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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.