological features are significant enough to distinguish IAV targets from human proteome, a discrimination model was constructed based on these features using support vector machine coupled with genetic algorithm. The average result of overall prediction accuracy is 71.04% by leave-one-out across validation test. The optimized classifier was then applied to 9706 human proteins. As a result, 1418 novel genes were identified from human interactome, some of which were experimentally validated by others' works to be important for IAV infection. The findings presented in this study might be important in discovering new drug targets for therapeutic treatments as well as revealing topological features and functional properties specific for viral infection.

Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features.

In the post-genomic era, one of the most important and challenging tasks is to identify protein complexes and further elucidate its molecular mechanisms in specific biological processes. Previous computational approaches usually identify protein complexes from protein interaction network based on dense sub-graphs and incomplete priori information. Additionally, the computational approaches have little concern about the biological properties of proteins and there is no a common evaluation metric to evaluate the performance. So, it is necessary to construct novel method for identifying protein complexes and elucidating the function of protein complexes. In this study, a novel approach is proposed to identify protein complexes using random forest and topological structure. Each protein complex is represented by a graph of interactions, where descriptor of the protein primary structure is used to characterize biological properties of protein and vertex is weighted by the descriptor. The topological structure features are developed and used to characterize protein complexes. Random forest algorithm is utilized to build prediction model and identify protein complexes from local sub-graphs instead of dense sub-graphs. As a demonstration, the proposed approach is applied to protein interaction data in human, and the satisfied results are obtained with accuracy of 80.24%, sensitivity of 81.94%, specificity of 80.07%, and Matthew's correlation coefficient of 0.4087 in 10-fold cross-validation test. Some new protein complexes are identified, and analysis based on Gene Ontology shows that the complexes are likely to be true complexes and play important roles in the pathogenesis of some diseases. PCI-RFTS, a corresponding executable program for protein complexes identification, can be acquired freely on request from the authors.

Investigation of dual-layer membrane cloaking method by surface plasmon resonance for direct chronoamperometric immunoassay of serum sample.

A "dual-layer membrane cloaking" (DLMC) method was developed to construct disposable electrochemical immunosensor for direct determination of serum sample. Mouse IgG (MIgG) molecules were firstly immobilized on a substrate. After the formation of a didodecyldimethylammonium bromide (DDAB) membrane on the MIgG modified substrate, an additional bovine serum albumin (BSA) thin layer was formed to build a BSA/DDAB dual-layer membrane (DLM). When alkaline phosphatase conjugated anti-mouse IgG antibodies (anti-MIgG-ALP) in human serum were incubated on the substrate, anti-MIgG-ALP was recognized specifically by the immobilized MIgG while all nonspecifically adsorbed proteins were selectively removed together with BSA/DDAB DLM by 5% Triton X-100 (v/v) before final measurements. The BSA/DDAB DLM was characterized and optimized by surface plasmon resonance (SPR) technique, and further employed in a disposable immunoassay based on an ITO chip. Under optimal conditions, MIgG in human serum was directly detected in the range of 2.0-18.0 ng mL(-1) without dilution or separation. A limit of detection as low as 0.922 ng mL(-1) (6.15 pM) was obtained. The proposed DLMC method can efficiently prevent the penetration of matrix proteins through single cloaking membrane and completely eliminate nonspecific adsorption. It has great potential in providing a versatile way for direct determination of serum sample with ultra-sensitivity.

Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine.

Protein methylation is involved in dozens of biological processes and plays an important role in adjusting protein physicochemical properties, conformation and function. However, with the rapid increase of protein sequence entering into databanks, the gap between the number of known sequence and the number of known methylation annotation is widening rapidly. Therefore, it is vitally significant to develop a computational method for quick and accurate identification of methylation sites. In this study, a novel predictor (Methy_SVMIACO) based on support vector machine (SVM) and improved ant colony optimization algorithm (IACO) is developed to identify methylation sites. The IACO is utilized to find the optimal feature subset and parameter of SVM, while SVM is employed to perform the identification of methylation sites. Comparison of the IACO with conventional ACO shows that the IACO converges quickly toward the global optimal solution and it is more useful tool for feature selection and SVM parameter optimization. The performance of Methy_SVMIACO is evaluated with a sensitivity of 85.71%, a specificity of 86.67%, an accuracy of 86.19% and a Matthew's correlation coefficient (MCC) of 0.7238 for lysine as well as a sensitivity of 89.08%, a specificity of 94.07%, an accuracy of 91.56% and a MCC of 0.8323 for arginine in 10-fold cross-validation test. It is shown through the analysis of the optimal feature subset that some upstream and downstream residues play important role in the methylation of arginine and lysine. Compared with other existing methods, the Methy_SVMIACO provides higher Acc, Sen and Spe, indicating that the current method may serve as a powerful complementary tool to other existing approaches in this area. The Methy_SVMIACO can be acquired freely on request from the authors.

Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes.

With the rapid increment of protein sequence data, it is indispensable to develop automated and reliable predictive methods for protein function annotation. One approach for facilitating protein function prediction is to classify proteins into functional families from primary sequence. Being the most important group of all proteins, the accurate prediction for enzyme family classes and subfamily classes is closely related to their biological functions. In this paper, for the prediction of enzyme subfamily classes, the Chou's amphiphilic pseudo-amino acid composition [Chou, K.C., 2005. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21, 10-19] has been adopted to represent the protein samples for training the 'one-versus-rest' support vector machine. As a demonstration, the jackknife test was performed on the dataset that contains 2640 oxidoreductase sequences classified into 16 subfamily classes [Chou, K.C., Elrod, D.W., 2003. Prediction of enzyme family classes. J. Proteome Res. 2, 183-190]. The overall accuracy thus obtained was 80.87%. The significant enhancement in the accuracy indicates that the current method might play a complementary role to the exiting methods.

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.

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.