Introduction to HSSP curve
HSSP is a derived database merging structural (3-D) and sequence (1-D) information. For each protein of known 3-D structure from the Protein Data Bank (PDB), the database has a multiple sequence alignment of all available homologues and a sequence profile characteristic of the family. The list of homologues is the result of a database search in SwissProt using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently. The listed homologues are very likely to have the same 3-D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of aligned sequence families, but also a database of implied secondary and tertiary structures covering 29% of all SwissProt-stored sequences.
According to the paper: Sequence alignments unambiguously distinguish between protein pairs of similar and non-similar structure when the pairwise sequence identity is high (>40% for long alignments). The signal gets blurred in the twilight zone of 20-35% sequence identity. Here, more than a million sequence alignments were analysed between protein pairs of known structures to re-define a line distinguishing between true and false positives for low levels of similarity. Four results stood out. (i) The transition from the safe zone of sequence alignment into the twilight zone is described by an explosion of false negatives. More than 95% of all pairs detected in the twilight zone had different structures. More precisely, above a cut-off roughly corresponding to 30% sequence identity, 90% of the pairs were homologous; below 25% less than 10% were. (ii) Whether or not sequence homology implied structural identity depended crucially on the alignment length. For example, if 10 residues were similar in an alignment of length 16 (>60%), structural similarity could not be inferred. (iii) The 'more similar than identical' rule (discarding all pairs for which percentage similarity was lower than percentage identity) reduced false positives significantly. (iv) Using intermediate sequences for finding links between more distant families was almost as successful: pairs were predicted to be homologous when the respective sequence families had proteins in common. All findings are applicable to automatic database searches.
There is an existing implementation that can be found on this page.
The program accepts either a set of sequences in FASTA format or a list of identifiers from either of the following protein databases: SWISS-PROT (13), PDB (14) or TrEMBL (13). Alternatively, one of the following alignment-file formats is accepted to bypass the first step of the algorithm (see below): BLAST, PSIBLAST, pair, markx0, markx1, markx2, markx3, markx10 or srspair.
It runs based on a greedy algorithm that calculates the HSSP-values.
Visualize the HSSP curve and allow the user to dynamically filter or categorize the data shown on the graph for better insights.
- Understand the HSSP curve and the calculations needed to visualize it
- Gather input (BLAST results) with which we can work on visualizing
- Parse BLAST results input
- Calculate and visualize the HSSP curve
- Implement dynamic filtering of the curve
- Get feedback from biologist about possible improvements for better insights
- Work on changes/new features based on the feedback
What we done
- Read the paper and used it to make the calculations for plotting the values
- Worked with a Blast Result input as xml
- Imported the values into a structure we can use
- Implemented the plotting of the points
- First draft of the tool with the option to put xml input and parse it and show it on the plotter
What we plan for the next week
- Further study the paper
- Show the hssp curve over the plotted data
- Implement a drop down selection for different values of hssp curve