An archive of data for the legacy Sisyphus project
If you find the data useful , please cite :
Andreeva A, Prlić A, Hubbard TJ, Murzin AG.
SISYPHUS--structural alignments for proteins with non-trivial relationships.
Nucleic Acids Res. 2007 Jan;35(Database issue):D253-9.
doi: 10.1093/nar/gkl746. Epub 2006 Oct 26.
PMID: 17068077;
PMCID: PMC1635320.
With the increasing amount of structural data, the number of homologous protein structures bearing topological irregularities is steadily growing. These include proteins with circular permutations, segment-swapping, context-dependent folding or chameleon sequences that can adopt alternative secondary structures. Most of these non-trivial protein relationships are readily identified during the systematic analysis in the SCOP (Structural Classification of Proteins) database. However, their automatic identification using the existing computational tools still remains difficult or impossible. Such non-trivial cases of protein relationships are known to pose a problem to the multiple alignment algorithms and to impede the comparative modeling studies. They support a new emerging concept of evolutionary changeable protein fold and thus create practical difficulties to the hierarchical protein structural classifications.
To facilitate the understanding and to provide a comprehensive annotation of proteins with such non-trivial structural relationships we have created SISYPHUS, a compendium to the SCOP database.
The SISYPHUS database contains a collection of manually curated structural alignments and their interrelationships. Each multiple alignment within the SISYPHUS database consists of structurally similar regions common to a group of proteins. These regions range from oligomeric biological units, or individual domains to fragments of different size representing either internal structural repeats or motifs common to structurally distinct proteins. The SISYPHUS multiple alignments are displayed with SPICE, a browser that provides an integrated view of protein sequences, structures and their annotations.
Note: This dataset is from before the PDB remediation efforts, as such there may be some entries with inconsistent chain IDs.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent