Identification, Characterization and Expression Analysis of Anthocyanin Biosynthesis-related bHLH Genes in Blueberry (Vaccinium corymbosum L.)

Basic helix-loop-helix proteins (bHLHs) play very important roles in the anthocyanin biosynthesis of many plant species. However, the reports on blueberry anthocyanin biosynthesis-related bHLHs were very limited. In this study, six anthocyanin biosynthesis-related bHLHs were identified from blueberry genome data through homologous protein sequence alignment. Among these blueberry bHLHs, VcAN1, VcbHLH42-1, VcbHLH42-2 and VcbHLH42-3 were clustered into one group, while VcbHLH1-1 and VcbHLH1-2 were clustered into the other group. All these bHLHs were of the bHLH-MYC_N domain, had DNA binding sites and reported conserved amino acids in the bHLH domain, indicating that they were all G-box binding proteins. Protein subcellular location prediction result revealed that all these bHLHs were nucleus-located. Gene structure analysis showed that VcAN1 gDNA contained eight introns, while all the others contained seven introns. Many light-, phytohormone-, stress- and plant growth and development-related cis-acting elements and transcription factor binding sites (TFBSs) were identified in their promoters, but the types and numbers of cis-elements and TFBSs varied greatly between the two bHLH groups. Quantitative real-time PCR results showed that VcAN1 expressed highly in old leaf, stem and blue fruit, and its expression increased as the blueberry fruit ripened. Its expression in purple podetium and old leaf was respectively significantly higher than in green podetium and young leaf, indicating that VcAN1 plays roles in anthocyanin biosynthesis regulation not only in fruit but also in podetium and leaf. VcbHLH1-1 expressed the highest in young leaf and stem, and the lowest in green fruit. The expression of VcbHLH1-1 also increased as the fruit ripened, and its expression in blue fruit was significantly higher than in green fruit. VcbHLH1-2 showed high expression in stem but low expression in fruit, especially in red fruit. Our study indicated that the anthocyanin biosynthesis regulatory functions of these bHLHs showed certain spatiotemporal specificity. Additionally, VcAN1 might be a key gene controlling the anthocyanin biosynthesis in blueberry, whose function is worth exploring further for its potential applications in plant high anthocyanin breeding.

PSL-Recommender: Protein Subcellular Localization Prediction using Recommender System

Identifying a protein’s subcellular location is of great interest for understanding its function and behavior within the cell. In the last decade, many computational approaches have been proposed as a surrogate for expensive and labor-intensive wet-lab methods that are used for protein subcellular localization. Yet, there is still much room for improving the prediction accuracy of these methods. In this article, we meant to develop a customized computational method rather than using common machine learning predictors, which are used in the majority of computational research on this topic. The neighbourhood regularized logistic matrix factorization technique was used to create PSL-Recommender (Protein subcellular location recommender), a GO-based predictor. We declared statistical inference as the driving force behind the PSL-Recommender here. Following that, it was benchmarked against twelve well-known methods using five different datasets, demonstrating outstanding performance. Finally, we discussed potential research avenues for developing a comprehensive prediction tool for protein subcellular location prediction. The datasets and codes are available at: https://github.com/RJamali/PSL-Recommender

Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis

Stimulated by the 5-steps rule during the last decade or so, computational proteomics has achieved remarkable progresses in the following three areas: (1) protein structural class prediction; (2) protein subcellular location prediction; (3) post-translational modification (PTM) site prediction. The results obtained by these predictions are very useful not only for an in-depth study of the functions of proteins and their biological processes in a cell, but also for developing novel drugs against major diseases such as cancers, Alzheimer’s, and Parkinson’s. Moreover, since the targets to be predicted may have the multi-label feature, two sets of metrics are introduced: one is for inspecting the global prediction quality, while the other for the local prediction quality. All the predictors covered in this review have a userfriendly web-server, through which the majority of experimental scientists can easily obtain their desired data without the need to go through the complicated mathematics.