PSORTm: a bacterial and archaeal protein subcellular localization prediction tool for metagenomics data

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.

Proteomics Exploration of Brucella melitensis to Design an Innovative Multi-Epitope mRNA Vaccine

Brucellosis is a chronic and debilitating disease in humans, causing great economic losses in the livestock industry. Making an effective vaccine is one of the most important concerns for this disease. The new mRNA vaccine technology due to its accuracy and high efficiency has given promising results in various diseases. The objective of this research was to create a novel mRNA vaccine with multiple epitopes targeting Brucella melitensis. Seventeen antigenic proteins and their appropriate epitopes were selected with immunoinformatic tools and surveyed in terms of toxicity, allergenicity, and homology. Then, their presentation and identification by MHC cells and other immune cells were checked with valid tools such as molecular docking, and a multi-epitope protein was modeled, and after optimization, mRNA was analyzed in terms of structure and stability. Ultimately, the immune system's reaction to this novel vaccine was evaluated and the results disclosed that the designed mRNA construct can be an effective and promising vaccine that requires laboratory and clinical trials.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Natural language processing approach to model the secretion signal of type III effectors

Type III effectors are proteins injected by Gram-negative bacteria into eukaryotic hosts. In many plant and animal pathogens, these effectors manipulate host cellular processes to the benefit of the bacteria. Type III effectors are secreted by a type III secretion system that must "classify" each bacterial protein into one of two categories, either the protein should be translocated or not. It was previously shown that type III effectors have a secretion signal within their N-terminus, however, despite numerous efforts, the exact biochemical identity of this secretion signal is generally unknown. Computational characterization of the secretion signal is important for the identification of novel effectors and for better understanding the molecular translocation mechanism. In this work we developed novel machine-learning algorithms for characterizing the secretion signal in both plant and animal pathogens. Specifically, we represented each protein as a vector in high-dimensional space using Facebook's protein language model. Classification algorithms were next used to separate effectors from non-effector proteins. We subsequently curated a benchmark dataset of hundreds of effectors and thousands of non-effector proteins. We showed that on this curated dataset, our novel approach yielded substantially better classification accuracy compared to previously developed methodologies. We have also tested the hypothesis that plant and animal pathogen effectors are characterized by different secretion signals. Finally, we integrated the novel approach in Effectidor, a web-server for predicting type III effector proteins, leading to a more accurate classification of effectors from non-effectors.

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.

Comparative Analysis of the Chalcone-Flavanone Isomerase Genes in Six Citrus Species and Their Expression Analysis in Sweet Orange (Citrus sinensis)

Citrus fruit contains rich nutrients which is edible and of officinal value. Citrus flavanones are widely used in the treatment of cardiovascular and other diseases, and they are a foundational material of Chinese medicine. The chalcone-flavanone isomerase (CHI) plays a key role in flavanone synthesis. Therefore, we comprehensively analyzed CHI genes in Citrus species. Here, thirty CHI genes were identified for the first time in six Citrus species, which were divided into CHI and FAP groups. Evolutionary analysis showed that CHI gene members were highly conserved and were an ancient family. All CsCHI genes showed the highest expression level after the second physiological fruit-falling period in C. sinensis. CsCHI1 and CsCHI3 were highly expressed at 50 days after the flowering (DAF) stage in albedo. The expression of CsFAP2 and CsCHI3 genes at the 50 DAF stage was 16.5 and 24.3 times higher than that at the 220 DAF stage, respectively. The expression of CsCHI1, CsCHI3, and CsFAP2 genes in the peel was higher than that in the pulp, especially in common sweet orange. The CsCHI3 gene maintained a high expression level in the epicarp and juice sac at all periods. The members of CHIs interacted with chalcone synthase (CHS), flavonol synthase/flavanone 3-hydroxylase (FLS) and naringenin, and 2-oxoglutarate 3-dioxygenase (F3H) to form heterodimers, which might together play a regulatory role and participate in the flavonoid pathway. This study will provide the basis for the selection of flavonoids in plant tissues and periods and fundamental information for further functional studies.

Genome-Scale Mining of Novel Anchor Proteins of Corynebacterium glutamicum

The display of recombinant proteins on the surfaces of bacteria is a research topic with many possible biotechnology applications—among which, the choice of host cell and anchoring motif is the key for efficient display. Corynebacterium glutamicum is a promising host for surface display due to its natural advantages, while single screening methods and fewer anchor proteins restrict its application. In this study, the subcellular localization (SCL) predictor LocateP and tied-mixture hidden Markov models were used to analyze all five known endogenous anchor proteins of C. glutamicum and test the accuracy of the predictions. Using these two tools, the SCLs of all proteins encoded by the genome of C. glutamicum 13032 were predicted, and 14 potential anchor proteins were screened. Compared with the positive controls NCgl1221 and NCgl1337, three anchoring proteins—NCgl1307, NCgl2775, and NCgl0717—performed better. This study also discussed the applicability of the anchor protein screening method used in this experiment to other bacteria.

PSORTdb 4.0: expanded and redesigned bacterial and archaeal protein subcellular localization database incorporating new secondary localizations

Protein subcellular localization (SCL) is important for understanding protein function, genome annotation, and aids identification of potential cell surface diagnostic markers, drug targets, or vaccine components. PSORTdb comprises ePSORTdb, a manually curated database of experimentally verified protein SCLs, and cPSORTdb, a pre-computed database of PSORTb-predicted SCLs for NCBI's RefSeq deduced bacterial and archaeal proteomes. We now report PSORTdb 4.0 (http://db.psort.org/). It features a website refresh, in particular a more user-friendly database search. It also addresses the need to uniquely identify proteins from NCBI genomes now that GI numbers have been retired. It further expands both ePSORTdb and cPSORTdb, including additional data about novel secondary localizations, such as proteins found in bacterial outer membrane vesicles. Protein predictions in cPSORTdb have increased along with the number of available microbial genomes, from approximately 13 million when PSORTdb 3.0 was released, to over 66 million currently. Now, analyses of both complete and draft genomes are included. This expanded database will be of wide use to researchers developing SCL predictors or studying diverse microbes, including medically, agriculturally and industrially important species that have both classic or atypical cell envelope structures or vesicles.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.