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

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep - convolutional architecture. We tested configurations of Deep N-to-1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better-performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one-hot encoding, converting each character into a numerical representation, which is straightforward for non-numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI-BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

Genome Sequencing Reveals the Potential of Enterobacter sp. Strain UNJFSC003 for Hydrocarbon Bioremediation

Bioremediation induced by bacteria offers a promising alternative for the contamination of aromatic hydrocarbons due to their metabolic processes suitable for the removal of these pollutants, as many of them are carcinogenic molecules and dangerous to human health. Our research focused on isolating a bacterium from the rhizosphere of the tara tree with the ability to degrade polycyclic aromatic hydrocarbons, using draft genomic sequencing and computational analysis. Enterobacter sp. strain UNJFSC 003 possesses 4460 protein-coding genes, two rRNA genes, 77 tRNA genes, and a GC content of 54.38%. A taxonomic analysis of our strain revealed that it has an average nucleotide identity (ANI) of 87.8%, indicating that it is a new native Enterobacteria. Additionally, a pangenomic analysis with 15 strains demonstrated that our strain has a phylogenetic relationship with strain FDAARGOS 1428 (Enterobacter cancerogenus), with a total of 381 core genes and 4778 accessory genes. Orthologous methods predicted that strain UNJFSC 003 possesses genes with potential for use in hydrocarbon bioremediation. Genes were predicted in the sub-pathways for the degradation of homoprotocatechuate and phenylacetate, primarily located in the cytoplasm. Studies conducted through molecular modeling and docking revealed the affinity of the predicted proteins in the degradation of benzo[a]pyrene in the homoprotocatechuate sub-pathway, specifically hpcB, which has enzymatic activity as a dioxygenase, and hpcC, which functions as an aldehyde dehydrogenase. This study provides information on native strains from Lomas de Lachay with capabilities for the bioremediation of aromatic hydrocarbons and other compounds.

Mesomycoplasma (Mycoplasma) ovipneumoniae dihydrolipoamide dehydrogenase is an immunogenic plasminogen binding protein and a putative adhesin

The interaction of Mesomycoplasma (Mycoplasma) ovipneumoniae (M. ovipneumoniae) with host cells is a pivotal step in the infection process, underlining the necessity to develop vaccines and therapeutic approaches targeting the pathogen's key invasion mechanisms. The bacterium's capacity for adherence, invasion, and subsequent evasion of the host immune response underpins its pathogenicity, rendering adherence genes feasible vaccine targets. This study focuses on pyruvate dehydrogenase complex component E3 (PdhD), a membrane-anchored surface protein implicated in these pathogenic processes. Bioinformatics analysis reveals the conservation of PdhD sequence within M. ovipneumoniae. Membrane protein extraction, immunoblotting and ELISA assay have confirmed the presence of PdhD on the M. ovipneumoniae surface and cytoplasm, suggesting its multifunctionality. Our research employed antibody inhibition assays to characterize the bacterial adhesion suppression by anti-PdhD antibodies, complemented by bactericidal complement assays, supporting its candidacy as a putative vaccine target. The ELISA binding assay substantiated that PdhD binded to plasminogen (Plg) in a dose-dependent manner. Notably, PdhD is also involved in biofilm formation. The inhibitory effect of anti-PdhD sera on biofilm formation is congruent with novel therapeutic strategies targeting related mycoplasmas. This study reports the characterization of the first virulence-associated protein PdhD of M. ovipneumoniae and suggests its potential as a vaccine target to combat M. ovipneumoniae infection.

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.

Predicting the subcellular location of prokaryotic proteins with DeepLocPro

MOTIVATION

Protein subcellular location prediction is a widely explored task in bioinformatics because of its importance in proteomics research. We propose DeepLocPro, an extension to the popular method DeepLoc, tailored specifically to archaeal and bacterial organisms.

RESULTS

DeepLocPro is a multiclass subcellular location prediction tool for prokaryotic proteins, trained on experimentally verified data curated from UniProt and PSORTdb. DeepLocPro compares favorably to the PSORTb 3.0 ensemble method, surpassing its performance across multiple metrics in our benchmark experiment.

AVAILABILITY AND IMPLEMENTATION

The DeepLocPro prediction tool is available online at https://ku.biolib.com/deeplocpro and https://services.healthtech.dtu.dk/services/DeepLocPro-1.0/.

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.