Name
Title | ||
|---|---|---|
Structural updates of alignment of protein domains and consequences on evolutionary models of domain superfamilies. |
Abstract | ||
|---|---|---|
Influx of newly determined crystal structures into primary structural databases is increasing at a rapid pace. This leads to updation of primary and their dependent secondary databases which makes large scale analysis of structures even more challenging. Hence, it becomes essential to compare and appreciate replacement of data and inclusion of new data that is critical between two updates. PASS2 is a database that retains structure-based sequence alignments of protein domain superfamilies and relies on SCOP database for its hierarchy and definition of superfamily members. Since, accurate alignments of distantly related proteins are useful evolutionary models for depicting variations within protein superfamilies, this study aims to trace the changes in data in between PASS2 updates.In this study, differences in superfamily compositions, family constituents and length variations between different versions of PASS2 have been tracked. Studying length variations in protein domains, which have been introduced by indels (insertions/deletions), are important because theses indels act as evolutionary signatures in introducing variations in substrate specificity, domain interactions and sometimes even regulating protein stability. With this objective of classifying the nature and source of variations in the superfamilies during transitions (between the different versions of PASS2), increasing length-rigidity of the superfamilies in the recent version is observed. In order to study such length-variant superfamilies in detail, an improved classification approach is also presented, which divides the superfamilies into distinct groups based on their extent of length variation.An objective study in terms of transition between the database updates, detailed investigation of the new/old members and examination of their structural alignments is non-trivial and will help researchers in designing experiments on specific superfamilies, in various modelling studies, in linking representative superfamily members to rapidly expanding sequence space and in evaluating the effects of length variations of new members in drug target proteins. The improved objective classification scheme developed here would be useful in future for automatic analysis of length variation in cases of updates of databases or even within different secondary databases. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1756-0381-6-20 | BioData mining |
Keywords | Field | DocType |
bioinformatics,biomedical research | Data mining,Protein domain,SUPERFAMILY,Computer science,Bioinformatics,Hierarchy,Structural Classification of Proteins database | Journal |
Volume | Issue | ISSN |
6 | 1 | 1756-0381 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eshita Mutt | 1 | 3 | 1.77 |
| Sudha Sane Rani | 2 | 0 | 0.34 |
| Ramanathan Sowdhamini | 3 | 215 | 21.20 |