Paper Info
Title
LenVarDB: database of length-variant protein domains.
Abstract
Protein domains are functionally and structurally independent modules, which add to the functional variety of proteins. This array of functional diversity has been enabled by evolutionary changes, such as amino acid substitutions or insertions or deletions, occurring in these protein domains. Length variations (indels) can introduce changes at structural, functional and interaction levels. LenVarDB (freely available at http://caps.ncbs.res.in/lenvardb/) traces these length variations, starting from structure-based sequence alignments in our Protein Alignments organized as Structural Superfamilies (PASS2) database, across 731 structural classification of proteins (SCOP)-based protein domain superfamilies connected to 2 730 625 sequence homologues. Alignment of sequence homologues corresponding to a structural domain is available, starting from a structure-based sequence alignment of the superfamily. Orientation of the length-variant (indel) regions in protein domains can be visualized by mapping them on the structure and on the alignment. Knowledge about location of length variations within protein domains and their visual representation will be useful in predicting changes within structurally or functionally relevant sites, which may ultimately regulate protein function. Non-technical summary: Evolutionary changes bring about natural changes to proteins that may be found in many organisms. Such changes could be reflected as amino acid substitutions or insertions-deletions (indels) in protein sequences. LenVarDB is a database that provides an early overview of observed length variations that were set among 731 protein families and after examining >2 million sequences. Indels are followed up to observe if they are close to the active site such that they can affect the activity of proteins. Inclusion of such information can aid the design of bioengineering experiments.
Year
DOI
Venue
2014
10.1093/nar/gkt1014
NUCLEIC ACIDS RESEARCH
Field
DocType
Volume
Sequence alignment,Protein structure prediction,Structural alignment,Protein domain,Biology,Loop modeling,Genetics,Multiple sequence alignment,Structural Classification of Proteins database,Protein function prediction,Molecular biology,Database
Journal
42
Issue
ISSN
Citations 
D1
0305-1048
2
PageRank 
References 
Authors
0.39
10
3
Name
Order
Citations
PageRank
Eshita Mutt131.77
Oommen K Mathew261.86
Ramanathan Sowdhamini321521.20