Protein methylation is one type of reversible post-translational modifications (PTMs), which plays vital roles in many cellular processes such as transcription activity, DNA repair. Experimental identification of methylation sites on proteins without prior knowledge is costly and time-consuming. In silico prediction of methylation sites might not only provide researches with information on the candidate sites for further determination, but also facilitate to perform downstream characterizations and site-specific investigations. In the present study, a novel approach based on Bi-profile Bayes feature extraction combined with support vector machines (SVMs) was employed to develop the model for Prediction of Protein Methylation Sites (BPB-PPMS) from primary sequence. Methylation can occur at many residues including arginine, lysine, histidine, glutamine, and proline. For the present, BPB-PPMS is only designed to predict the methylation status for lysine and arginine residues on polypeptides due to the absence of enough experimentally verified data to build and train prediction models for other residues. The performance of BPB-PPMS is measured with a sensitivity of 74.71%, a specificity of 94.32% and an accuracy of 87.98% for arginine as well as a sensitivity of 70.05%, a specificity of 77.08% and an accuracy of 75.51% for lysine in 5-fold cross validation experiments. Results obtained from cross-validation experiments and test on independent data sets suggest that BPB-PPMS presented here might facilitate the identification and annotation of protein methylation. Besides, BPB-PPMS can be extended to build predictors for other types of PTM sites with ease. For public access, BPB-PPMS is available at http://www.bioinfo.bio.cuhk.edu.hk/bpbppms.

Protein intrinsic disorder has been shown to play an important role in some posttranslational modifications (PTM). In this paper, we systematically investigated the correlation between protein disorder and dozens of PTMs using data from UniProt/SwissProt and 3-D structures solved by NMR from Protein Data Bank. We observed that many PTMs have a preference for occurrence in disordered regions, including phospho-serine/-threonine/-tyrosine, hydroxylation, sulfotyrosine, S-geranylgeranyl cysteine, deamidated glutamine, 4-carboxyglutamate, 6'-bromotryptophan and most of methylation; while a few PTMs have a preference for occurrence in ordered regions, including 4-aspartylphosphate, S-nitrosocysteine, tele-methylhistidine, FMN conjugation, 4,5-dihydroxylysine, 3-methylthioaspartic acid, most of ADP-ribosylation, and most of FAD attachment. It is also noted that acetyllysine does not show any significant preference for occurrence in either disordered or ordered regions. Further analysis of NMR structures suggested disorder-toorder transitions might be introduced by modifications of phospho-serine/-threonine, mono-/di-/tri-methyllysine, sulfotyrosine, 4-carboxyglutamate, and potentially 4-hydroxyproline. This study sheds light on the functions and mechanisms of various PTMs.

Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), squamous cell lung cancer (SQCLC) as well as large cell lung cancer. Many previous studies demonstrated that DNA methylation has emerged as potential lung cancer-specific biomarkers. However, whether there exists a set of DNA methylation markers simultaneously distinguishing such three types of lung cancers remains elusive. In the present study, ROC (Receiving Operating Curve), RFs (Random Forests) and mRMR (Maximum Relevancy and Minimum Redundancy) were proposed to capture the unbiased, informative as well as compact molecular signatures followed by machine learning methods to classify LADC, SQCLC and SCLC. As a result, a panel of 16 DNA methylation markers exhibits an ideal classification power with an accuracy of 86.54%, 84.6% and a recall 84.37%, 85.5% in the leave-one-out cross-validation (LOOCV) and independent data set test experiments, respectively. Besides, comparison results indicate that ensemble-based feature selection methods outperform individual ones when combined with the incremental feature selection (IFS) strategy in terms of the informative and compact property of features. Taken together, results obtained suggest the effectiveness of the ensemble-based feature selection approach and the possible existence of a common panel of DNA methylation markers among such three types of lung cancer tissue, which would facilitate clinical diagnosis and treatment.

Lysine acetylation is a reversible post-translational modification (PTM) which has been linked to many biological and pathological implications. Hence, localization of lysine acetylation is essential for deciphering the mechanism of such implications. Whereas many acetylated lysines in human proteins have been localized through experimental approaches in wet lab, it still fails to reach completion. In the present study, we proposed a novel feature extraction approach, bi-relative adapted binomial score Bayes (BRABSB), combined with support vector machines (SVMs) to construct a human-specific lysine acetylation predictor, which yields, on average, a sensitivity of 83.91%, a specificity of 87.25% and an accuracy of 85.58%, in the case of 5-fold cross validation experiments. Results obtained through the validation on independent data sets show that the proposed approach here outperforms other existing lysine acetylation predictors. Furthermore, due to the fact that global analysis of human lysine acetylproteins, which would ultimately facilitate the systematic investigation of the biological and pathological consequences associated with lysine acetylation events, remains to be resolved, we made an attempt to systematically analyze human lysine acetylproteins, demonstrating their diversity with respect to subcellular localization as well as biological process and predominance by "binding" in terms of molecular function. Our analysis also revealed that human lysine acetylproteins are significantly enriched in neurodegenerative disorders and cancer pathways. Remarkably, lysine acetylproteins in mitochondria are significantly related to neurodegenerative disorders and those in the nucleus are instead significantly involved in pathways in cancers, all of which might ultimately provide novel global insights into such pathological processes for the therapeutic purpose. The web server is deployed at http://www.bioinfo.bio.cuhk.edu.hk/bpbphka.

The 0.7Pb(Mg1/3Nb2/3)O3-0.3PbTiO3(0.7PMN-0.3PT) nanorods were obtained via hydrothermal method with high yield (over 78%). Then, new piezoelectric nanocomposites based on (1−x)Pb(Mg1/3Nb2/3)O3-xPbTiO3 (PMN-PT) nanorods were fabricated by dispersing the 0.7PMN-0.3PT nanorods into piezoelectric poly(vinylidene fluoride) (PVDF) polymer. The mechanical behaviors of the nanocomposites were investigated. The voltage and current generation of PMN-PT/PVDF nanocomposites were also measured. The results showed that the tensile strength, yield strength, and Young’s modulus of nanocomposites were enhanced as compared to that of the pure PVDF. The largest Young’s modulus of 1.71 GPa was found in the samples with 20 wt % nanorod content. The maximum output voltage of 10.3 V and output current of 46 nA were obtained in the samples with 20 wt % nanorod content, which was able to provide a 13-fold larger output voltage and a 4.5-fold larger output current than that of pure PVDF piezoelectric polymer. The current density of PMN-PT/PVDF nanocomposites is 20 nA/cm2. The PMN-PT/PVDF nanocomposites exhibited great potential for flexible self-powered sensing applications.