ABSTRACT
Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.
ABSTRACT
Objective To assess the value of serum amyloid A(SAA)in patients with acute exacerbation of chronic pulmonary diseases. Methods Seventy AECOPD patients were randomly chosen. The AECOPD patients were divided into bacterial infection induced group and non-bacterial infection induced group by sputum bacteria culture. Thirty five SCOPD patients were chosen as control group. General data was collected. Lung function ,chest X ray,blood routine,CRP,SAA,IL6 and PCT were deteced and compared in the 3 groups. The diagnostic value of SAA to distinguish bacterial infection induced AECOPD was estimated. Results SAA of both AECOPD sub-groups were significantly higher than that of healthy controls. SAA in infection group is higher that that in exacerba-tion group. In terms of ROC curve,AUC was 0.8682 for SAA to distinguish merging bacterial infection,and the cut-off value was 72.10 mg/L with sensitivity of 94.29% and specificity of 65.71%. Conclusion SAA increases in AECOPD patients,and more obviously in AECOPD patients with bacterial infection. SAA may be used as a reliable biomarker not only to distinguish AECOPD patients from SCOPD patients ,but also distinguish merging bacterial infection during AECOPD.