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Entamoeba histolytica is an invasive, pathogenic parasite causing amoebiasis. Given that proteins involved in transmembrane (TM) transport are crucial for the adherence, invasion, and nutrition of the parasite, we conducted a genome-wide bioinformatics analysis of encoding proteins to functionally classify and characterize all the TM proteins in E. histolytica. In the present study, 692 TM proteins have been identified, of which 546 are TM transporters. For the first time, we report a set of 141 uncharacterized proteins predicted as TM transporters. The percentage of TM proteins was found to be lower in comparison to the free-living eukaryotes, due to the extracellular nature and functional diversification of the TM proteins. The number of multi-pass proteins is larger than the single-pass proteins; though both have their own significance in parasitism, multi-pass proteins are more extensively required as these are involved in acquiring nutrition and for ion transport, while single-pass proteins are only required at the time of inciting infection. Overall, this intestinal parasite implements multiple mechanisms for establishing infection, obtaining nutrition, and adapting itself to the new host environment. A classification of the repertoire of TM transporters in the present study augments several hints on potential methods of targeting the parasite for therapeutic benefits.
Pubmed ID: 30332795
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Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.
View all literature mentionsService providing functional analysis of proteins by classifying them into families and predicting domains and important sites. They combine protein signatures from a number of member databases into a single searchable resource, capitalizing on their individual strengths to produce a powerful integrated database and diagnostic tool. This integrated database of predictive protein signatures is used for the classification and automatic annotation of proteins and genomes. InterPro classifies sequences at superfamily, family and subfamily levels, predicting the occurrence of functional domains, repeats and important sites. InterPro adds in-depth annotation, including GO terms, to the protein signatures. You can access the data programmatically, via Web Services. The member databases use a number of approaches: # ProDom: provider of sequence-clusters built from UniProtKB using PSI-BLAST. # PROSITE patterns: provider of simple regular expressions. # PROSITE and HAMAP profiles: provide sequence matrices. # PRINTS provider of fingerprints, which are groups of aligned, un-weighted Position Specific Sequence Matrices (PSSMs). # PANTHER, PIRSF, Pfam, SMART, TIGRFAMs, Gene3D and SUPERFAMILY: are providers of hidden Markov models (HMMs). Your contributions are welcome. You are encouraged to use the ''''Add your annotation'''' button on InterPro entry pages to suggest updated or improved annotation for individual InterPro entries.
View all literature mentionsA subCELlular LOcalization predictor based on a multi-class support vector machine (SVM) classification system. CELLO uses 4 types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. They combine votes from these classifiers and use the jury votes to determine the final assignment.
View all literature mentionsWeb application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
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