ConSeq Overview

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Introduction

   ConSeq is a web server for the identification of structurally and functionally important residues in protein sequences. Given a set of homologous proteins in the form of multiple sequence alignment (MSA), the evolutionary rate at each amino acid site in the MSA is calculated. The slowly evolving sites are often biologically important. To determine which of these sites are important for maintaining the protein structure and which are functionally important, the MSA is used to predict the relative solvent accessibility state of each site (i.e. buried vs. exposed). The basic assumption is that functionally important residues,which for example take part in ligand binding, DNA binding and protein-protein interactions, are often evolutionarily conserved and are most likely to be solvent accessible, whereas highly conserved residues within the protein core are likely to have an important structural role in maintaining the protein's fold. However it should be emphasized that the discrimination between the functional and structural residues ("functional" = highly conserved and exposed, whereas "structural" = highly conserved and buried) might be problematic in cases where a certain residue has both functional role and structural roles. Moreover, it should be noted that in certain cases, functionally important sites might evolve faster; for example, hypervariable peptide binding sites in MHC molecules (see example at: http://consurf.tau.ac.il/gallery.html).

ConSeq was designed to analyze protein sequences of unknown three-dimensional (3D) structure and can provide fast and useful leads for the design and analysis of mutagenesis studies. For cases in which the 3D structure of the protein is known, we recommend the use of the ConSurf server (http://consurf.tau.ac.il/).




 A ConSeq analysis of a set of 5 well-documented proteins and a comparison to other available web-servers, are presented in the VALIDATION and  COMPARISON sections, respectively. The outcome of a ConSeq analysis of a set of 111 proteins of unknown function is presented in "PREDICTIONS" section.



Methodology 



Given the sequence of a protein or a domain as an input, the server
automatically carries out a PSI-BLAST search for close homologous sequences in the SWISS-PROT
database 





or the full UNI-PROT knowledgebase (SWISS-PROT + TrEMBL) databases.



 It then multiply
aligns them using the CLUSTALW or MUSCLE program, builds a phylogenetic tree consistent with the
MSA, and calculates the conservation scores using either an empirical Bayesian or the Maximum Likelihood method. Alternatively, a
user-provided MSA can be processed. In this case, the PSI-BLAST search and CLUSTALW   or MUSCLE alignment steps are passed over, and the phylogenetic tree is calculated directly from the user-provided MSA. 



 



Based on the MSA, the server predicts
the burial status of each residue, i.e., whether it is buried in the protein
core or exposed to the solvent, based on a neural network algorithm. Slowly-evolving and exposed residues are marked with an "f", to indicate that they
are predicted to be functional, whereas slowly-evolving and buried residues
are marked with an "s", to indicate that they are predicted to have an
important structural role. 



The sequence, with the conservation scores color-coded onto it, the
relative accessibility prediction and the indication of the functional and
structurally predicted residues, can finally be visualized on-line (Fig. 1). 








Protein query sequence



The
user is requested to paste the query sequence of the protein or domain in a FASTA
format. 

  



Searching for homologous
sequences 



The
server uses the PSI-BLAST
heuristic algorithm (Altschul et al., 1997) with default parameters to collect
homologous sequences to the query sequence. The search is carried out using the
SWISS-PROT
database (O'Donovan et al., 2002) 


or the full UNI-PROT knowledgebase (SWISS-PROT + TrEMBL) databases



 and a default single iteration of
PSI-BLAST with an E-value
cutoff of 0.001. A profile search can be generated with up to 5 PSI-BLAST
iterations, by changing the number of iterations in the Home Page. The 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. The higher
the E-value, the more hits will be expected, but the pairwise distance between
them and the query sequence will increase.The E-value cutoff can also be changed, from the server Home Page.



The
homologue names extracted from PSI-BLAST output file are the original
SWISS-PROT key names found by PSI-BLAST (e.g. XXXX_YYY). If more than one match
is found for the same sequence, the first one conserves its original SWISS-PROT
name while the others receive a sequential number (e.g. XXXX_YYY_1). 



 



Generating the Multiple Sequence Alignment  (MSA)  



The
server uses CLUSTALW
(Thompson et al., 1994) with default parameters to align the
homologues extracted from the PSI-BLAST output file. 



In cases where the user provides an MSA, it can be in any of the following
formats: NBRF/PIR, EMBL/SwissProt, Pearson (Fasta), GDE, Clustal, GCG/MSF and
RSF formats. For more information on these formats check Additional
MSA Format Information and The Format
Converter.In case of using a MSA from the HSSP database, please convert it first to the Fasta format using the HsspToFasta script.   

  



Calculating the amino acid
conservation scores  



The
server uses the neighbor-joining method (Saitou and Nei, 1987) to construct an
evolutionary tree consistent with the available MSA, and calculates a
conservation score for each site on the MSA. This is carried out using either an empirical Bayesian or the Maximum Likelihood method. 




The rate of evolution is not constant among amino
acid sites: some positions evolve slowly and are commonly referred to as
"conserved", while others evolve rapidly and are referred to as
"variable". The rate variations correspond to different levels of
purifying selection which act on these sites. The purifying selection can be
the result of geometrical constraints on the folding of the protein into its 3D
structure, or constraints at amino acid sites involved in enzymatic activity, in
ligand binding or at amino acid sites that take part in protein-protein
interactions. The rate of evolution at each site is calculated using either an empirical Bayesian or the Maximum Likelihood paradigm. This permits taking into account the stochastic process, which underlies sequence evolution within protein families and considers the topology and branch lengths of the phylogenetic tree of the protein family. The conservation score at a site corresponds to the
site's evolutionary rate. A detailed description of the algorithm is provided
in Bioinformatics 18 Suppl. 1 S1-S7, 2002 (PDF). Rate4Site, a stand-alone
implementation of this algorithm, is available at the URL: Rate4Site. 





The conservation scores calculated by ConSeq
appear in the SCORE column in the "Amino
Acid Conservation Score" output file. The scores are normalized, so
that the average score for all residues is zero, and the standard deviation is
one. The conservation scores calculated by ConSeq are a relative measure of
evolutionary conservation at each sequence site of the query sequence. The
lowest score represents the most conserved position in a protein. It does not
necessarily indicate 100% conservation (e.g. no mutations at all), but rather
indicates that this position is the most conserved in this specific protein calculated by the use of a specific MSA.





  




Model of substitution for proteins
          

          
The inference of
evolutionary conservation relies on a specified probabilistic model of
amino-acid replacements. The server supports a
few models of
substitution for nuclear DNA-encoded proteins as well as models of
non-nuclear DNA-encoded proteins. The model of substitution can be
chosen from the "Model of substitution for proteins" drop-down list,
which is available in the Advance Options in the server Home Page. The
JTT, Dayhoff and WAG matrices are suitable for nuclear
DNA-encoded proteins. The WAG matrix has been inferred from a large
database of sequences comprising a broad range of protein families and
is thus suitable for distantly related amino acid sequences.
The
mtREV and cpREV matrices are suitable for mitochondrial, and
chloroplast DNA-encoded proteins, respectively.




Conservation
coloring scheme 



The
discrete conservation color scheme used for visualization is based on the
continuous conservation scores. The color grades (1-9) are assigned as
follows:  



The
conservation scores below the average (negative values, which are indicative of
slowly evolving, conserved sites) are divided into 4.5 equal intervals. The
same 4.5 intervals are used for the scores above the average (positive
values, which are indicative of rapidly evolving, variable sites). Thus, 9
qually sized categories of conservation, or grades, are obtained. Colors are
assigned to the 9 grades for graphic visualization. The coloring results of a
ConSeq run do not indicate the absolute magnitudes of evolutionary distances,
but rather the relative degrees of conservation for each residue. ConSeq
scaling procedure does not guarantee that grades 1-8 will always be occupied,
although grade 9 is always occupied by at least one residue.



Surface accessibility prediction



A
feed-forward neural network system (Fariselli and Casadio, 2001) is combined
with evolutionary information, using pre-made PSI-BLAST profiles in order to
predict the relative solvent accessibility of each residue in the query
sequence based on the MSA. The classification depends on a relative
accessibility value lower or higher than 16%. The output consists of a single
logistic unit for each residue that estimates whether it is solvent-accessible or not (i.e. buried vs. exposed). The prediction of the solvent
accessibility reaches an accuracy of 76%. A detailed description of the
algorithm is provided in Bioinformatics, 17, 202-204, 2001 (PDF)
and in Proteins 47: 142-153, 2002 (PDF).


Important note: The neural network algorithm was designed to predict the relative surface accessibility of amino acids in globular proteins. Thus, the buried/exposed prediction for the transmembrane (TM) segments of proteins is inaccurate; consequently the structural/functional classification is meaningless for these parts. Therefore it is recommended to ignore the surface accessibility predictions for these segments and to consider the evolutionary rates only.


 

 



Output 



In each run, ConSeq produces an output file called  "ConSeq
Job Status Page". This file is automatically updated every 30 seconds,
showing the input parameters uploaded to the server and messages regarding the
different stages of the server activity. When the calculation finishes, ConSeq
produces the message "ConSeq finished the calculation" and several links
appear: 



"View The
Colored Results " 



This is the
main link of the page. It leads to the graphic
visualization of the color-coded sequence, the solvent accessibility
prediction for the amino acids in the sequence and an indication regarding the
important functional and structural residues.



 



In
addition, ConSeq presents several files that are generated during the
calculations:



"PSI-BLAST output" 



This links to the output
file generated by PSI-BLAST, which includes the sequences found, their
pairwise alignment with the query sequence, etc. 



This file is not generated
if the user provides the MSA.



"The
Homologues found by PSI-BLAST (in FASTA format)"



This links to the file
including the homologous sequences and their SWISS-PROT code names extracted
from the PSI-BLAST output, converted to FASTA format.  



This file is not generated
if the user provides the MSA.



"Multiple
Sequence Alignment (in Clustal format)" 



This links to the output
file produced by CLUSTALW.




If the user provides a non-CLUSTALW format MSA, the MSA is converted to CLUSTALW format and presented in this output file. 



"Phylogenetic
Tree" 



This links to the textual data of the generated phylogenetic tree based on the neighbor-joining method (Saitou and Nei, 1987). To view a graphic display of the tree, several programs can be used for displaying phylogenies, for example TreeView.



"Amino Acid
Conservation Scores" 



This link includes the conservation scores
obtained for each amino acid position of the target sequence. This output file
also includes the color grades for each amino acid site. The buried (b) or
exposed (e) predicted status for each residue is shown in an additional column.
The "FUNCTION" column indicates the structural and functional residues
according to these codes: highly conserved (color grade: 8 or 9) and exposed
residues are marked with an "f", since they are predicted to be functional,
whereas highly conserved (color grade: 9) and buried residues are marked with
an "s", since they are predicted to have an important structural role. Since
gaps are treated in the algorithm as missing data, not all sequences are taken
into account in each position. Hence the "MSA data" column indicates the total
number of non-gaped homologues that were calculated at each amino-acid site, out of all the homologue sequences.  Sites that contain less than
6 non-gaped homologue sequences (or 10% of the sequences, for more than 60
homologues) are considered as "insufficient data" sites, and the one letter
amino-acid code is colored in yellow in the graphic visualization output.



Graphic Visualization (Figure 1)



The ConSeq results are
automatically visualized on-line. 



The output consists of
three row batches. The uppermost row includes the sequence of the query protein with the evolutionary rates color-coded onto each site  (see Legend). The residues of the query sequence are always numbered starting from 1. The middle row lists the predicted burial status of the site (i.e. "b"-buried vs.
"e"-exposed). The lowermost row indicates residues predicted to be structurally
and functionally important, "s" and "f", respectively. 



Amino-acid sites
categorized as "Insufficient data" (see above) are colored in yellow,
indicating that the calculation for these sites was generated using
only a few of the homologous sequences. 



A legend below the result shows the conservation coloring scale and graphical description.



 



Figure 1: ConSeq
results: 











REFERENCES



 



1.     
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang,
J., Zhang, Z., Miller, W. and Lipman, D.J. Gapped BLAST and PSI-BLAST: a new
generation of protein database search programs. Nucleic Acids Res.
25:3389-3402, 1997.



2.     
Fariselli, P. and Casadio, R. RCNPRED: prediction of the
residue co-ordination numbers in proteins. Bioinformatics, 17, 202-204, 2001.



3.     
Glaser, F., Pupko, T., Paz, I., Bell, R.E., Bechor, D.,
Martz, E. and Ben-Tal N. ConSurf: Identification of functional regions in
proteins by surface-mapping of phylogenetic information. Bioinformatics
19:1-3, 2002.



4.     
Pollastri, G., Baldi, P., Fariselli, P. And Casadio, R.
Prediction of coordination number and relative solvent accessibility in
proteins. Proteins 47: 142-153, 2002.



    
 
  1. O'Donovan, C., Martin, M.J., Gattiker, A., Gasteiger, E., Bairoch, A. and Apweiler, R.
     High-quality protein knowledge resource: SWISS-PROT and TrEMBL Brief.
     Bioinform. 3, 275-284, 2002.
    2. 
 
  2. Pupko, T., Bell, R.E.,
     Mayrose, I., Glaser, F. and Ben-Tal N. Rate4Site: an algorithmic
     tool for the identification of functional regions in proteins by surface
     mapping of evolutionary determinants within their homologues. Bioinformatics
     18:S1-S7, 2002.
    3. 




7.      Saitou, N.
and Nei, M. The neighbor-joining method: A new method for reconstructing
phylogenetic trees. Mol. Biol. Evol. 4, 406-425. 1987.






8.      Thompson, J.D,
Higgins, D.G., Gibson, T.J. CLUSTALW: improving the sensitivity of progressive multiple
sequence alignment through sequence weighting, position-specific gap penalties
and weight matrix choice.Nucleic Acids Res. 22:4673-4680. 1994.





9.      Robert C. Edgar, MUSCLE: multiple sequence alignment with high accuracy and high throughput.
Nucleic Acids Research. 32:1792-1797. 2004. 
.





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