CarSPred: a computational tool for predicting carbonylation sites of human proteins

• Published in: PloS one 2014-10, Vol.9 (10), p.e111478-e111478
• Main Authors: Lv, Hongqiang, Han, Jiuqiang, Liu, Jun, Zheng, Jiguang, Liu, Ruiling, Zhong, Dexing
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Alzheimers disease; Amino acids; Arginine; Bioinformatics; Biology and Life Sciences; Biomarkers; Carbonyl groups; Carbonyls; Comparative analysis; Computation; Computer and Information Sciences; Computer applications; Computer programs; Datasets; Engineering; Etiology; Feature extraction; Humans; Hydrophobicity; Identification; Lysine; Mammals; Mass spectrometry; Mass spectroscopy; Models, Biological; Neural networks; Oxidative stress; Pathogenesis; Phosphorylation; Post-translation; Predictions; Proline; Protein Carbonylation; Proteins; Proteome - chemistry; Proteome - metabolism; Proteomics; Redundancy; Research and Analysis Methods; Residues; Sequence Analysis, Protein - methods; Software; Software development tools; Threonine
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0111478

Protein carbonylation is one of the most pervasive oxidative stress-induced post-translational modifications (PTMs), which plays a significant role in the etiology and progression of several human diseases. It has been regarded as a biomarker of oxidative stress due to its relatively early formation and stability compared with other oxidative PTMs. Only a subset of proteins is prone to carbonylation and most carbonyl groups are formed from lysine (K), arginine (R), threonine (T) and proline (P) residues. Recent advancements in analysis of the PTM by mass spectrometry provided new insights into the mechanisms of protein carbonylation, such as protein susceptibility and exact modification sites. However, the experimental approaches to identifying carbonylation sites are costly, time-consuming and capable of processing a limited number of proteins, and there is no bioinformatics method or tool devoted to predicting carbonylation sites of human proteins so far. In the paper, a computational method is proposed to identify carbonylation sites of human proteins. The method extracted four kinds of features and combined the minimum Redundancy Maximum Relevance (mRMR) feature selection criterion with weighted support vector machine (WSVM) to achieve total accuracies of 85.72%, 85.95%, 83.92% and 85.72% for K, R, T and P carbonylation site predictions respectively using 10-fold cross-validation. The final optimal feature sets were analysed, the position-specific composition and hydrophobicity environment of flanking residues of modification sites were discussed. In addition, a software tool named CarSPred has been developed to facilitate the application of the method. Datasets and the software involved in the paper are available at https://sourceforge.net/projects/hqlstudio/files/CarSPred-1.0/.