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firestar help page

firestar[1,2] makes predictions of functionally important residues using the large inventory of functionally important residues in the FireDB[3] database. The reliability of the transfer of functional information between the functionally important residues in FireDB and the query sequence is evaluated via the local residue conservation between the two sequences.

User Input

The user must unambiguously identify a protein sequence. Three options are allowed:

Prediction summary

The first page returned by the server reports a compact summary of firestar analysis. An output example is shown below:

This summary page is the final step in the firestar pipeline.
An intermediate yet informative step can be displayed following the EXTENDED links. All alignments between the query and templates found with PSI-BLAST[4] and HHsearch[5] are graphically represented hereafter, separately. Here you can find a detailed help page with all the information described: EXTENDED Help.

Biologic Relevance

In FireDB (and of course in firestar) all the small molecule binding annotations are culled from from the experimental data stored in the PDB database. PDB ligands have been divided in three categories: COGNATE, POSSIBLE_COGNATE and NON_COGNATE in order to distinguish between possible biologically important compounds and other molecules (inhibitors, analogues, solvents from the crystalization conditions, etc.).

"COGNATE" compounds are those that are almost always candidates to be the real biological ligands. While "POSSIBLE COGNATE" ligands are also sometimes biological ligands, they often appear in a structure because they are present in buffers or crystallization solutions. "NON COGNATE" ligands could be drugs, inhibitors, analogues etc. etc. that are almost never natural biological ligands. Although "POSSIBLE COGNATE" and "NON COGNATE" ligands may not perform biological roles in a target protein, their binding may still provide information about the query protein, especially if the binding site is highly conserved.

If you are interested in a specific compound tag, complete lists are available here.

Per-Site Reliability

The reliability for each site is calculated combining three parameters:

3D Models

firestar selects the best template found in the HHsearch analysis that contains all the predicted site's residues and builds a model with MODELLER. B-factors in the file are modified, in order to permit user visualize predicted residues in red using visualization software, such as RasMol or PyMOL.

Per-residue Frequency Score

The "Highlight Option" permits you to highlight predicted residues with their normalized relative frequency score. These scores are calculated from the alignments used to predict each site and weights depending on the evolutive similarity of the aligned template are used. The scores for the residues can be seen by moving the mouse over each individual residue. These scores are independent for each site and should not be compared between sites.

High Throughput Mode

firestar is available as a REST web service. With a "wget" command you can directly query our web server and you will retrieve all the information provided in the summary page output in a easy-to-parse format. A query example is shown here:

    $> wget -O your_output_file_here ''

where the parameters are:
There are 4 more possible parameters available:
PLEASE, if you have a very big dataset (more than 1000 proteins), contact us and we can provide you a dedicated cluster for the analysis (

firestar has been also designed as BioMOBY service. This web service differs from the web server in that it predicts only ligand binding residues and a confidence score for each residue.

A Perl program example, (for reasons of server security the suffix of this file is '.txt', you should rename suffix), that invokes and parses firestar BioMOBY web services. It uses the following libraries:

To run this script:

    --input= <Amino acid sequences file as FASTA format>

    --output= <Output file where firestar reponses will be saved>

Using this input sample, you will run the script as below: --input=sample.faa --output=sample.output

For more information click here.

1. Lopez G, Maietta P, Rodriguez JM, Valencia A and Tress ML. (2011) "firestar--advances in the prediction of functionally important residues"
Nucleic Acids Research. doi: 10.1093/nar/gkr437
2. Lopez G, Valencia A, Tress ML. (2007) "firestar--prediction of functionally important residues using structural templates an alignment reliability"
Nucleic Acids Research, doi:10.1093/nar/gkm297
3. Maietta P, Lopez G, Carro A, Pingilley BJ, Leon LG, Valencia A, Tress ML. (2014) "FireDB: a compendium of biological and pharmacologically relevant ligands."
Nucleic Acids Research, doi: 10.1093/nar/gkt1127

4. Lopez G, Valencia A, Tress ML. (2007) "FireDB--a database of functionally important residues from proteins of known structure"
Nucleic Acids Research, doi: 10.1093/nar/gkl897
5. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W and Lipman DJ. (1997) "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs"
Nucleic Acids Research, doi: 10.1093/nar/25.17.3389
6. Söding J. (2005) "Protein homology detection by HMM-HMM comparison."
Bioinformatics 2005; doi: 10.1093/bioinformatics/bti125