Subcellular locations of potential cell wall proteins in plants: predictors, databases and cross-referencing

Digging for Stress-Responsive Cell Wall Proteins for Developing Stress-Resistant Maize

As a vital component of plant cell walls, proteins play important roles in stress response by modifying the structure of cell walls and involving in the wall integrity signaling pathway. Recently, we have critically reviewed the predictors, databases, and cross-referencing of the subcellular locations of possible cell wall proteins (CWPs) in plants (Briefings in Bioinformatics 2018;19:1130-1140). Here, we briefly introduce strategies for isolating CWPs during proteomic analysis. Taking maize (Zea mays) as an example, we retrieved 1873 probable maize CWPs recorded in the UniProt KnowledgeBase (UniProtKB). After curation, 863 maize CWPs were identified and classified into 59 kinds of protein families. By referring to gene ontology (GO) annotations and gene differential expression in the Expression Atlas, we have highlighted the potential of CWPs acting in the front line of defense against biotic and abiotic stresses. Moreover, the analysis results of cis-acting elements revealed the responsiveness of the genes encoding CWPs toward phytohormones and various stresses. We suggest that the stress-responsive CWPs could be promising candidates for applications in developing varieties of stress-resistant maize.

Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins

Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information, and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants.