interpro database
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2020 ◽  
Vol 49 (D1) ◽  
pp. D344-D354 ◽  
Author(s):  
Matthias Blum ◽  
Hsin-Yu Chang ◽  
Sara Chuguransky ◽  
Tiago Grego ◽  
Swaathi Kandasaamy ◽  
...  

Abstract The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. InterProScan is the underlying software that allows protein and nucleic acid sequences to be searched against InterPro's signatures. Signatures are predictive models which describe protein families, domains or sites, and are provided by multiple databases. InterPro combines signatures representing equivalent families, domains or sites, and provides additional information such as descriptions, literature references and Gene Ontology (GO) terms, to produce a comprehensive resource for protein classification. Founded in 1999, InterPro has become one of the most widely used resources for protein family annotation. Here, we report the status of InterPro (version 81.0) in its 20th year of operation, and its associated software, including updates to database content, the release of a new website and REST API, and performance improvements in InterProScan.


Author(s):  
Amrita Banerjee ◽  
Dipannita Santra ◽  
Smarajit Maiti

AbstractThe recent outbreak by SARS-CoV-2 has generated a chaos in global health and economy and claimed/infected a large number of lives. Closely resembling with SARS CoV, the present strain has manifested exceptionally higher degree of spreadability, virulence and stability possibly due to some unidentified mutations. The viral spike glycoprotein is very likely to interact with host Angiotensin-Converting Enzyme 2 (ACE2) and transmits its genetic materials and hijacks host machinery with extreme fidelity for self propagation. Few attempts have been made to develop a suitable vaccine or ACE2 blocker or virus-receptor inhibitor within this short period of time. Here, attempt was taken to develop some therapeutic and vaccination strategies with a comparison of spike glycoproteins among SARS-CoV, MERS-CoV and the SARS-CoV-2. We verified their structure quality (SWISS-MODEL, Phyre2, Pymol) topology (ProFunc), motifs (MEME Suite, GLAM2Scan), gene ontology based conserved domain (InterPro database) and screened several epitopes (SVMTrip) of SARS CoV-2 based on their energetics, IC50 and antigenicity with regard to their possible glycosylation and MHC/paratopic binding (Vaxigen v2.0, HawkDock, ZDOCK Server) effects. We screened here few pairs of spike protein epitopic regions and selected their energetic, IC50, MHC II reactivity and found some of those to be very good target for vaccination. A possible role of glycosylation on epitopic region showed profound effects on epitopic recognition. The present work might be helpful for the urgent development of a suitable vaccination regimen against SARS CoV-2.


Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3814 ◽  
Author(s):  
Sheng Wu ◽  
Alexander E. Wilson ◽  
Lijing Chang ◽  
Li Tian

Although the evolutionary significance of the early-diverging flowering plant Amborella (Amborella trichopoda Baill.) is widely recognized, its metabolic landscape, particularly specialized metabolites, is currently underexplored. In this work, we analyzed the metabolomes of Amborella tissues using liquid chromatography high-resolution electrospray ionization mass spectrometry (LC-HR-ESI-MS). By matching the mass spectra of Amborella metabolites with those of authentic phytochemical standards in the publicly accessible libraries, 63, 39, and 21 compounds were tentatively identified in leaves, stems, and roots, respectively. Free amino acids, organic acids, simple sugars, cofactors, as well as abundant glycosylated and/or methylated phenolic specialized metabolites were observed in Amborella leaves. Diverse metabolites were also detected in stems and roots, including those that were not identified in leaves. To understand the biosynthesis of specialized metabolites with glycosyl and methyl modifications, families of small molecule UDP-dependent glycosyltransferases (UGTs) and O-methyltransferases (OMTs) were identified in the Amborella genome and the InterPro database based on conserved functional domains. Of the 17 phylogenetic groups of plant UGTs (A–Q) defined to date, Amborella UGTs are absent from groups B, N, and P, but they are highly abundant in group L. Among the 25 Amborella OMTs, 7 cluster with caffeoyl-coenzyme A (CCoA) OMTs involved in lignin and phenolic metabolism, whereas 18 form a clade with plant OMTs that methylate hydroxycinnamic acids, flavonoids, or alkaloids. Overall, this first report of metabolomes and candidate metabolic genes in Amborella provides a starting point to a better understanding of specialized metabolites and biosynthetic enzymes in this basal lineage of flowering plants.


2015 ◽  
Author(s):  
Seyed Ziaeddin Alborzi ◽  
Marie-Dominique Devignes ◽  
David W. RITCHIE

Abstract With the growing number of protein structures in the protein data bank (PDB), there is a need to annotate these structures at the domain level in order to relate protein structure to protein function. Thanks to the SIFTS database, many PDB chains are now cross-referenced with Pfam domains and enzyme commission (EC) numbers. However, these annotations do not include any explicit relationship between individual Pfam domains and EC numbers. This article presents a novel statistical training-based method called EC-PSI that can automatically infer high confidence associations between EC numbers and Pfam domains directly from EC-chain associations from SIFTS and from EC-sequence associations from the SwissProt, and TrEMBL databases. By collecting and integrating these existing EC-chain/sequence annotations, our approach is able to infer a total of 8,329 direct EC-Pfam associations with an overall F-measure of 0.819 with respect to the manually curated InterPro database, which we treat here as a “gold standard” reference dataset. Thus, compared to the 1,493 EC-Pfam associations in InterPro, our approach provides a way to find over six times as many high quality EC-Pfam associations completely automatically.


2007 ◽  
Vol 35 (Database) ◽  
pp. D224-D228 ◽  
Author(s):  
N. J. Mulder ◽  
R. Apweiler ◽  
T. K. Attwood ◽  
A. Bairoch ◽  
A. Bateman ◽  
...  

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