Structure and function predictions of the Msa protein in Staphylococcus aureus

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.

Characterization of putative proteins encoded by variable ORFs in white spot syndrome virus genome

Background

White Spot Syndrome Virus (WSSV) is an enveloped double-stranded DNA virus which causes mortality of several species of shrimp, being considered one of the main pathogens that affects global shrimp farming. This virus presents a complex genome of ~ 300 kb and viral isolates that present genomes with great identity. Despite this conservation, some variable regions in the WSSV genome occur in coding regions, and these putative proteins may have some relationship with viral adaptation and virulence mechanisms. Until now, the functions of these proteins were little studied. In this work, sequences and putative proteins encoded by WSSV variable regions were characterized in silico.

Results

The in silico approach enabled determining the variability of some sequences, as well as the identification of some domains resembling the Formin homology 2, RNA recognition motif, Xeroderma pigmentosum group D repair helicase, Hemagglutinin and Ankyrin motif. The information obtained from the sequences and the analysis of secondary and tertiary structure models allow to infer that some of these proteins possibly have functions related to protein modulation/degradation, intracellular transport, recombination and endosome fusion events.

Conclusions

The bioinformatics approaches were efficient in generating three-dimensional models and to identify domains, thereby enabling to propose possible functions for the putative polypeptides produced by the ORFs wsv129, wsv178, wsv249, wsv463a, wsv477, wsv479, wsv492, and wsv497.

Electronic supplementary material

The online version of this article (10.1186/s12900-019-0106-y) contains supplementary material, which is available to authorized users.

Tertiary structure prediction and identification of druggable pocket in the cancer biomarker - Osteopontin-c

Background

Osteopontin (Eta, secreted sialoprotein 1, opn) is secreted from different cell types including cancer cells. Three splice variant forms namely osteopontin-a, osteopontin-b and osteopontin-c have been identified. The main astonishing feature is that osteopontin-c is found to be elevated in almost all types of cancer cells. This was the vital point to consider it for sequence analysis and structure predictions which provide ample chances for prognostic, therapeutic and preventive cancer research.

Methods

Osteopontin-c gene sequence was determined from Breast Cancer sample and was translated to protein sequence. It was then analyzed using various software and web tools for binding pockets, docking and druggability analysis. Due to the lack of homological templates, tertiary structure was predicted using ab-initio method server – I-TASSER and was evaluated after refinement using web tools. Refined structure was compared with known bone sialoprotein electron microscopic structure and docked with CD44 for binding analysis and binding pockets were identified for drug designing.

Results

Signal sequence of about sixteen amino acid residues was identified using signal sequence prediction servers. Due to the absence of known structures of similar proteins, three dimensional structure of osteopontin-c was predicted using I-TASSER server. The predicted structure was refined with the help of SUMMA server and was validated using SAVES server. Molecular dynamic analysis was carried out using GROMACS software. The final model was built and was used for docking with CD44. Druggable pockets were identified using pocket energies.

Conclusions

The tertiary structure of osteopontin-c was predicted successfully using the ab-initio method and the predictions showed that osteopontin-c is of fibrous nature comparable to firbronectin. Docking studies showed the significant similarities of QSAET motif in the interaction of CD44 and osteopontins between the normal and splice variant forms of osteopontins and binding pockets analyses revealed several pockets which paved the way to the identification of a druggable pocket.

Protein function prediction--the power of multiplicity

Advances in experimental and computational methods have quietly ushered in a new era in protein function annotation. This 'age of multiplicity' is marked by the notion that only the use of multiple tools, multiple evidence and considering the multiple aspects of function can give us the broad picture that 21st century biology will need to link and alter micro- and macroscopic phenotypes. It might also help us to undo past mistakes by removing errors from our databases and prevent us from producing more. On the downside, multiplicity is often confusing. We therefore systematically review methods and resources for automated protein function prediction, looking at individual (biochemical) and contextual (network) functions, respectively.

The Role of msa in Staphylococcus aureus Biofilm Formation

BACKGROUND

Staphylococcus aureus is an important pathogen that forms biofilms. The global regulator sarA is essential for biofilm formation. Since the modulator of sarA (msa) is required for full expression of sarA and regulates several virulence factors, we examined the capacity of the msa mutant to form biofilm.

RESULTS

We found that mutation of msa results in reduced expression of sarA in biofilm and that the msa mutant formed a weak and unstable biofilm. The msa mutant is able to adhere to surfaces and begins to form biofilm but fails to mature indicating that the defect of the msa mutant biofilm is in the accumulation stage but not in primary adhesion.

CONCLUSION

The msa gene plays an important role in biofilm development which is likely due to its role in modulating the expression of sarA. This finding is significant because it identifies a new gene that plays a role in the development of biofilm.