PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets

C/EBPβ Acts Upstream of NF-κB P65 Subunit in Ox-LDL-Induced IL-1β Production by Macrophages

Background/Aims: Interleukin-1β (IL-1β) is one of the critical inflammatory factors during atherogenesis. CCAAT/enhancer binding proteins β (C/EBPβ), a regulator of IL-1β production, recently been evidenced as a key player in the development of atherosclerosis. However, the mechanisms of how C/EBPβ regulates the production of IL-1β are unclear. In this study, we aimed to explore the role of C/EBPβ in regulating IL-1β production in macrophages after oxidized low-density lipoprotein (ox-LDL) exposure and the underlying mechanisms. Methods: RAW264.7 macrophages were treated with 0, 25, 50 or 100 μg/ml ox-LDL for 12, 24 or 48 h. Small interfering RNAs were used to silence related proteins. The gene and protein expression levels were determined by quantitative real-time polymerase chain reaction or western blot (WB). IL-1β secretion was assessed by enzyme-linked immunosorbent assay. The cytoplasmic and nuclear proteins were evaluated by nuclear fractionation followed by WB. Localization of p65 was observed by immunofluorescence. The binding activity of p65 to IL-1β was tested by dual-luciferase reporter assay. Results: Ox-LDL increased IL-1β production, accompanied with increasing C/EBPβ and p65 expression in a dose- and time-dependent manner. Moreover, C/EBPβ deficiency in macrophages blocked ox-LDL-induced increases in IL-1β expression, maturation as well as p65 activation. However, p65 deficiency inhibited the increase in IL-1β production, but not C/EBPβ expression. Dual-luciferase reporter results showed that overexpression of C/EBPβ significantly enhanced binding activity of p65 to IL-1β promoter. In addition, C/EBP 1β deficiency in macrophages abolished the ox-LDL-induced gene transcription increases of IL-1β, IL-6, p65 and caspase-1. Conclusions: Our results demonstrate that C/EBPβ acts upstream of NF-κB p65 subunit in ox-LDL-induced IL-1β production in macrophages and may regulate IL-1β maturation by promoting caspase-1. C/EBPβ may be a promising candidate for the prevention and treatment of atherosclerosis

Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM

BackgroundIdentification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.ResultsHere we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools.ConclusionsComparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots

Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM.

BackgroundIdentification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.ResultsHere we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools.ConclusionsComparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots

PPARA

Abstract Background Left cardiac pumping function determines the compensatory capacity of the cardiovascular system following acute high‐altitude exposure. Variations in cardiac output (CO) at high altitude are inconsistent between individuals, and genetic susceptibility may play a crucial role. We sought to identify genetic causes of cardiac pumping function variations and describe the genotype–phenotype correlations. Methods A total of 151 young male volunteers were recruited and transferred to Lhasa (3,700 m) from Chengdu (<500 m) by plane. Genetic information related to hypoxic signaling and cardiovascular‐related pathways was collected before departure. Echocardiography was performed both before departure and 24 hr after arrival at high altitude. Results Here we reported that PPARA variants were closely related to high‐altitude cardiac function. The variants of rs6520015 C‐allele and rs7292407 A‐allele significantly increased the risk for cardiac pumping function reductions following acute high‐altitude exposure. In addition, the individuals carrying haplotypes in PPARA, namely, rs135538 C‐allele, rs4253623 A‐allele, rs6520015 C‐allele and rs7292407 A‐allele (C‐A‐C‐A), suffered a 7.27‐fold risk for cardiac pumping function reduction (95% CI: 2.39–22.15, p = .0006) compared with those carrying the wild‐type haplotype. Conclusions This self‐controlled study revealed that PPARA variations significantly increased the risk for cardiac pumping function reductions following acute high‐altitude exposure, providing a potential predictive marker before high‐altitude exposure and targets in mechanistic studies

Examples to show the predicted results by our predictor based on five datasets.

<p>Examples to show the predicted results by our predictor based on five datasets.</p

This graph shows the overlapped PROFEAT features of Z277 and Z498.

<p>After feature selection by SVM-REF, 157 and 155 PROFEAT features are selected in top322 features for datasets Z277 and Z498, and have significant overlap (117 common features).</p