PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria

Identification of therapeutic drug target of Shigella Flexneri serotype X through subtractive genomic approach and in-silico screening based on drug repurposing

Shigellosis, induced by Shigella flexneri, constitutes a significant health burden in developing nations, particularly impacting socioeconomically disadvantaged communities. Designated as the second most prevalent cause of diarrheal illness by the World Health Organization (WHO), it precipitates an estimated 212,000 fatalities annually. Within the spectrum of S. flexneri strains, serotype X is notably pervasive and resilient, yet its comprehensive characterization remains deficient. The present investigation endeavors to discern potential pharmacological targets and repurpose existing drug compounds against S. flexneri serotype X. Employing the framework of subtractive genomics, the study interrogates the reference genome of S. flexneri Serotype X (strain 2,002,017; UP000001884) to delineate its proteome into categories of non-homologous, non-paralogous, essential, virulent, and resistant constituents, thereby facilitating the identification of therapeutic targets. Subsequently, a screening of approximately 9000 compounds from the FDA library against the identified drug target aims to delineate efficacious agents for combating S. flexneri serotype X infections. The application of subtractive genomics methodology yields prognostic insights, unveiling non-paralogous proteins (n = 4122), non-homologues (n = 1803), essential (n = 1246), drug-like (n = 389), resistant (n = 167), alongside 42 virulent proteins within the reference proteome. This iterative process culminates in the identification of Serine O-acetyltransferase as a viable drug target. Subsequent virtual screening endeavors to unearth FDA-approved medicinal compounds capable of inhibiting Serine O-acetyltransferase. Noteworthy candidates such as DB12983, DB15085, DB16098, DB16185, and DB16262 emerge, exhibiting potential for mitigating S. flexneri Serotype X. Despite the auspicious findings, diligent scrutiny is imperative to ascertain the efficacy and safety profile of the proposed drug candidates vis-à-vis S. flexneri.

Global proteome of the saprophytic strain Leptospira biflexa and comparative analysis with pathogenic strain Leptospira interrogans uncover new pathogenesis mechanisms

Leptospira is a genus of bacteria that includes free-living saprophytic species found in water or soil, and pathogenic species, which are the etiologic agents of leptospirosis. Besides all the efforts, there are only a few proteins described as virulence factors in the pathogenic strain L. interrogans. This work aims to perform L. biflexa serovar Patoc1 strain Paris global proteome and to compare with the proteome database of pathogenic L. interrogans serovar Copenhageni strain Fiocruz L1-130. We identified a total of 2327 expressed proteins of L. biflexa by mass spectrometry. Using the Get Homologues software with the global proteome of L. biflexa and L. interrogans, we found orthologous proteins classified into conserved, low conserved, and specific proteins. Comparative bioinformatic analyses were performed to understand the biological functions of the proteins, subcellular localization, the presence of signal peptide, structural domains, and motifs using public softwares. These results lead to the selection of 182 low conserved within the saprophyte, and 176 specific proteins of L. interrogans. It is anticipated that these findings will indicate further studies to uncover virulence factors in the pathogenic strain. This work presents for the first time the global proteome of saprophytic strain L. biflexa serovar Patoc, strain Patoc1. SIGNIFICANCE: The comparative analysis established an array of specific proteins in pathogenic strain that will narrow down the identification of immune protective proteins that will help fight leptospirosis.

Design and characterization of a multi-epitope vaccine against Clostridium botulinum A3 Loch Maree intoxication in humans

Clostridium botulinum Loch Maree expresses an extremely potent botulinum neurotoxin subtype, A3 causing botulism and several gastrointestinal disorders in mammals. Several recombinant vaccines have been developed for human botulism and no vaccine is currently available for the treatment of diseases caused by other virulence factors. Hence, we designed, constructed, and characterized a multi-epitope vaccine from new virulence proteins identified from this organism using an immunoinformatics approach. The vaccine construct used in this study was designed from 6B cell linear epitopes, 12 cytotoxic T cell lymphocyte epitopes, and 15 helper T cell lymphocyte epitopes, with a defensin adjuvant and adjusting linker sequences. A molecular modeling approach was used to model, refine, and validate the 3D structure of the vaccine construct. Molecular docking studies were performed to determine the stability of the molecular interactions between the vaccine construct and human toll-like receptor 7. The in silico molecular cloning was used to clone a codon-optimized synthetic vaccine gene in pCYB1 vector and expressed in Escherichia coli. The results of this study identified six new virulence proteins: peptidoglycan hydrolase, SCP-like extracellular protein, N-acetylmuramoyl-l-alanine amidase, putative membrane protein, drug/metabolite exporter, and bacillolysin. The top B-cell, cytotoxic T-cell lymphocyte, and helper T-lymphocyte epitopes were predicted from these virulence proteins with greater accuracy and reliability. HLA-A*02:01 and HLA-A*03:01 were identified as HLA-A-binding alleles for cytotoxic T-cell lymphocyte epitopes. DRB1*0110 and DRB1*0115 are the dominant alleles that bind to helper T-cell lymphocyte epitopes. The synthetic gene construct was highly expressed in a heterologous host and produced considerable amounts of antigenic protein. The multi-epitope vaccine is more conservative in the sequence-structure-function link, immunogenic with less allergenicity, and possibly provokes cellular and humoral immunity. The present study suggests that the designed multi-epitope vaccine is a promising prophylactic candidate for the virulence and intoxication caused by subtype A3 strains.

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.

SignalP: The Evolution of a Web Server

SignalP ( https://services.healthtech.dtu.dk/services/SignalP-6.0/ ) is a very popular prediction method for signal peptides, the intrinsic signals that make proteins secretory. The SignalP web server has existed since 1995 and is now in its sixth major version. In this historical account, we (three authors who have taken part in the entire journey plus the first author of the latest version) describe the differences between the versions and discuss the various decisions taken along the way.

Pangenome diversification and resistance gene characterization in Salmonella Typhi prioritized RfaJ as a significant therapeutic marker

BACKGROUND

Salmonella Typhi stands as the etiological agent responsible for the onset of human typhoid fever. The pressing demand for innovative therapeutic targets against S. Typhi is underscored by the escalating prevalence of this pathogen and the severe nature of its infections. Consequently, this study employs pangenome analysis to scrutinize 119 S. Typhi-resistant strains, aiming to identify the most promising therapeutic targets originating from its core genome.

RESULTS

Subtractive genomics was employed to systematically eliminate non-homologous (n=1147), essential (n=551), drug-like (n=80), and pathogenicity-related (n=18) proteins from the initial pool of 3351 core genome proteins. Consequently, lipopolysaccharide 1,2-glucosyltransferase RfaJ was designated as the optimal pharmacological target due to its potential versatility. Furthermore, a compendium of 9000 FDA-approved compounds was repurposed for evaluation against the RfaJ drug target, with the specific intent of prioritizing novel, high-potency therapeutic candidates for combating S. Typhi. Ultimately, four compounds, namely DB00549 (Zafirlukast), DB15637 (Fluzoparib), DB15688 (Zavegepant), and DB12411 (Bemcentinib), were singled out as potential inhibitors based on the ligand-protein binding affinity (indicated by the lowest anticipated binding energy) and the overall stability of these compounds. Notably, molecular dynamics simulations, conducted over a 50 nanosecond interval, convincingly demonstrated the stability of these compounds in the context of the RfaJ protein.

CONCLUSION

In summary, the present findings hold significant promise as an initial stride in the broader drug discovery endeavor against S. Typhi infections. However, the experimental validation of the identified drug target and drug candidate is further required to increase the effectiveness of the applied methodology.

Roles for the Synechococcus elongatus RNA-Binding Protein Rbp2 in Regulating the Circadian Clock

The cyanobacterial circadian oscillator, consisting of KaiA, KaiB, and KaiC proteins, drives global rhythms of gene expression and compaction of the chromosome and regulates the timing of cell division and natural transformation. While the KaiABC posttranslational oscillator can be reconstituted in vitro, the Kai-based oscillator is subject to several layers of regulation in vivo. Specifically, the oscillator proteins undergo changes in their subcellular localization patterns, where KaiA and KaiC are diffuse throughout the cell during the day and localized as a focus at or near the pole of the cell at night. Here, we report that the CI domain of KaiC, when in a hexameric state, is sufficient to target KaiC to the pole. Moreover, increased ATPase activity of KaiC correlates with enhanced polar localization. We identified proteins associated with KaiC in either a localized or diffuse state. We found that loss of Rbp2, found to be associated with localized KaiC, results in decreased incidence of KaiC localization and long-period circadian phenotypes. Rbp2 is an RNA-binding protein, and it appears that RNA-binding activity of Rbp2 is required to execute clock functions. These findings uncover previously unrecognized roles for Rbp2 in regulating the circadian clock and suggest that the proper localization of KaiC is required for a fully functional clock in vivo.

A review from biological mapping to computation-based subcellular localization

Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.

Recognition of outer membrane proteins using multiple feature fusion

Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification. Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction. Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins.

Genome-wide identification, characterization and evolutionary dynamic of invertase gene family in apple, and revealing its roles in cold tolerance

Invertases are ubiquitous enzymes that catalyze the unalterable cleavage of sucrose into glucose and fructose, and are crucially involved in plant growth, development and stress response. In this study, a total of 17 putative invertase genes, including 3 cell wall invertases, 3 vacuolar invertases, and 11 neutral invertases were identified in apple genome. Subcellular localization of MdNINV7 and MdNINV11 indicated that both invertases were located in the cytoplasm. Comprehensive analyses of physicochemical properties, chromosomal localization, genomic characterization, and gene evolution of MdINV family were conducted. Gene duplication revealed that whole-genome or segmental duplication and random duplication might have been the major driving force for MdINVs expansion. Selection index values, ω, showed strong evidence of positive selection signatures among the INV clusters. Gene expression analysis indicated that MdNINV1/3/6/7 members are crucially involved in fruit development and sugar accumulation. Similarly, expression profiles of MdCWINV1, MdVINV1, and MdNINV1/2/7/11 suggested their potential roles in response to cold stress. Furthermore, overexpression of MdNINV11 in apple calli at least in part promoted the expression of MdCBF1-5 and H₂O₂ detoxification in response to cold. Overall, our results will be useful for understanding the functions of MdINVs in the regulation of apple fruit development and cold stress response.

Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development

Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates. In this chapter, we describe a deep learning-based technique which utilizes 18 biological and 9154 physicochemical properties of proteins for finding potential vaccine candidates. Using this technique, a new web-based system, named Vaxi-DL, was developed which helped in finding new vaccine candidates from bacteria, protozoa, viruses, and fungi. Vaxi-DL is available at: https://vac.kamalrawal.in/vaxidl/ .

Adaptations to high pressure of Nautilia sp. strain PV-1, a piezophilic Campylobacterium (aka Epsilonproteobacterium) isolated from a deep-sea hydrothermal vent

Physiological and gene expression studies of deep-sea bacteria under pressure conditions similar to those experienced in their natural habitat are critical for understanding growth kinetics and metabolic adaptations to in situ conditions. The Campylobacterium (aka Epsilonproteobacterium) Nautilia sp. strain PV-1 was isolated from hydrothermal fluids released from an active deep-sea hydrothermal vent at 9° N on the East Pacific Rise. Strain PV-1 is a piezophilic, moderately thermophilic, chemolithoautotrophic anaerobe that conserves energy by coupling the oxidation of hydrogen to the reduction of nitrate or elemental sulfur. Using a high-pressure-high temperature continuous culture system, we established that strain PV-1 has the shortest generation time of all known piezophilic bacteria and we investigated its protein expression pattern in response to different hydrostatic pressure regimes. Proteogenomic analyses of strain PV-1 grown at 20 and 5 MPa showed that pressure adaptation is not restricted to stress response or homeoviscous adaptation but extends to enzymes involved in central metabolic pathways. Protein synthesis, motility, transport, and energy metabolism are all affected by pressure, although to different extents. In strain PV-1, low-pressure conditions induce the synthesis of phage-related proteins and an overexpression of enzymes involved in carbon fixation.

Learning With Multiclass AUC: Theory and Algorithms

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically; (ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(√{1/N}) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework. The code is now available at https://github.com/joshuaas/Learning-with-Multiclass-AUC-Theory-and-Algorithms.

Manganese oxidation counteracts the deleterious effect of low temperatures on biofilm formation in Pseudomonas sp. MOB-449

Mn removal from groundwater by biological sand filter technology is negatively impacted by low temperatures in winter periods. Therefore, the need to study Mn(II)-oxidizing bacteria (MOB) having the potential to oxidize Mn(II) and form biofilms at low temperatures is imperative. These MOB can have potential as inocula for sand filter bioaugmentation strategies to optimize Mn removal during winter periods. We previously showed that a Pseudomonas sp. MOB-449 (MOB-449), isolated from a Mn biofilter, oxidizes Mn(II) in a biofilm-dependent way at low temperatures. In this work, MOB-449 Mn(II) oxidation and growth capacities were evaluated under planktonic and biofilm conditions at different temperatures. At 18°C, MOB-449 showed enhanced biofilm formation due to the addition of Mn(II) to the medium correlating with Mn(II) oxidation, compared to biofilms grown in control medium. Moreover, this enhancement on biofilm formation due to the addition of Mn(II) was only observed at 18°C. At this temperature, Mn(II) oxidation in membrane fractions collected from biofilms was induced by uncoupling oxidative phosphorylation from the electron transport chain with 2,4-Dinitrophenol. In Pseudomonas, a role for c-type cytochrome in Mn(II) oxidation has been demonstrated. Accordingly, transcriptional profiles of all terminal oxidases genes found in MOB-449 showed an induction of cytochrome c terminal oxidases expression mediated by Mn(II) oxidation at 18°C. Finally, heme peroxidase activity assays and MS analysis revealed that PetC, a cytochrome c5, and also CcmE, involved in the cytochrome c biogenesis machinery, are induced at 18°C only in the presence of Mn(II). These results present evidence supporting that cytochromes c and also the cytochrome c terminal oxidases are activated at low temperatures in the presence of Mn(II). Overall, this work demonstrate that in MOB-449 Mn(II) oxidation is activated at low temperatures to gain energy, suggesting that this process is important for survival under adverse environmental conditions and contributing to the understanding of the physiological role of bacterial Mn(II) oxidation.

Designing a novel chimeric multi-epitope vaccine against Burkholderia pseudomallei, a causative agent of melioidosis

Burkholderia pseudomallei, a gram-negative soil-dwelling bacterium, is primarily considered a causative agent of melioidosis infection in both animals and humans. Despite the severity of the disease, there is currently no licensed vaccine on the market. The development of an effective vaccine against B. pseudomallei could help prevent the spread of infection. The purpose of this study was to develop a multi-epitope-based vaccine against B. pseudomallei using advanced bacterial pan-genome analysis. A total of four proteins were prioritized for epitope prediction by using multiple subtractive proteomics filters. Following that, a multi-epitopes based chimeric vaccine construct was modeled and joined with an adjuvant to improve the potency of the designed vaccine construct. The structure of the construct was predicted and analyzed for flexibility. A population coverage analysis was performed to evaluate the broad-spectrum applicability of B. pseudomallei. The computed combined world population coverage was 99.74%. Molecular docking analysis was applied further to evaluate the binding efficacy of the designed vaccine construct with the human toll-like receptors-5 (TLR-5). Furthermore, the dynamic behavior and stability of the docked complexes were investigated using molecular dynamics simulation, and the binding free energy determined for Vaccine-TLR-5 was delta total -168.3588. The docking result revealed that the vaccine construct may elicit a suitable immunological response within the host body. Hence, we believe that the designed in-silico vaccine could be helpful for experimentalists in the formulation of a highly effective vaccine for B. pseudomallei.

Design of a Multi-Epitope Vaccine against Tropheryma whipplei Using Immunoinformatics and Molecular Dynamics Simulation Techniques

Whipple’s disease is caused by T. whipplei, a Gram-positive pathogenic bacterium. It is considered a persistent infection affecting various organs, more likely to infect males. There is currently no licensed vaccination available for Whipple’s disease; thus, the development of a chimeric peptide-based vaccine against T. whipplei has the potential to be tremendously beneficial in preventing Whipple’s disease in the future. The present study aimed to apply modern computational approaches to generate a multi-epitope-based vaccine that expresses antigenic determinants prioritized from the core proteome of two T. whipplei whole proteomes. Using an integrated computational approach, four immunodominant epitopes were found from two extracellular proteins. Combined, these epitopes covered 89.03% of the global population. The shortlisted epitopes exhibited a strong binding affinity for the B- and T-cell reference set of alleles, high antigenicity score, nonallergenic nature, high solubility, nontoxicity, and excellent binders of DRB1*0101. Through the use of appropriate linkers and adjuvation with a suitable adjuvant molecule, the epitopes were designed into a chimeric vaccine. An adjuvant was linked to the connected epitopes to boost immunogenicity and efficiently engage both innate and adaptive immunity. The physiochemical properties of the vaccine were observed favorable, leading toward the 3D modeling of the construct. Furthermore, the vaccine’s binding confirmation to the TLR-4 critical innate immune receptor was also determined using molecular docking and molecular dynamics (MD) simulations, which shows that the vaccine has a strong binding affinity for TLR4 (−29.4452 kcal/mol in MM-GBSA and −42.3229 kcal/mol in MM-PBSA). Overall, the vaccine described here has a promising potential for eliciting protective and targeted immunogenicity, subject to further experimental testing.

Investigating the Functional Role of Hypothetical Proteins From an Antarctic Bacterium Pseudomonas sp. Lz4W: Emphasis on Identifying Proteins Involved in Cold Adaptation

Exploring the molecular mechanisms behind bacterial adaptation to extreme temperatures has potential biotechnological applications. In the present study, Pseudomonas sp. Lz4W, a Gram-negative psychrophilic bacterium adapted to survive in Antarctica, was selected to decipher the molecular mechanism underlying the cold adaptation. Proteome analysis of the isolates grown at 4°C was performed to identify the proteins and pathways that are responsible for the adaptation. However, many proteins from the expressed proteome were found to be hypothetical proteins (HPs), whose function is unknown. Investigating the functional roles of these proteins may provide additional information in the biological understanding of the bacterial cold adaptation. Thus, our study aimed to assign functions to these HPs and understand their role at the molecular level. We used a structured insilico workflow combining different bioinformatics tools and databases for functional annotation. Pseudomonas sp. Lz4W genome (CP017432, version 1) contains 4493 genes and 4412 coding sequences (CDS), of which 743 CDS were annotated as HPs. Of these, from the proteome analysis, 61 HPs were found to be expressed consistently at the protein level. The amino acid sequences of these 61 HPs were submitted to our workflow and we could successfully assign a function to 18 HPs. Most of these proteins were predicted to be involved in biological mechanisms of cold adaptations such as peptidoglycan metabolism, cell wall organization, ATP hydrolysis, outer membrane fluidity, catalysis, and others. This study provided a better understanding of the functional significance of HPs in cold adaptation of Pseudomonas sp. Lz4W. Our approach emphasizes the importance of addressing the “hypothetical protein problem” for a thorough understanding of mechanisms at the cellular level, as well as, provided the assessment of integrating proteomics methods with various annotation and curation approaches to characterize hypothetical or uncharacterized protein data. The MS proteomics data generated from this study has been deposited to the ProteomeXchange through PRIDE with the dataset identifier–PXD029741.

Computational methods for protein localization prediction

The accurate annotation of protein localization is crucial in understanding protein function in tandem with a broad range of applications such as pathological analysis and drug design. Since most proteins do not have experimentally-determined localization information, the computational prediction of protein localization has been an active research area for more than two decades. In particular, recent machine-learning advancements have fueled the development of new methods in protein localization prediction. In this review paper, we first categorize the main features and algorithms used for protein localization prediction. Then, we summarize a list of protein localization prediction tools in terms of their coverage, characteristics, and accessibility to help users find suitable tools based on their needs. Next, we evaluate some of these tools on a benchmark dataset. Finally, we provide an outlook on the future exploration of protein localization methods.

Vaccine Design and Vaccination Strategies against Rickettsiae

Rickettsioses are febrile, potentially lethal infectious diseases that are a serious health threat, especially in poor income countries. The causative agents are small obligate intracellular bacteria, rickettsiae. Rickettsial infections are emerging worldwide with increasing incidence and geographic distribution. Nonetheless, these infections are clearly underdiagnosed because methods of diagnosis are still limited and often not available. Another problem is that the bacteria respond to only a few antibiotics, so delayed or wrong antibiotic treatment often leads to a more severe outcome of the disease. In addition to that, the development of antibiotic resistance is a serious threat because alternative antibiotics are missing. For these reasons, prophylactic vaccines against rickettsiae are urgently needed. In the past years, knowledge about protective immunity against rickettsiae and immunogenic determinants has been increasing and provides a basis for vaccine development against these bacterial pathogens. This review provides an overview of experimental vaccination approaches against rickettsial infections and perspectives on vaccination strategies.

Capabilities of bioinformatics tools for optimizing physicochemical features of proteins used in Nano biosensors: A short overview of the tools related to bioinformatics

Protein-protein ligand is one of the most detection methods used in Nano biosensors. Based on the advantage of specific docking between two special 3D structures, they have become a potent candidate in bioanalysis and Nanodiagnostic tools. These tools lease users to do a simple, fast, cost-effective, sensitive, and specific detection of molecular biomarkers in real samples. Recent advantages of using protein-protein ligand Nano-biosensors application is remarkable due to its special docking that refers to each protein unique 3D conformation. However, it challenges different problems such as low rate of docking and hard process for fixation on the basic layer. These challenges make developers to optimize the structure and functions of proteins. The process has different Nano scale calculation that could be done with algorithms and solutions are available as bioinformatics tools. This article aimed to have a short overview of the abilities of bioinformatics tools for modeling and optimization of physiochemical features of proteins in Nano scale.

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Nano biosensors use different strategies which based on docking between two molecules to detect and identify different proteins.

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Molecular docking between transducer in Nano biosensors and proteins rely on physicochemical features of transducer, protein and docking strategy.

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Nano bioinformatics use bioinformatics tools and algorithms as a collective solution for developing functional structure in Nano scale.

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Nano bioinformatics use different bioinformatics tools to optimize physicochemical features of proteins as a new approach in Nano biosensors and drug discovery.

In Silico Analysis of Potential Outer Membrane Beta-Barrel Proteins in Aeromonas hydrophila Pangenome

Outer membrane proteins (OMPs) of Aeromonas hydrophila have a variety of functional roles in virulence and pathogenesis and represent promising targets for vaccine development. The main objective of this study was to develop an in-silico model of beta-barrel OMP present among the valid A. hydrophila pangenomes (n = 22). With a program named the β-barrel Outer Membrane Protein Predictor (BOMP), total beta-barrel OMPs (n = 3127) were predicted across 22 genomes with the estimated median number of 64 per genome. In pangenome analysis, only 32 OMPs were found to be conserved. These beta-barrel OMPs also showed variations among source of isolation, COG and KEGG classes. Among 32 conserved OMPs, a highly antigenic protein was identified by utilizing Vaxijen. With B cell epitope predictions, two fragments of amino acid sequences i.e. GLTLGAQFTGNNDPQNADRSN (21 mer) and FKPSLAYLRTDVKDNARGI DDTATEY (26 mer) bearing B-cell binding sites were selected. Further, an epitope (12 amino acids: GLTLGAQFTGNN) that complexes to maximum MHC alleles with a higher antigenicity was determined. The analysis of evolutionary forces on the identified OMP sequence and epitope indicated that none of basic amino acid sites has shown significantly different substitution ratios. This conserved protein and epitope will be helpful in developing a vaccine that may be effective against all the A. hydrophila strains. Also, this study provides a theoretical basis for vaccine design against other pathogenic bacteria.

Discovery of fibrillar adhesins across bacterial species

BACKGROUND

Fibrillar adhesins are long multidomain proteins that form filamentous structures at the cell surface of bacteria. They are an important yet understudied class of proteins composed of adhesive and stalk domains that mediate interactions of bacteria with their environment. This study aims to characterize fibrillar adhesins in a wide range of bacterial phyla and to identify new fibrillar adhesin-like proteins to improve our understanding of host-bacteria interactions.

RESULTS

Through careful literature and computational searches, we identified 82 stalk and 27 adhesive domain families in fibrillar adhesins. Based on the presence of these domains in the UniProt Reference Proteomes database, we identified and analysed 3,542 fibrillar adhesin-like proteins across species of the most common bacterial phyla. We further enumerate the adhesive and stalk domain combinations found in nature and demonstrate that fibrillar adhesins have complex and variable domain architectures, which differ across species. By analysing the domain architecture of fibrillar adhesins, we show that in Gram positive bacteria, adhesive domains are mostly positioned at the N-terminus and cell surface anchors at the C-terminus of the protein, while their positions are more variable in Gram negative bacteria. We provide an open repository of fibrillar adhesin-like proteins and domains to enable further studies of this class of bacterial surface proteins.

CONCLUSION

This study provides a domain-based characterization of fibrillar adhesins and demonstrates that they are widely found in species across the main bacterial phyla. We have discovered numerous novel fibrillar adhesins and improved our understanding of pathogenic adhesion and invasion mechanisms.

Characterization and significance of extracellular polymeric substances, reactive oxygen species, and extracellular electron transfer in methanogenic biocathode

The microbial electrolysis cell assisted anaerobic digestion holds great promises over conventional anaerobic digestion. This article reports an experimental investigation of extracellular polymeric substances (EPS), reactive oxygen species (ROS), and the expression of genes associated with extracellular electron transfer (EET) in methanogenic biocathodes. The MEC-AD systems were examined using two cathode materials: carbon fibers and stainless-steel mesh. A higher abundance of hydrogenotrophic Methanobacterium sp. and homoacetogenic Acetobacterium sp. appeared to play a major role in superior methanogenesis from stainless steel biocathode than carbon fibers. Moreover, the higher secretion of EPS accompanied by the lower ROS level in stainless steel biocathode indicated that higher EPS perhaps protected cells from harsh metabolic conditions (possibly unfavorable local pH) induced by faster catalysis of hydrogen evolution reaction. In contrast, EET-associated gene expression patterns were comparable in both biocathodes. Thus, these results indicated hydrogenotrophic methanogenesis is the key mechanism, while cathodic EET has a trivial role in distinguishing performances between two cathode electrodes. These results provide new insights into the efficient methanogenic biocathode development.

Advanced strategies for development of vaccines against human bacterial pathogens

Infectious diseases are one of the main grounds of death and disabilities in human beings globally. Lack of effective treatment and immunization for many deadly infectious diseases and emerging drug resistance in pathogens underlines the need to either develop new vaccines or sufficiently improve the effectiveness of currently available drugs and vaccines. In this review, we discuss the application of advanced tools like bioinformatics, genomics, proteomics and associated techniques for a rational vaccine design.

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.

Bird Eye View of Protein Subcellular Localization Prediction

Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.

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.

HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network

Aims

To develop a tool that can annotate subcellular localization of human proteins.

Background

With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research.

Objective

To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision.

Methods

In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset.

Results

The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit).

Conclusion

In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.

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

Motivation

Many methods for microbial protein subcellular localization (SCL) prediction exist; however, none is readily available for analysis of metagenomic sequence data, despite growing interest from researchers studying microbial communities in humans, agri-food relevant organisms and in other environments (e.g. for identification of cell-surface biomarkers for rapid protein-based diagnostic tests). We wished to also identify new markers of water quality from freshwater samples collected from pristine versus pollution-impacted watersheds.

Results

We report PSORTm, the first bioinformatics tool designed for prediction of diverse bacterial and archaeal protein SCL from metagenomics data. PSORTm incorporates components of PSORTb, one of the most precise and widely used protein SCL predictors, with an automated classification by cell envelope. An evaluation using 5-fold cross-validation with in silico-fragmented sequences with known localization showed that PSORTm maintains PSORTb’s high precision, while sensitivity increases proportionately with metagenomic sequence fragment length. PSORTm’s read-based analysis was similar to PSORTb-based analysis of metagenome-assembled genomes (MAGs); however, the latter requires non-trivial manual classification of each MAG by cell envelope, and cannot make use of unassembled sequences. Analysis of the watershed samples revealed the importance of normalization and identified potential biomarkers of water quality. This method should be useful for examining a wide range of microbial communities, including human microbiomes, and other microbiomes of medical, environmental or industrial importance.

Availability and implementation

Documentation, source code and docker containers are available for running PSORTm locally at https://www.psort.org/psortm/ (freely available, open-source software under GNU General Public License Version 3).

Supplementary information

Supplementary data are available at Bioinformatics online.

Deciphering the functional role of hypothetical proteins from Chloroflexus aurantiacs J-10-f1 using bioinformatics approach

Chloroflexus aurantiacus J-10-f1 is an anoxygenic, photosynthetic, facultative autotrophic gram negative bacterium found from hot spring at a temperature range of 50-60°C. It can sustain itself in dark only if oxygen is available thereby exhibiting a dark orange color, however display a dark green color when grown in sunlight. Genome of the organism contains total of 3853 proteins out of which 785 (~20%) proteins are uncharacterised or hypothetical proteins (HPs). Therefore in this work we have characterized the 785 hypothetical proteins of Chloroflexus aurantiacus J-10-f1 using bioinformatics tools and databases. HPs annotated by more than five domain prediction tools were filtered and named high confidence-hypothetical proteins (HC-HPs). These HC-HPs were further annotated by calculating their physiochemical properties, homologous, subcellular locations, signal peptides and transmembrane regions. We found most of the HC-HPs were involved in photosynthesis, carbohydrate metabolism, biofuel production and cellulose synthesis processes. Furthermore, few of these HC-HPs could provide resistance to bacteria at high temperature due to their thermophilic nature. Hence these HC-HPs have the potential to be used in industrial as well as in biomedical needs. To conclude, the bioinformatics approach used in this study provides an insight to better understand the nature and role of Chloroflexus aurantiacus J-10-f1 hypothetical proteins.

Heterologous expression of a glycosyl hydrolase and cellular reprogramming enable Zymomonas mobilis growth on cellobiose

Plant-derived fuels and chemicals from renewable biomass have significant potential to replace reliance on petroleum and improve global carbon balance. However, plant biomass contains significant fractions of oligosaccharides that are not usable natively by many industrial microorganisms, including Escherichia coli, Saccharomyces cerevisiae, and Zymomonas mobilis. Even after chemical or enzymatic hydrolysis, some carbohydrate remains as non-metabolizable oligosaccharides (e.g., cellobiose or longer cellulose-derived oligomers), thus reducing the efficiency of conversion to useful products. To begin to address this problem for Z. mobilis, we engineered a strain (Z. mobilis GH3) that expresses a glycosyl hydrolase (GH) with β-glucosidase activity from a related α-proteobacterial species, Caulobacter crescentus, and subjected it to an adaptation in cellobiose medium. Growth on cellobiose was achieved after a prolonged lag phase in cellobiose medium that induced changes in gene expression and cell composition, including increased expression and extracellular release of GH. These changes were reversible upon growth in glucose-containing medium, meaning they did not result from genetic mutation but could be retained upon transfer of cells to fresh cellobiose medium. After adaptation to cellobiose, our GH-expressing strain was able to convert about 50% of cellobiose to glucose within 24 h and use it for growth and ethanol production. Alternatively, pre-growth of Z. mobilis GH3 in sucrose medium enabled immediate growth on cellobiose. Proteomic analysis of cellobiose- and sucrose-adapted strains revealed upregulation of secretion-, transport-, and outer membrane-related proteins, which may aid release or surface display of GHs, entry of cellobiose into the periplasm, or both. Our two key findings are that Z. mobilis can be reprogrammed to grow on cellobiose as a sole carbon source and that this reprogramming is related to a natural response of Z. mobilis to sucrose that promotes sucrase production.

Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key importance for protein function elucidation. However, it remains a challenging task, especially for multiple sites proteins known to shuttle between cell compartments to perform their proper biological functions and proteins which do not have significant homology to proteins of known subcellular locations. Due to their low-cost and reasonable accuracy, machine learning-based methods have gained much attention in this context with the availability of a plethora of biological databases and annotated proteins for analysis and benchmarking. Various predictive models have been proposed to tackle the SCL problem, using different protein sequence features pertaining to the subcellular localization, however, the overwhelming majority of them focuses on single localization and cover very limited cellular locations. The prediction was basically established on sorting signals, amino acids compositions, and homology. To improve the prediction quality, focus is actually on knowledge information extracted from annotation databases, such as protein-protein interactions and Gene Ontology (GO) functional domains annotation which has been recently a widely adopted and essential information for learning systems. To deal with such problem, in the present study, we considered SCL prediction task as a multi-label learning problem and tried to label both single site and multiple sites unannotated bacterial protein sequences by mining proteins homology relationships using both GO terms of protein homologs and PSI-BLAST profiles. The experiments using 5-fold cross-validation tests on the benchmark datasets showed a significant improvement on the results obtained by the proposed consensus multi-label prediction model which discriminates six compartments for Gram-negative and five compartments for Gram-positive bacterial proteins.

Identification of novel vaccine candidate against Salmonella enterica serovar Typhi by reverse vaccinology method and evaluation of its immunization

Salmonella enterica serovar Typhi (S. Typhi) is an essential enteric fever causing bacterium worldwide. Due to the emergence of multidrug-resistant strains, urgently attention is needed to prevent the global spread of them. Vaccination is an alternative approach to control these kinds of infections. Currently available commercial vaccines have significant limitations such as non-recommendation for children below six years of age and poor long-term efficacy. Thus, the development of a new vaccine overcoming these limitations is immediately required. Reverse Vaccinology (RV) is one of the most robust approaches for direct screening of genome sequence assemblies to identify new protein-based vaccines. The present study aimed to identify potential vaccine candidates against S. Typhi by using the RV approach. Immunogenicity of the best candidate against S. Typhi was further investigated. The proteome of S. Typhi strain Ty2 was analyzed to identify the most immunogenic, conserved, and protective surface proteins. Among the predicted vaccine candidates, steD (fimbrial subunit) was the best for qualifying all the applied criteria. The synthetic steD gene was expressed in E.coli, and the mice were immunized with purified recombinant steD protein and then challenged with a lethal dose of S. Typhi. Immunized animals generated high protein-specific antibody titers and demonstrated 70% survival following lethal dose challenge with S. Typhi. Pretreatment of the S. Typhi cells with immunized mice antisera significantly decreased their adhesion to Caco-2 cells. Altogether, steD as a protective antigen could induce a robust and long term and protective immunity in immunized mice against S. Typhi challenge.

Haemophilus influenzae HP1 Bacteriophage Encodes a Lytic Cassette with a Pinholin and a Signal-Arrest-Release Endolysin

HP1 is a temperate bacteriophage, belonging to the Myoviridae family and infecting Haemophilus influenzae Rd. By in silico analysis and molecular cloning, we characterized lys and hol gene products, present in the previously proposed lytic module of HP1 phage. The amino acid sequence of the lys gene product revealed the presence of signal-arrest-release (SAR) and muraminidase domains, characteristic for some endolysins. HP1 endolysin was able to induce lysis on its own when cloned and expressed in Escherichia coli, but the new phage release from infected H. influenzae cells was suppressed by inhibition of the secretion (sec) pathway. Protein encoded by hol gene is a transmembrane protein, with unusual C-out and N-in topology, when overexpressed/activated. Its overexpression in E. coli did not allow the formation of large pores (lack of leakage of β-galactosidase), but caused cell death (decrease in viable cell count) without lysis (turbidity remained constant). These data suggest that lys gene encodes a SAR-endolysin and that the hol gene product is a pinholin. HP1 SAR-endolysin is responsible for cell lysis and HP1 pinholin seems to regulate the cell lysis and the phage progeny release from H. influenzae cells, as new phage release from the natural host was inhibited by deletion of the hol gene.

In Silico Subtractive Proteomics Approach for Identification of Potential Drug Targets in Staphylococcus saprophyticus

Staphylococcus saprophyticus is a uropathogenic bacteria responsible for acute urinary tract infections (UTIs) mainly in young female patients. Patients suffering from urinary catheterization, pregnant patients, the elderly as well as those with nosocomial UTIs are at greater risk of the colonizing S. saprophyticus infection. The causative factors include benign prostatic hyperplasia, indwelling catheter, neurogenic bladder, pregnancy, and history of frequent UTIs. Recent findings have exhibited that S. saprophyticus is resistant to several antimicrobial agents. Moreover, there is a global concern regarding the increasing level of antimicrobial resistance, which leads to treatment failure and reduced effectiveness of broad-spectrum antimicrobials. Therefore, a novel approach is being utilized to combat resistant microbes since the past few years. Subtractive proteome analysis has been performed with the entire proteome of S. saprophyticus strain American Type Culture Collection (ATCC) 15305 using several bioinformatics servers and software. The proteins that were non-homologous to humans and bacteria were identified for metabolic pathway analysis. Only four cytoplasmic proteins were found possessing the potential of novel drug target candidates. The development of innovative therapeutic agents by targeting the inhibition of any essential proteins may disrupt the metabolic pathways specific to the pathogen, thus causing destruction as well as eradication of the pathogen from a particular host. The identified targets can facilitate in designing novel and potent drugs against S. saprophyticus strain ATCC 15305.

EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains

In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets.

Genome Investigation of a Cariogenic Pathogen with Implications in Cardiovascular Diseases

The proportion of people suffering from cardiovascular diseases has risen by 34% in the last 15 years in India. Cardiomyopathy is among the many forms of CVD s present. Infection of heart muscles is the suspected etiological agent for the same. Oral pathogens gaining entry into the bloodstream are responsible for such infections. Streptococcus mutans is an oral pathogen with implications in cardiovascular diseases. Previous studies have shown certain strains of S. mutans are found predominantly within atherosclerotic plaques and extirpated valves. To decipher the genetic differences responsible for endothelial cell invasion, we have sequenced the genome of Streptococcus mutans B14. Pan-genome analysis, search for adhesion proteins through a special algorithm, and protein-protein interactions search through HPIDB have been done. Pan-genome analysis of 187 whole genomes, assemblies revealed 6965 genes in total and 918 genes forming the core gene cluster. Adhesion to the endothelial cell is a critical virulence factor distinguishing virulent and non-virulent strains. Overall, 4% of the total proteins in S. mutans B14 were categorized as adhesion proteins. Protein-protein interaction between putative adhesion proteins and Human extracellular matrix components was predicted, revealing novel interactions. A conserved gene catalyzing the synthesis of branched-chain amino acids in S. mutans B14 shows possible interaction with isoforms of cathepsin protein of the ECM. This genome sequence analysis indicates towards other proteins in the S. mutans genome, which might have a specific role to play in host cell interaction.

Genetic Similarity of Gonococcal Homologs to Meningococcal Outer Membrane Proteins of Serogroup B Vaccine

The human pathogens Neisseria gonorrhoeae and Neisseria meningitidis share high genome identity. Retrospective analysis of surveillance data from New Zealand indicates the potential cross-protective effect of outer membrane vesicle (OMV) meningococcal serogroup B vaccine (MeNZB) against N. gonorrhoeae A licensed OMV-based MenB vaccine, MenB-4C, consists of a recombinant FHbp, NhbA, NadA, and the MeNZB OMV. Previous work has identified several abundantly expressed outer membrane proteins (OMPs) as major components of the MenB-4C OMV with high sequence similarity between N. gonorrhoeae and N. meningitidis, suggesting a mechanism for cross-protection. To build off these findings, we performed comparative genomic analysis on 970 recent N. gonorrhoeae isolates collected through a U.S surveillance system against N. meningitidis serogroup B (NmB) reference sequences. We identified 1,525 proteins that were common to both Neisseria species, of which 57 proteins were predicted to be OMPs using in silico methods. Among the MenB-4C antigens, NhbA showed moderate sequence identity (73%) to the respective gonococcal homolog, was highly conserved within N. gonorrhoeae, and was predicted to be surface expressed. In contrast, the gonococcal FHbp was predicted not to be surface expressed, while NadA was absent in all N. gonorrhoeae isolates. Our work confirmed recent observations (E. A. Semchenko, A. Tan, R. Borrow, and K. L. Seib, Clin Infect Dis, 2018, https://doi.org/10.1093/cid/ciy1061) and describes homologous OMPs from a large panel of epidemiologically relevant N. gonorrhoeae strains in the United States against NmB reference strains. Based on our results, we report a set of OMPs that may contribute to the previously observed cross-protection and provide potential antigen targets to guide the next steps in gonorrhea vaccine development.IMPORTANCE Gonorrhea, a sexually transmitted disease, causes substantial global morbidity and economic burden. New prevention and control measures for this disease are urgently needed, as strains resistant to almost all classes of antibiotics available for treatment have emerged. Previous reports demonstrate that cross-protection from gonococcal infections may be conferred by meningococcal serogroup B (MenB) outer membrane vesicle (OMV)-based vaccines. Among 1,525 common proteins shared across the genomes of both N. gonorrhoeae and N. meningitidis, 57 proteins were predicted to be surface expressed (outer membrane proteins [OMPs]) and thus preferred targets for vaccine development. The majority of these OMPs showed high sequence identity between the 2 bacterial species. Our results provide valuable insight into the meningococcal antigens present in the current OMV-containing MenB-4C vaccine that may contribute to cross-protection against gonorrhea and may inform next steps in gonorrhea vaccine development.

pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset

Background/Objective:

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as “multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.

Methods:

Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization of multi-label virus proteins.

Results:

Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.

Conclusion:

Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.

A Brief History of Protein Sorting Prediction

Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

Entry Exclusion of Conjugative Plasmids of the IncA, IncC, and Related Untyped Incompatibility Groups

Conjugative plasmids of incompatibility group C (IncC), formerly known as A/C₂, disseminate antibiotic resistance genes globally in diverse pathogenic species of Gammaproteobacteria. Salmonella genomic island 1 (SGI1) can be mobilized by IncC plasmids and was recently shown to reshape the conjugative type IV secretion system (T4SS) encoded by these plasmids to evade entry exclusion. Entry exclusion blocks DNA translocation between cells containing identical or highly similar plasmids. Here, we report that the protein encoded by the entry exclusion gene of IncC plasmids (eexC) mediates entry exclusion in recipient cells through recognition of the IncC-encoded TraG_C protein in donor cells. Phylogenetic analyses based on EexC and TraG_C homologs predicted the existence of at least three different exclusion groups among IncC-related conjugative plasmids. Mating assays using Eex proteins encoded by representative IncC and IncA (former A/C₁) and related untyped plasmids confirmed these predictions and showed that the IncC and IncA plasmids belong to the C exclusion group, thereby explaining their apparent incompatibility despite their compatible replicons. Representatives of the two other exclusion groups (D and E) are untyped conjugative plasmids found in Aeromonas sp. Finally, we determined through domain swapping that the carboxyl terminus of the EexC and EexE proteins controls the specificity of these exclusion groups. Together, these results unravel the role of entry exclusion in the apparent incompatibility between IncA and IncC plasmids while shedding light on the importance of the TraG subunit substitution used by SGI1 to evade entry exclusion.IMPORTANCE IncA and IncC conjugative plasmids drive antibiotic resistance dissemination among several pathogenic species of Gammaproteobacteria due to the diversity of drug resistance genes that they carry and their ability to mobilize antibiotic resistance-conferring genomic islands such as SGI1 of Salmonella enterica While historically grouped as "IncA/C," IncA and IncC replicons were recently confirmed to be compatible and to abolish each other's entry into the cell in which they reside during conjugative transfer. The significance of our study is in identifying an entry exclusion system that is shared by IncA and IncC plasmids. It impedes DNA transfer to recipient cells bearing a plasmid of either incompatibility group. The entry exclusion protein of this system is unrelated to any other known entry exclusion proteins.

Nucleotide Second Messenger-Based Signaling in Extreme Acidophiles of the Acidithiobacillus Species Complex: Partition Between the Core and Variable Gene Complements

Cyclic and linear nucleotides are key elements of the signal transduction networks linking perception of the environment to specific cellular behavior of prokaryotes. These molecular mechanisms are particularly important in bacteria exposed to different, and frequently simultaneous, types of extreme conditions. This is the case in acidithiobacilli, a group of extremophilic bacteria thriving in highly acidic biotopes, that must also cope with significant variations in temperature, osmotic potentials and concentrations of various transition metals and metalloids. Environmental cues sensed by bacteria are transduced into differential levels of nucleotides acting as intracellular second messengers, promoting the activation or inhibition of target components and eliciting different output phenotypes. Cyclic (c) di-GMP, one of the most common bacterial second messengers, plays a key role in lifestyle changes in many bacteria, including acidithiobacilli. The presence of functional c-di-GMP-dependent signal transduction pathways in representative strains of the best-known linages of this species complex has been reported. However, a comprehensive panorama of the c-di-GMP modulated networks, the cognate input signals and output responses, are still missing for this group of extremophiles. Moreover, little fundamental understanding has been gathered for other nucleotides acting as second messengers. Taking advantage of the increasing number of sequenced genomes of the taxon, here we address the challenge of disentangling the nucleotide-driven signal transduction pathways in this group of polyextremophiles using comparative genomic tools and strategies. Results indicate that the acidithiobacilli possess all the genetic elements required to establish functional transduction pathways based in three different nucleotide-second messengers: (p)ppGpp, cyclic AMP (cAMP), and c-di-GMP. The elements related with the metabolism and transduction of (p)ppGpp and cAMP appear highly conserved, integrating signals related with nutrient starvation and polyphosphate metabolism, respectively. In contrast, c-di-GMP networks appear diverse and complex, differing both at the species and strain levels. Molecular elements of c-di-GMP metabolism and transduction were mostly found scattered along the flexible genome of the acidithiobacilli, allowing the identification of probable control modules that could be critical for substrate colonization, biofilm development and intercellular interactions. These may ultimately convey increased endurance to environmental stress and increased potential for gene sharing and adaptation to changing conditions.

Biofilm development and computational screening for new putative inhibitors of a homolog of the regulatory protein BrpA in Streptococcus dysgalactiae subsp. dysgalactiae

Streptococcus dysgalactiae subsp. dysgalactiae (SDSD), a Lancefield group C streptococci (GCS), is a frequent cause of bovine mastitis. This highly prevalent disease is the costliest in dairy industry. Adherence and biofilm production are important factors in streptoccocal pathogenesis. We have previously described the adhesion and internalization of SDSD isolates in human cells and now we describe the biofilm production capability of this bacterium. In this work we integrated microbiology, imaging and computational methods to evaluate the biofilm production capability of SDSD isolates; to assess the presence of biofilm regulatory protein BrpA homolog in the biofilm producers; and to predict a structural model of BrpA-like protein and its binding to putative inhibitors. Our results show that SDSD isolates form biofilms on abiotic surface such as glass (hydrophilic) and polystyrene (hydrophobic), with the strongest biofilm formation observed in glass. This ability was mainly associated with a proteinaceous extracellular matrix, confirmed by the dispersion of the biofilms after proteinase K and trypsin treatment. The biofilm formation in SDSD isolates was also confirmed by confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM). Under SEM observation, VSD16 isolate formed cell aggregates during biofilm growth while VSD9 and VSD10 formed smooth and filmy layers. We show that brpA-like gene is present and expressed in SDSD biofilm-producing isolates and its expression levels correlated with the biofilm production capability, being more expressed in the late exponential phase of planktonic growth compared to biofilm growth. Fisetin, a known biofilm inhibitor and a putative BrpA binding molecule, dramatically inhibited biofilm formation by the SDSD isolates but did not affect planktonic growth, at the tested concentrations. Homology modeling was used to predict the 3D structure of BrpA-like protein. Using high throughput virtual screening and molecular docking, we selected five ligand molecules with strong binding affinity to the hydrophobic cleft of the protein, making them potential inhibitor candidates of the SDSD BrpA-like protein. These results warrant further investigations for developing novel strategies for SDSD anti-biofilm therapy.