Cargando…

In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease

BACKGROUND: Amoebiasis is the third most common parasitic cause of morbidity and mortality, particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence there arises a necessity for a better diagnostic approach. Serine-rich Entamoeba hist...

Descripción completa

Detalles Bibliográficos
Autores principales: Manochitra, Kumar, Parija, Subhash Chandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493030/
https://www.ncbi.nlm.nih.gov/pubmed/28674640
http://dx.doi.org/10.7717/peerj.3160
_version_ 1783247438678589440
author Manochitra, Kumar
Parija, Subhash Chandra
author_facet Manochitra, Kumar
Parija, Subhash Chandra
author_sort Manochitra, Kumar
collection PubMed
description BACKGROUND: Amoebiasis is the third most common parasitic cause of morbidity and mortality, particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence there arises a necessity for a better diagnostic approach. Serine-rich Entamoeba histolyticaprotein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal in E. histolyticavirulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential. However, studies in this aspect are scant. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using appropriate in-silicomethods. METHODS: The amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out. RESULTS: The protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be an extracellular protein, peroxiredoxin a peripheral membrane protein while Gal/GalNAc lectin was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc lectin, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All the three proteins exhibited similarity in their structures and were mostly composed of loops. DISCUSSION: The structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures of SREHP and peroxiredoxin predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities.
format Online
Article
Text
id pubmed-5493030
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-54930302017-07-03 In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease Manochitra, Kumar Parija, Subhash Chandra PeerJ Biochemistry BACKGROUND: Amoebiasis is the third most common parasitic cause of morbidity and mortality, particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence there arises a necessity for a better diagnostic approach. Serine-rich Entamoeba histolyticaprotein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal in E. histolyticavirulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential. However, studies in this aspect are scant. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using appropriate in-silicomethods. METHODS: The amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out. RESULTS: The protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be an extracellular protein, peroxiredoxin a peripheral membrane protein while Gal/GalNAc lectin was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc lectin, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All the three proteins exhibited similarity in their structures and were mostly composed of loops. DISCUSSION: The structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures of SREHP and peroxiredoxin predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities. PeerJ Inc. 2017-06-28 /pmc/articles/PMC5493030/ /pubmed/28674640 http://dx.doi.org/10.7717/peerj.3160 Text en ©2017 Manochitra and Parija http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Biochemistry
Manochitra, Kumar
Parija, Subhash Chandra
In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease
title In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease
title_full In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease
title_fullStr In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease
title_full_unstemmed In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease
title_short In-silico prediction and modeling of the Entamoeba histolytica proteins: Serine-rich Entamoeba histolytica protein and 29 kDa Cysteine-rich protease
title_sort in-silico prediction and modeling of the entamoeba histolytica proteins: serine-rich entamoeba histolytica protein and 29 kda cysteine-rich protease
topic Biochemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493030/
https://www.ncbi.nlm.nih.gov/pubmed/28674640
http://dx.doi.org/10.7717/peerj.3160
work_keys_str_mv AT manochitrakumar insilicopredictionandmodelingoftheentamoebahistolyticaproteinsserinerichentamoebahistolyticaproteinand29kdacysteinerichprotease
AT parijasubhashchandra insilicopredictionandmodelingoftheentamoebahistolyticaproteinsserinerichentamoebahistolyticaproteinand29kdacysteinerichprotease