A protein structural class prediction method based on novel features

In this study, a 12-dimensional feature vector is constructed to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Among the 12 features, 6 novel features are specially designed to improve the prediction accuracies for α/β and α + β classes based on the distributions of α-helices and β-strands and the characteristics of parallel β-sheets and anti-parallel β-sheets. To evaluate our method, the jackknife cross-validating test is employed on two widely-used datasets, 25PDB and 1189 datasets with sequence similarity lower than 40% and 25%, respectively. The performance of our method outperforms the recently reported methods in most cases, and the 6 newly-designed features have significant positive effect to the prediction accuracies, especially for α/β and α + β classes.     

Search for promoter sites of prokaryotic DNA using learning techniques

Using learning techniques previously described in this journal, we have built an expert system able to point to the start DNA point of a sequence and therefore to recognize a promoter. However, to build this system, we have focused on the TATA box and its environment. We have used this expert system to look for new promoters and also to construct new promoters. The results obtained are discussed.     

A novel method for N-terminal acetylation prediction

The NetAcet method has been developed to make predictions of N-terminal acetylation sites, but more information of the data set could be utilized to improve the performance of the model. By employing a new way to extract patterns from sequences and using a sample balancing mechanism, we obtained a correlation coefficient of 0.85, and a sensitivity of 93% on an independent mammalian data set. A web server utilizing this method has been constructed and is available at http://166.111.24.5/acetylation.html.     

A novel protein structural classes prediction method based on predicted secondary structure

Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E–H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between α/β class and α + β class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate α/β and α + β classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html.     

A comparison study on feature selection of DNA structural properties for promoter prediction

Background  

Promoter prediction is an integrant step for understanding gene regulation and annotating genomes. Traditional promoter analysis is mainly based on sequence compositional features. Recently, many kinds of structural features have been employed in promoter prediction. However, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. Thus it is necessary to choose some appropriate features for the prediction task.     

A novel method for DNA molecular counting.

We have developed a new method for counting DNA molecules using 'capillary-plates' consisting of a large number of small glass-capillary 'channels' fused together in parallel. PCR mixtures containing serially diluted DNA templates with the DNA indicator dye Hoechst 33258 were poured into the plates and sealed with silicone rubber-plates. Following 40 PCR cycles, fluorescence microscopy revealed that the fluorescence in some channels had increased about three-times more than in others at template concentrations of 1 fM or lower. No bright fluorescence was observed in the absence of template. The relationship between the proportion of fluorescent channels in the capillary-plates and the template concentrations was linear according to Poisson probabilities in the range of 0.1-1,000 aM. These results demonstrate the amplification of single templates in the channels, and that a small number of templates could be quantified by counting the proportion of positive channels on the capillary-plates.     

A novel method for preparing single-stranded DNA for pyrosequencing

Pyrosequencing technology is a powerful genotyping tool that requires the generation of single stranded DNA. Currently, two simple, solid-phase-based methods are available for this, but they require special equipment, they are not automated, and they are relatively expensive because of the need for biotinylated polymerase chain reaction primers. In this article, an enzymatic liquid-phase method for the generation of high-quality, single-stranded DNA, and its novel use for Pyrosequencing are described. The method has also been fully automated.     

A novel training-free method for real-time prediction of femoral strain

Surrogate methods for rapid calculation of femoral strain are limited by the scope of the training data. We compared a newly developed training-free method based on the superposition principle (Superposition Principle Method, SPM) and popular surrogate methods for calculating femoral strain during activity. Finite-element calculations of femoral strain, muscle, and joint forces for five different activity types were obtained previously. Multi-linear regression, multivariate adaptive regression splines, and Gaussian process were trained for 50, 100, 200, and 300 random samples generated using Latin Hypercube (LH) and Design of Experiment (DOE) sampling. The SPM method used weighted linear combinations of 173 activity-independent finite-element analyses accounting for each muscle and hip contact force. Across the surrogate methods, we found that 200 DOE samples consistently provided low error (RMSE < 100 µε), with model construction time ranging from 3.8 to 63.3 h and prediction time ranging from 6 to 1236 s per activity. The SPM method provided the lowest error (RMSE = 40 µε), the fastest model construction time (3.2 h) and the second fastest prediction time per activity (36 s) after Multi-linear Regression (6 s). The SPM method will enable large numerical studies of femoral strain and will narrow the gap between bone strain prediction and real-time clinical applications.     

A novel method for rapid isolation of plasmid DNA

A new disposable chromatographic column, pZ523, has been developed for separating plasmid DNA from bacterial chromosomal DNA. Use of pZ523 spun columns eliminates the need for ethidium bromide-cesium chloride density gradients which require long centrifugation times. pZ523 purified plasmids have been shown to be of purity suitable for restriction analysis, ligation, transfection of mammalian cells and transformation of bacteria. Unlike the traditional ultracentrifugation method, pZ523 offers an extremely rapid alternative method for purifying large amounts of plasmid DNA (2.5 mg to 4.5 mg) from cleared bacterial lysates in only 25 minutes.     

A novel method for the isolation of mycobacterial DNA

Abstract DNA was isolated from mycobacteria by a simplified procedure. Cells were suspended in 6 M guanidinium chloride, the suspension was cooled to −70 °C, then incubated at 65 °C for 10 min, cooled in ice, deproteinized by chloroform and DNA was recovered from the supernatant. The procedure was used to obtain DNA from several mycobacteria (1 × 10⁹) or more cells) including Mycobacterium neoaurum M. fortuitum M. phlei and M. smegmatis . Each of the species was shown to have two ribosomal RNA operons per genome, and preliminary evidence was obtained which suggests that one of these operons is homologous with one of the operons of M. smegmatis .     

SUMOhydro: a novel method for the prediction of sumoylation sites based on hydrophobic properties

Sumoylation is one of the most essential mechanisms of reversible protein post-translational modifications and is a crucial biochemical process in the regulation of a variety of important biological functions. Sumoylation is also closely involved in various human diseases. The accurate computational identification of sumoylation sites in protein sequences aids in experimental design and mechanistic research in cellular biology. In this study, we introduced amino acid hydrophobicity as a parameter into a traditional binary encoding scheme and developed a novel sumoylation site prediction tool termed SUMOhydro. With the assistance of a support vector machine, the proposed method was trained and tested using a stringent non-redundant sumoylation dataset. In a leave-one-out cross-validation, the proposed method yielded an excellent performance with a correlation coefficient, specificity, sensitivity and accuracy equal to 0.690, 98.6%, 71.1% and 97.5%, respectively. In addition, SUMOhydro has been benchmarked against previously described predictors based on an independent dataset, thereby suggesting that the introduction of hydrophobicity as an additional parameter could assist in the prediction of sumoylation sites. Currently, SUMOhydro is freely accessible at http://protein.cau.edu.cn/others/SUMOhydro/.     

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.     

A novel common triplet profile for GC-rich prokaryotic genomes

It has been reported that there is a majority triplet profile among genomes, which was considered as a reflection of general mechanisms of genome evolution (Albrecht-Buehler, 2007). However, there are actually, according to our further analysis and at least among prokaryotic genomes, two common triplet profiles: one is from low-GC content genomes; the other is from high-GC content genomes. Both common profiles would be direct reflections of GC content variations and strand symmetry of genomic sequences.     

Survey for g-proteins in the prokaryotic genomes: prediction of functional roles based on classification

The members of the family of G-proteins are characterized by their ability to bind and hydrolyze guanosine triphosphate (GTP) to guanosine diphosphate (GDP). Despite a common biochemical function of GTP hydrolysis shared among the members of the family of G-proteins, they are associated with diverse biological roles. The current work describes the identification and detailed analysis of the putative G-proteins encoded in the completely sequenced prokaryotic genomes. Inferences on the biological roles of these G-proteins have been obtained by their classification into known functional subfamilies. We have identified 497 G-proteins in 42 genomes. Seven small GTP-binding protein homologues have been identified in prokaryotes with at least two of the diagnostic sequence motifs of G-proteins conserved. The translation factors have the largest representation (234 sequences) and are found to be ubiquitous, which is consistent with their critical role in protein synthesis. The GTP_OBG subfamily comprises of 79 sequences in our dataset. A total of 177 sequences belong to the subfamily of GTPase of unknown function and 154 of these could be associated with domains of known functions such as cell cycle regulation and t-RNA modification. The large GTP-binding proteins and the alpha-subunit of heterotrimeric G-proteins are not detected in the genomes of the prokaryotes surveyed.