Protein Subcellular Localization Prediction

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein-protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.

Multi-omics identification of a key glycosyl hydrolase gene FtGH1 involved in rutin hydrolysis in Tartary buckwheat (Fagopyrum tataricum)

Rutin, a flavonoid rich in buckwheat, is important for human health and plant resistance to external stresses. The hydrolysis of rutin to quercetin underlies the bitter taste of Tartary buckwheat. In order to identify rutin hydrolysis genes, a 200 genotypes mini-core Tartary buckwheat germplasm resource was re-sequenced with 30-fold coverage depth. By combining the content of the intermediate metabolites of rutin metabolism with genome resequencing data, metabolite genome-wide association analyses (GWAS) eventually identified a glycosyl hydrolase gene FtGH1, which could hydrolyse rutin to quercetin. This function was validated both in Tartary buckwheat overexpression hairy roots and in vitro enzyme activity assays. Mutation of the two key active sites, which were determined by molecular docking and experimentally verified via overexpression in hairy roots and transient expression in tobacco leaves, exhibited abnormal subcellular localization, suggesting functional changes. Sequence analysis revealed that mutation of the FtGH1 promoter in accessions of two haplotypes might be necessary for enzymatic activity. Co-expression analysis and GWAS revealed that FtbHLH165 not only repressed FtGH1 expression, but also increased seed length. This work reveals a potential mechanism behind rutin metabolism, which should provide both theoretical support in the study of flavonoid metabolism and in the molecular breeding of Tartary buckwheat.

Identification of Pyrabactin resistance 1-like (PYL) genes in Brachypodium distachyon and functional characterization of BdPYL5

Abscisic acid (ABA) is an endogenous phytohormone that plays an important role in regulating plant growth, development, and stress response. Pyrabactin resistance 1-like (PYR/PYL) proteins are ABA receptors and core components of ABA signalling in plants. This study identified nine PYL genes in the Brachypodium distachyon genome and they distribute on three chromosomes. Phylogenetical BdPYLs were classified into three clades. 81 protein-protein interactions between 9 BdPYLs and 9 BdPP2C proteins were predicted and 66 pairs were verified by yeast two-hybrid assay previously. Relatively, BdPYL genes are expressed in leaves at high level, and ABA and drought regulate their expression. A homologue of Arabidopsis PYL9, BdPYL5 was selected to overexpress in Arabidopsis to characterize its function. In general, overexpression of BdPYL5 enhanced ABA sensitivity and drought tolerance, implying its conserved function. Our study lays the foundation for further functional elucidation of BdPYL genes.

Western blotting: a powerful staple in scientific and biomedical research

Western blotting (WB), also known as immunoblotting, is a well-known molecular biology method that biologists often use to investigate many features of the protein, ranging from basic protein analysis to disease detection. WB is simple, unique, rapid, widely used routine tool with easy interpretation and definite results. It is being used in various fields of science, research and development, diagnostic labs and hospitals. The principle of WB is to accomplish the separation of proteins based on molecular weight and charge. This review addresses in detail the individual steps involved in the WB technique, its troubleshooting, internal loading controls, total protein staining and its diverse applications in scientific research and clinical settings, along with its future perspectives.

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite

Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.