Predictive characterization of hypothetical proteins in Staphylococcus aureus NCTC 8325

In silico analysis of a novel hypothetical protein (YP_498675.1) from Staphylococcus aureus unravels the protein of tryptophan synthase beta superfamily (Try-synth-beta_ II)

BACKGROUND

Staphylococcus aureus is a gram-positive spherical bacteria and the most common cause of nosocomial infections in the world. Given its clinical significance, the genome sequence of S. aureus has been elucidated to enhance our comprehension of its lifestyle and pathogenicity. The research aimed to summarize a potential hypothetical protein that may play an important role in S. aureus virulence and pathogenicity, covering its anticipated structure, probable biological functions, and importance in this context.

RESULTS

A hypothetical protein, YP_498675.1 with 281 amino acid residues of S. aureus, was chosen for analysis and modeling by several bioinformatics tools and databases in this work. According to primary and secondary structure analyses, YP_498675.1 is a stable hydrophilic protein with a significant proportion of α-helices. Subcellular localization predictions by CELLO, PSORTb, and SOSUI server indicate that it is a cytoplasmic protein. NCBI-CDD, Pfam, and InterProScan functional genomics research revealed that the hypothetical protein may include the pyridoxal phosphate (PLP)-dependent 2, 3-diaminopropionate biosynthesis protein SbnA domain. In the homology modeling method, the HHpred server was employed to create its 3D structure using the template structure of a Staphyloferrin B precursor biosynthetic enzyme SbnA bound to PLP (PDB ID: 5D84_A), an X-ray diffraction model having 100% sequence identity with the hypothetical protein. After energy minimization, several quality assessments and validation factors determined that the generated protein model was reliable and of reasonable quality.

CONCLUSION

The present study has characterized and functionally annotated the hypothetical protein YP_498675.1 of S. aureus. Further experimental validation would aid in determining the actual function of YP_498675.1 as well as confirm the protein's value as a therapeutic target.

Draft genome sequence of Staphylococcus aureus sequence type 5 SA01 isolated from bloodstream infection and comparative analysis with reference strains

A Staphylococcus aureus isolate (SA01) obtained from bloodstream infection exhibited a remarkable drug resistance profile. In this study, we report the draft genome sequence of S. aureus ST 5 SA01, a multidrug-resistant isolate, and analyzed the genes associated with drug resistance and virulence. The genome sketch of S. aureus ST5 SA01 was sequenced with Illumina and annotated using the Prokka software. Rapid Annotation Subsystem Technology (RAST) was used to verify the gene functions in the genome subsystems. The Comprehensive Antibiotic Resistance Database (CARD) and Virulence Factor Database (VFDB) were used in the analysis. The RAST indicated a contribution of 25 proteins to host adenine, fibronectin-binding protein A (FnbA), and biofilm formation as an intercellular polysaccharide adhesive system (PIA). The MLST indicated that S. aureus ST 5 SA01 belongs to ST5 (CC5). In silico analyses also showed an extensive repertoire of genes associated with toxins, such as LukGH leukocidin, enterotoxins, and superantigen staphylococcal classes (SSL). The 11 genes for antimicrobial resistance in S. aureus ST 5 SA01 showed similarity and identity above ≥ 99% with nucleotide sequences deposited in GenBank. Although studies on ST5 clones in Brazil are scarce, monitoring the clone of S. aureus ST 5 SA01 is essential, as it has become a problem in pediatrics in several countries.

Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative "Superbugs": Six Protein Clusters Amenable for Distinct Scientific Applications

Uncharacterized proteins have been underutilized as targets for the development of novel therapeutics for difficult-to-treat bacterial infections. To facilitate the exploration of these proteins, 2819 predicted, uncharacterized proteins (19.1% of the total) from reference strains of multidrug Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa species were organized using an unsupervised k-means machine learning algorithm. Classification using normalized values for protein length, pI, hydrophobicity, degree of conservation, structural disorder, and %AT of the coding gene rendered six natural clusters. Cluster proteins showed different trends regarding operon membership, expression, presence of unknown function domains, and interactomic relevance. Clusters 2, 4, and 5 were enriched with highly disordered proteins, nonworkable membrane proteins, and likely spurious proteins, respectively. Clusters 1, 3, and 6 showed closer distances to known antigens, antibiotic targets, and virulence factors. Up to 21.8% of proteins in these clusters were structurally covered by modeling, which allowed assessment of druggability and discontinuous B-cell epitopes. Five proteins (4 in Cluster 1) were potential druggable targets for antibiotherapy. Eighteen proteins (11 in Cluster 6) were strong B-cell and T-cell immunogen candidates for vaccine development. Conclusively, we provide a feature-based schema to fractionate the functional dark proteome of critical pathogens for fundamental and biomedical purposes.

In silico functional and structural characterization revealed virulent proteins of Francisella tularensis strain SCHU4

Francisella tularensis is a pathogenic, aerobic gram-negative coccobacillus bacterium. It is the causative agent of tularemia, a rare infectious disease that can attack skin, lungs, eyes, and lymph nodes. The genome of F. tularensis has been sequenced, and ~16% of the proteome is still uncharacterized. Characterizations of these proteins are essential to find new drug targets for better therapeutics. In silico characterization of proteins has become an extremely important approach to determine the functionality of proteins as experimental functional elucidation is unable to keep pace with the current growth of the sequence database. Initially, we have annotated 577 Hypothetical Proteins (HPs) of F. tularensis strain SCHU4 with seven bioinformatics tools which characterized them based on the family, domain and motif. Out of 577 HPs, 119 HPs were annotated by five or more tools and are further screened to predict their virulence properties, subcellular localization, transmembrane helices as well as physicochemical parameters. VirulentPred predicted 66 HPs out of 119 as virulent. These virulent proteins were annotated to find the interacting partner using STRING, and proteins with high confidence interaction scores were used to predict their 3D structures using Phyre2. The three virulent proteins Q5NH99 (phosphoserine phosphatase), Q5NG42 (Cystathionine beta-synthase) and Q5NG83 (Rrf2-type helix turn helix domain) were predicted to involve in modulation of cytoskeletal and innate immunity of host, H2S (hydrogen sulfide) based antibiotic tolerance and nitrite and iron metabolism of bacteria. The above predicted virulent proteins can serve as novel drug targets in the era of antibiotic resistance.

A novel gene from the acidophilic bacterium Leptospirillum sp. CF-1 and its role in oxidative stress and chromate tolerance

Background

Acidophilic microorganisms like Leptospirillum sp. CF-1 thrive in environments with extremely low pH and high concentrations of dissolved heavy metals that can induce the generation of reactive oxygen species (ROS). Several hypothetical genes and proteins from Leptospirillum sp. CF-1 are known to be up-regulated under oxidative stress conditions.

Results

In the present work, the function of hypothetical gene ABH19_09590 from Leptospirillum sp. CF-1 was studied. Heterologous expression of this gene in Escherichia coli led to an increase in the ability to grow under oxidant conditions with 5 mM K₂CrO₄ or 5 mM H₂O₂. Similarly, a significant reduction in ROS production in E. coli transformed with a plasmid carrying ABH19_09590 was observed after exposure to these oxidative stress elicitors for 30 min, compared to a strain complemented with the empty vector. A co-transcriptional study using RT-PCR showed that ABH19_09590 is contained in an operon, here named the “och” operon, that also contains ABH19_09585, ABH19_09595 and ABH19_09600 genes. The expression of the och operon was significantly up-regulated in Leptospirillum sp. CF-1 exposed to 5 mM K₂CrO₄ for 15 and 30 min. Genes of this operon potentially encode a NADH:ubiquinone oxidoreductase, a CXXC motif-containing protein likely involved in thiol/disulfide exchange, a hypothetical protein, and a di-hydroxy-acid dehydratase. A comparative genomic analysis revealed that the och operon is a characteristic genetic determinant of the Leptospirillum genus that is not present in other acidophiles.

Conclusions

Altogether, these results suggest that the och operon plays a protective role against chromate and hydrogen peroxide and is an important mechanism required to face polyextremophilic conditions in acid environments.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

The Virulent Hypothetical Proteins: The Potential Drug Target Involved in Bacterial Pathogenesis

Abstract:

Hypothetical proteins (HPs) are non-predicted sequences that are identified only by open reading frames in sequenced genomes but their protein products remain uncharacterized by any experimental means. The genome of every species consists of HPs that are involved in various cellular processes and signaling pathways. Annotation of HPs is important as they play a key role in disease mechanisms, drug designing, vaccine production, antibiotic production, and host adaptation. In the case of bacteria, 25-50% of the genome comprises of HPs, which are involved in metabolic pathways and pathogenesis. The characterization of bacterial HPs helps to identify virulent proteins that are involved in pathogenesis. This can be done using in-silico studies, which provide sequence analogs, physiochemical properties, cellular or subcellular localization, structure and function validation, and protein-protein interactions. The most diverse types of virulent proteins are exotoxins, endotoxins, and adherent virulent factors that are encoded by virulent genes present on the chromosomal DNA of the bacteria. This review evaluates virulent HPs of pathogenic bacteria, such as Staphylococcus aureus, Chlamydia trachomatis, Fusobacterium nucleatum, and Yersinia pestis. The potential of these HPs as a drug target in bacteria-caused infectious diseases along with the mode of action and treatment approaches have been discussed.

Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs

Background

Severe Acute Respiratory Syndrome (SARS) corona virus (CoV) infections are a serious public health threat because of their pandemic-causing potential. This work is the first to analyze mRNA expression data from SARS infections through meta-analysis of gene signatures, possibly identifying therapeutic targets associated with major SARS infections.

Methods

This work defines 37 gene signatures representing SARS-CoV, Middle East Respiratory Syndrome (MERS)-CoV, and SARS-CoV2 infections in human lung cultures and/or mouse lung cultures or samples and compares them through Gene Set Enrichment Analysis (GSEA). To do this, positive and negative infectious clone SARS (icSARS) gene panels are defined from GSEA-identified leading-edge genes between two icSARS-CoV derived signatures, both from human cultures. GSEA then is used to assess enrichment and identify leading-edge icSARS panel genes between icSARS gene panels and 27 other SARS-CoV gene signatures. The meta-analysis is expanded to include five MERS-CoV and three SARS-CoV2 gene signatures. Genes associated with SARS infection are predicted by examining the intersecting membership of GSEA-identified leading-edges across gene signatures.

Results

Significant enrichment (GSEA p<0.001) is observed between two icSARS-CoV derived signatures, and those leading-edge genes defined the positive (233 genes) and negative (114 genes) icSARS panels. Non-random significant enrichment (null distribution p<0.001) is observed between icSARS panels and all verification icSARSvsmock signatures derived from human cultures, from which 51 over- and 22 under-expressed genes are shared across leading-edges with 10 over-expressed genes already associated with icSARS infection. For the icSARSvsmock mouse signature, significant, non-random significant enrichment held for only the positive icSARS panel, from which nine genes are shared with icSARS infection in human cultures. Considering other SARS strains, significant, non-random enrichment (p<0.05) is observed across signatures derived from other SARS strains for the positive icSARS panel. Five positive icSARS panel genes, CXCL10, OAS3, OASL, IFIT3, and XAF1, are found across mice and human signatures regardless of SARS strains.

Conclusion

The GSEA-based meta-analysis approach used here identifies genes with and without reported associations with SARS-CoV infections, highlighting this approach’s predictability and usefulness in identifying genes that have potential as therapeutic targets to preclude or overcome SARS infections.

An Educational Bioinformatics Project to Improve Genome Annotation

Scientific advancement is hindered without proper genome annotation because biologists lack a complete understanding of cellular protein functions. In bacterial cells, hypothetical proteins (HPs) are open reading frames with unknown functions. HPs result from either an outdated database or insufficient experimental evidence (i.e., indeterminate annotation). While automated annotation reviews help keep genome annotation up to date, often manual reviews are needed to verify proper annotation. Students can provide the manual review necessary to improve genome annotation. This paper outlines an innovative classroom project that determines if HPs have outdated or indeterminate annotation. The Hypothetical Protein Characterization Project uses multiple well-documented, freely available, web-based, bioinformatics resources that analyze an amino acid sequence to (1) detect sequence similarities to other proteins, (2) identify domains, (3) predict tertiary structure including active site characterization and potential binding ligands, and (4) determine cellular location. Enough evidence can be generated from these analyses to support re-annotation of HPs or prioritize HPs for experimental examinations such as structural determination via X-ray crystallography. Additionally, this paper details several approaches for selecting HPs to characterize using the Hypothetical Protein Characterization Project. These approaches include student- and instructor-directed random selection, selection using differential gene expression from mRNA expression data, and selection based on phylogenetic relations. This paper also provides additional resources to support instructional use of the Hypothetical Protein Characterization Project, such as example assignment instructions with grading rubrics, links to training videos in YouTube, and several step-by-step example projects to demonstrate and interpret the range of achievable results that students might encounter. Educational use of the Hypothetical Protein Characterization Project provides students with an opportunity to learn and apply knowledge of bioinformatic programs to address scientific questions. The project is highly customizable in that HP selection and analysis can be specifically formulated based on the scope and purpose of each student's investigations. Programs used for HP analysis can be easily adapted to course learning objectives. The project can be used in both online and in-seat instruction for a wide variety of undergraduate and graduate classes as well as undergraduate capstone, honor's, and experiential learning projects.

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.

Identification and prioritization of macrolideresistance genes with hypothetical annotation inStreptococcus pneumoniae

Macrolide resistant Streptococcus pneumoniae infections have limited treatment options. While some resistance mechanisms are well established, ample understanding is limited by incomplete genome annotation (hypothetical genes). Some hypothetical genes encode a domain of unknown function (DUF), a conserved protein domain with uncharacterized function. Here, we identify and confirm macrolide resistance genes. We further explore DUFs from macrolide resistance hypothetical genes to prioritize them for experimental characterization. We found gene similarities between two macrolide resistance gene signatures from untreated and either erythromycin- or spiramycin-treated resistant Streptococcus pneumoniae. We confirmed the association of these gene sets with macrolide resistance through comparison to gene signatures from (i) second erythromycin resistant Streptococcus pneumoniae strain, and (ii) erythromycin-treated sensitive Streptococcus pneumoniae strain, both from non-overlapping datasets. Examination into which cellular processes these macrolide resistance genes belong found connections to known resistance mechanisms such as increased amino acid biosynthesis and efflux genes, and decreased ribonucleotide biosynthesis genes, highlighting the predictive ability of the method used. 22 genes had hypothetical annotation with 10 DUFs associated with macrolide resistance. DUF characterization could uncover novel co-therapies that restore macrolide efficacy across multiple macrolide resistant species. Application of the methods to other antibiotic resistances could revolutionize treatment of resistant infections

Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance

Exiguobacterium antarcticum strain B7 is a psychrophilic Gram-positive bacterium that possesses enzymes that can be used for several biotechnological applications. However, many proteins from its genome are considered hypothetical proteins (HPs). These functionally unknown proteins may indicate important functions regarding the biological role of this bacterium, and the use of bioinformatics tools can assist in the biological understanding of this organism through functional annotation analysis. Thus, our study aimed to assign functions to proteins previously described as HPs, present in the genome of E. antarcticum B7. We used an extensive in silico workflow combining several bioinformatics tools for function annotation, sub-cellular localization and physicochemical characterization, three-dimensional structure determination, and protein-protein interactions. This genome contains 2772 genes, of which 765 CDS were annotated as HPs. The amino acid sequences of all HPs were submitted to our workflow and we successfully attributed function to 132 HPs. We identified 11 proteins that play important roles in the mechanisms of adaptation to adverse environments, such as flagellar biosynthesis, biofilm formation, carotenoids biosynthesis, and others. In addition, three predicted HPs are possibly related to arsenic tolerance. Through an in vitro assay, we verified that E. antarcticum B7 can grow at high concentrations of this metal. The approach used was important to precisely assign function to proteins from diverse classes and to infer relationships with proteins with functions already described in the literature. This approach aims to produce a better understanding of the mechanism by which this bacterium adapts to extreme environments and to the finding of targets with biotechnological interest.

Structural and functional annotation of hypothetical proteins of human adenovirus: prioritizing the novel drug targets

OBJECTIVE

Human adenoviruses are small double stranded DNA viruses that provoke vast array of human diseases. Next generation sequencing techniques increase genomic data of HAdV rapidly, which increase their serotypes. The complete genome sequence of human adenovirus shows that it contains large amount of proteins with unknown cellular or biochemical function, known as hypothetical proteins. Hence, it is indispensable to functionally and structurally annotate these proteins to get better understanding of the novel drug targets. The purpose was the characterization of 38 randomly retrieved hypothetical proteins through determination of their physiochemical properties, subcellular localization, function, structure and ligand binding sites using various sequence and structure based bioinformatics tools.

RESULTS

Function of six hypothetical proteins P03269, P03261, P03263, Q83127, Q1L4D7 and I6LEV1 were predicted confidently and then used further for structure analysis. We found that these proteins may act as DNA terminal protein, DNA polymerase, DNA binding protein, adenovirus E3 region protein CR1 and adenoviral protein L1. Functional and structural annotation leading to detection of binding sites by means of docking analysis can indicate potential target for therapeutics to defeat adenoviral infection.

Improved Annotations of 23 Differentially Expressed Hypothetical Proteins in Methicillin Resistant S.aureus

Antibiotic resistant Staphylococcus aureus is a major public health concern effecting millions of people annually. Medical science has documented completely untreatable S. aureus infections. These strains are appearing in the community with increasing frequency. New diagnostic and therapeutic options are needed to combat this deadly infection. Interestingly, around 50% of the proteins in S. aureus are annotated as hypothetical. Methods to select hypothetical proteins related to antibiotic resistance have been inadequate. This study uses differential gene expression to identify hypothetical proteins related to antibiotic resistant phenotype strain variations. We apply computational tools to predict physiochemical properties, cellular location, sequence-based homologs, domains, 3D modeling, active site features, and binding partners. Nine of 23 hypothetical proteins were <100 residues, unlikely to be functional proteins based on size. Of the 14 differentially expressed hypothetical proteins examined, confident predictions on function could not be made. Most identified domains had unknown functions. Six hypothetical protein models had >50% confidence over >20% residues. These findings indicate the method of hypothetical protein identification is sufficient; however, current scientific knowledge is inadequate to properly annotate these proteins. This process should be repeated regularly until entire genomes are clearly and accurately annotated.