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Server for the Identification of Functional Regions in Proteins
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ConSurf Quick Help


 

PDB ID

Each structure in the PDB is represented by a 4 character alphanumeric identifier, assigned upon its deposition. For example, 1bxl and 1d66 are identification codes for PDB entries for Bcl-Xl / Bak complex and Gal4 (Residues 1 - 65) complex with 19mer DNA, respectively. 

For more information check the PDB home page.

 

User-provided PDB file

The ATOM record is essential for ConSurf run therefore it must be included in the user-provided PDB file; The CSI-BLAST or PSI-BLAST search for homologues is done using the sequence extracted from the SEQRES record, or from the ATOM record in case the SEQRES record is missing.

Chain identifier

To run ConSurf you should specify a Chain Identifier (A, B, C, in the corresponding field of the PDB file). If no chain is specified in the PDB file, please type 'none' on the chain field. 

One way to get the chain identifier is to display the molecule in FirstGlance in Jmol and click on the chain of interest. The chain identifier is reported in the message box at the lower left frame.

You can also find the chain identifiers of a standard PDB file on the third field (column 12) of the SEQRES records or the sixth field (column 22) of the ATOM records. The chain identifier may be any single legal character, including a blank character, which is used if there is only one chain.

For more information check the PDB File Format Contents Guide

Find sequence homologues

For protein sequence, homologous sequences are collected using a CSI-BLAST, PSI-BLAST or BLAST  search against the selected database and the user-specified criteria for defining homologues as described below.

These fields are irrelevant for a user-provided MSA file.

 

Database

Several databases can be searched for homologous sequences:

For nucleic acid sequence the search is performed using the BLAST algorithm with default parameters to collect homologous sequences from the NCBI NT database.

 

E-value (PSI-BLAST)

The Expectation value (E-value) is a parameter that describes the number of hits one can "expect" to see just by chance when searching a database of a particular size. It decreases exponentially with the Score (S) that is assigned to a match between two sequences. Essentially, the E-value describes the random background noise that exists for matches between sequences. The lower the E-value, the more significant the score is. The Expectation value is used as a convenient way to create a significance threshold for reporting results. For example, the meaning of an E-value of 1 assigned to a hit is that in a database of the current size one might expect to see 1 match with a similar score simply by chance. Using a higher E-value will generally yield more hits, but their distance from the query sequence will increase. 

 

Minimal identity (automatic removal of remote homologues)

The user can control the level of sequence identity for which a hit sequence is still considered a homologue. Filtration according to the sequence identity between the sequence found and the sequence of interest enables the user to filter out sequences that produce significant alignment with the sequence of interest however might have different function or structure. For proteins, the default level is set to 35% identity, which is the upper bound limit of the 'twilight zone' for proteins structures (Rost, 1999).

 

Maximal identity between sequences (removing redundant sequences)

The user can specify the level of redundant sequences for removal. The sequences found are clustered by their level of identity using CD-HIT and the cutoff specified by the user (default is 95% identity). Only one sequence from each cluster is used for the analysis.

 

Maximum number of homologues

The maximum number of homologues, after applying all the filters (described above), to be included in the calculation. In order to include all the homologues remain after those filters, replace the default value with the word "all".


Manual selection of sequences for the analysis

After searching for homologous sequences, the user can manually select the relevant sequences to be included in the analysis using a simple form that provides all the relevant data for the sequences found and links to external web-recourses.

 

User-provided multiple sequence alignment (MSA)

ConSurf accepts external MSAs in the 7 formats supported by BioPerl.

These are: NBRF/PIR, EMBL/SwissProt, Pearson (Fasta), GDE, Clustal, GCG/MSF and RSF format. For additional information see Format Converter (use "View in browser" choice) links.

In case you provide an external MSA file, you are required to fill the "Query sequence name in MSA file" field in the ConSurf form. This is the name of the sequence in the provided MSA file that corresponds to the query chain of the selected PDB structure.

In case you provide an external MSA file in Fasta format, please use the "-" sign as the only gap symbol, as this is the only standard gap sign that ConSurf accepts.

User-provided phylogenetic tree file

In case you provide an external multiple sequence alignment (MSA) file, ConSurf can also accept a corresponding external phylogenetic tree in Newick (Phylip) format.

The names of the sequences in the tree file must be identical to the names of the sequences in the MSA file.

 

Graphic visualization

The target protein chain is represented as a space-filling model with the conservation grades color-coded onto each amino acid van-der-Waals surface. All other chains in the PDB file are displayed in backbone representation and all ligands are presented in ball-and-stick representation. 

More on this topic can be found here.

 

Reliability of the results

Calculation quality

The quality of the results depends on many factors, but the two most important ones are the quality and number of the homologues. If the input homologues list does not include a minimal number of close enough homologues, the quality of the multiple sequence alignment (MSA) decreases, influencing the quality of the phylogenetic tree and the rest of the calculation. On the other hand, if homologues that are too remote are used, the conservation signal/s may decrease due to background noise added to the MSA. Therefore we recommend to run several runs with increasing number of homologues (10, 30, 50, 100, etc.), and compare the conserved regions in the protein. Generally, reliable conserved regions will progressively cluster. In any case, we recommend a minimum of 10 homologues for any ConSurf run.  

 

Position specific quality

PSI-BLAST is a local alignment engine, which means that it finds homologues that are generally shorter than the query. For this reason the multiple sequence alignment (MSA) usually includes many non-informative regions, or gaps. Obviously, the quality of the calculation for each amino acid position, i.e., column in the MSA, depends on the total number of informative non-gapped residues in the column. We report the number of sequences that are available for computation at each position, referred to as "MSA DATA", in the output file "Amino Acid Conservation Score". In cases that there are less than 6 non-gapped residues in a certain position, we consider the conservation score in this position as unreliable, and it is marked in the output files. When using the Bayesian method for the conservation scores calculations, a confidence interval for the scores estimations is obtained. The high and low values of the interval are assigned color grades according to the 1-9 coloring scheme. If the interval is equal to- or larger than- 4 color grades, the conservation scores are regarded as unreliable, and are also marked in the output files.   
 

Minimal requirements for a successful ConSurf run

When using a 3D structure:

For all ConSurf runs: