Secret-AAR: a web server to assess the antigenic density of proteins and homology search against bacterial and parasite secretome proteins

Cornejo‐Granados, Fernanda; Hurtado-Ramírez, Juan Manuel; Hernández-Pando, Rogelio; Ochoa-Leyva, Adrián

doi:10.1016/j.ygeno.2018.10.007

Cited by 11 publications

(10 citation statements)

References 17 publications

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“…Additionally, the abundance of antigenic regions (AAR), which evaluates antigenic density, found differences that were statistically significant between the NW and the O groups. As previous studies by our group have shown, the secretomes of more virulent pathogenic bacteria tend to have a higher antigenic density; this may be an indicator of pathogen activity in the obese subjects that may deserve further study [15,51].…”

Section: Discussionmentioning

confidence: 70%

“…The AAR is a value used to estimate the antigenic density of a protein, calculating the number of antigenic regions and normalized by the sequence length. We calculated this value using the Secret-AAR webserver for the different protein data sets [51]. Then, we used a Mann-Whitney test (p < 0.001) to establish if there was a significant difference between AAR values of the different data sets.…”

Section: Calculation Of the Abundance Of Antigenic Regions (Aar)mentioning

confidence: 99%

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Metatranscriptomic analysis to define the Secrebiome, and 16S rRNA profiling of the gut microbiome in obesity and metabolic syndrome of Mexican children

et al. 2020

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Background: In the last decade, increasing evidence has shown that changes in human gut microbiota are associated with diseases, such as obesity. The excreted/secreted proteins (secretome) of the gut microbiota affect the microbial composition, altering its colonization and persistence. Furthermore, it influences microbiota-host interactions by triggering inflammatory reactions and modulating the host's immune response. The metatranscriptome is essential to elucidate which genes are expressed under diseases. In this regard, little is known about the expressed secretome in the microbiome. Here, we use a metatranscriptomic approach to delineate the secretome of the gut microbiome of Mexican children with normal weight (NW) obesity (O) and obesity with metabolic syndrome (OMS). Additionally, we performed the 16S rRNA profiling of the gut microbiota. Results: Out of the 115,712 metatranscriptome genes that codified for proteins, 30,024 (26%) were predicted to be secreted, constituting the Secrebiome of the gut microbiome. The 16S profiling confirmed an increased abundance in Firmicutes and decreased in Bacteroidetes in the obesity groups, and a significantly higher richness and diversity than the normal weight group. We found novel biomarkers for obesity with metabolic syndrome such as increased Coriobacteraceae, Collinsela, and Collinsella aerofaciens; Erysipelotrichaceae, Catenibacterium and Catenibacterium sp., and decreased Parabacteroides distasonis, which correlated with clinical and anthropometric parameters associated to obesity and metabolic syndrome. Related to the Secrebiome, 16 genes, homologous to F. prausniitzi, were overexpressed for the obese and 15 genes homologous to Bacteroides, were overexpressed in the obesity with metabolic syndrome. Furthermore, a significant enrichment of CAZy enzymes was found in the Secrebiome. Additionally, significant differences in the antigenic density of the Secrebiome were found between normal weight and obesity groups. Conclusions: These findings show, for the first time, the role of the Secrebiome in the functional human-microbiota interaction. Our results highlight the importance of metatranscriptomics to provide novel information about the

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Section: Discussionmentioning

confidence: 70%

Section: Calculation Of the Abundance Of Antigenic Regions (Aar)mentioning

confidence: 99%

Metatranscriptomic analysis to define the Secrebiome, and 16S rRNA profiling of the gut microbiome in obesity and metabolic syndrome of Mexican children

et al. 2020

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“…The VaxiJen v2.0 ( http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html ) [41] , Secret-AAR ( http://microbiomics.ibt.unam.mx/tools/aar/ ) [42] , and ANTIGENPro ( http://scratch.proteomics.ics.uci.edu/ ) [43] , [44] servers were employed to predict the antigenicity of the candidate vaccine, C1, C2, and C3. The AllerCatPro ( https://allercatpro.bii.a-star.edu.sg/ ) [45] , AllergenFP v.1.0 ( http://ddg-pharmfac.net/AllergenFP/ ) [46] , and all the six approaches of the AlgPred (http://www.…”

Section: Methodsmentioning

confidence: 99%

Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches

Safavi

Kefayat

Mahdevar

et al. 2020

Vaccine

103

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Highlights The vaccine is composed of immunodominant regions of SARS-CoV-2 non-structural proteins. Also, the functional region of the spike protein is incorporated in the vaccine construct. The final vaccine construct contains multiple CD8+ and CD4+ overlapping epitopes Also, it contains multiple IFN-γ inducing, linear and conformational B cell epitopes. It forms significant interactions and stable complex with TLR-4/MD. The DNA vaccine is designed by reverse translation of the final vaccine construct.

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“…Of note, proteins with higher antigenic densities have lower AAR values. For this study, we calculated the AAR value for each protein in each data set using the Secret-AAR web-server ( http://microbiomics.ibt.unam.mx/tools/aar/index.php ) and reported the average unless stated otherwise [ 55 ]. Then, we used a Mann-Whitney statistical test to establish any significant differences between the AAR values of the different protein data sets.…”

Section: Methodsmentioning

confidence: 99%

Secretome characterization of clinical isolates from the Mycobacterium abscessus complex provides insight into antigenic differences

et al. 2021

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Background Mycobacterium abscessus (MAB) is a widely disseminated pathogenic non-tuberculous mycobacterium (NTM). Like with the M. tuberculosis complex (MTBC), excreted / secreted (ES) proteins play an essential role for its virulence and survival inside the host. Here, we used a robust bioinformatics pipeline to predict the secretome of the M. abscessus ATCC 19977 reference strain and 15 clinical isolates belonging to all three MAB subspecies, M. abscessus subsp. abscessus, M. abscessus subsp. bolletii, and M. abscessus subsp. massiliense. Results We found that ~ 18% of the proteins encoded in the MAB genomes were predicted as secreted and that the three MAB subspecies shared > 85% of the predicted secretomes. MAB isolates with a rough (R) colony morphotype showed larger predicted secretomes than isolates with a smooth (S) morphotype. Additionally, proteins exclusive to the secretomes of MAB R variants had higher antigenic densities than those exclusive to S variants, independent of the subspecies. For all investigated isolates, ES proteins had a significantly higher antigenic density than non-ES proteins. We identified 337 MAB ES proteins with homologues in previously investigated M. tuberculosis secretomes. Among these, 222 have previous experimental support of secretion, and some proteins showed homology with protein drug targets reported in the DrugBank database. The predicted MAB secretomes showed a higher abundance of proteins related to quorum-sensing and Mce domains as compared to MTBC indicating the importance of these pathways for MAB pathogenicity and virulence. Comparison of the predicted secretome of M. abscessus ATCC 19977 with the list of essential genes revealed that 99 secreted proteins corresponded to essential proteins required for in vitro growth. Conclusions This study represents the first systematic prediction and in silico characterization of the MAB secretome. Our study demonstrates that bioinformatics strategies can help to broadly explore mycobacterial secretomes including those of clinical isolates and to tailor subsequent, complex and time-consuming experimental approaches accordingly. This approach can support systematic investigation exploring candidate proteins for new vaccines and diagnostic markers to distinguish between colonization and infection. All predicted secretomes were deposited in the Secret-AAR web-server (http://microbiomics.ibt.unam.mx/tools/aar/index.php).

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Secret-AAR: a web server to assess the antigenic density of proteins and homology search against bacterial and parasite secretome proteins

Cited by 11 publications

References 17 publications

Metatranscriptomic analysis to define the Secrebiome, and 16S rRNA profiling of the gut microbiome in obesity and metabolic syndrome of Mexican children

Metatranscriptomic analysis to define the Secrebiome, and 16S rRNA profiling of the gut microbiome in obesity and metabolic syndrome of Mexican children

Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches

Secretome characterization of clinical isolates from the Mycobacterium abscessus complex provides insight into antigenic differences

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