Identification and Potential Roles of Human MicroRNAs in Ebola Virus Infection and Disease Pathogenesis.

Ebola virus (EBOV) is a highly pathogenic virus that causes a severe illness called Ebola virus disease (EVD). EVD has a high mortality rate and remains a significant threat to public health. Research on EVD pathogenesis has traditionally focused on host transcriptional responses. Limited recent studies, however, have revealed some information on the significance of cellular microRNAs (miRNAs) in EBOV infection and pathogenic mechanisms, but further studies are needed. Thus, this study aimed to identify and validate additional known and novel human miRNAs in EBOV-infected adult retinal pigment epithelial (ARPE) cells and predict their potential roles in EBOV infection and pathogenic mechanisms. We analyzed previously available small RNA-Seq data obtained from ARPE cells and identified 23 upregulated and seven downregulated miRNAs in the EBOV-infected cells; these included two novel miRNAs and 17 additional known miRNAs not previously identified in ARPE cells. In addition to pathways previously identified by others, these miRNAs are associated with pathways and biological processes that include WNT, FoxO, and phosphatidylinositol signaling; these pathways were not identified in the original study. This study thus confirms and expands on the previous study using the same datasets and demonstrates further the importance of human miRNAs in the host response and EVD pathogenesis during infection.

DomSVR: domain boundary prediction with support vector regression from sequence information alone.

Protein domains are structural and fundamental functional units of proteins. The information of protein domain boundaries is helpful in understanding the evolution, structures and functions of proteins, and also plays an important role in protein classification. In this paper, we propose a support vector regression-based method to address the problem of protein domain boundary identification based on novel input profiles extracted from AAindex database. As a result, our method achieves an average sensitivity of approximately 36.5% and an average specificity of approximately 81% for multi-domain protein chains, which is overall better than the performance of published approaches to identify domain boundary. As our method used sequence information alone, our method is simpler and faster.

Prediction of inter-residue contact clusters from hydrophobic cores.

A contact map is a key factor representing a specific protein structure. To simplify the protein contact map prediction, we predict the inter-residue contact clusters centred at the groups of their surrounding inter-residue contacts. In this paper, we adopt a Support Vector Machine (SVM)-based approach to predict the inter-residue contact cluster centres. The input of the SVM predictor includes sequence profile, evolutionary rate and predicted secondary structure. The SVM predictor is based on hydrophobic cores that may be considered as locations of the inter-residue contact clusters. About 35% of clustering centres of inter-residue contacts can be predicted accurately.

Protein fold classification with genetic algorithms and feature selection.

Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

Prediction of inter-residue contact clusters from hydrophobic cores.

A contact map is a key factor representing a specific protein structure. To simplify the protein contact map prediction, we predict the inter-residue contact clusters centered at the groups of their surrounding inter-residue contacts. In this paper, we adopt a Support Vector Machine (SVM)-based approach to predict the inter-residue contact cluster centers. The input of the SVM predictor includes sequence profile, evolutionary rate and predicted secondary structure. The SVM predictor is based on hydrophobic cores that may be considered as locations of the inter-residue contact clusters. About 35% of clustering centers of inter-residue contacts can be predicted accurately.