Selecting the option 'run ConSurf on subtree' will issue a new window with the new ConSurf run for the selected subtree sequences see example in Figure 3. The option to select sub tree and use it for a new ConSurf run is demonstrated. The subtree is marked by the red circle, and the selected sequences are highlighted with the black rectangle.
The analysis revealed, as expected, that the functional regions of this protein are highly conserved. In addition, other amino acid residues, which are in contact with the DNA i. ConSurf was also applied to nucleic acid sequences from yeast, which are the known binding sites of GAL4 and their adjacent neighborhood Figure 4. The amino-acids and the nucleotides are colored by their conservation grades using the color-coding bar, with turquoise-through-maroon indicating variable-through-conserved.
Positions, for which the inferred conservation level was assigned with low confidence, are marked with light yellow. The figure reveals that the functionally important regions on both the DNA and the protein are highly conserved. We synthesized a benchmark set of multiple sequence alignment, prepared using preset evolutionary rates, and examined the capacity of Rate4Site Mayrose et al.
In comparison, we examined the accuracy or reconstructing the rates using entropy based methods of estimating the evolutionary conservation, implemented by Capra and Singh, We compiled a set of multiple sequence alignments MSAs of homologous proteins by simulating the evolutionary process using INDELible Fletcher and Yang, and preset evolutionary trees.
Our set included 25 replicas of 16, 32, 64, , , and taxa, i. These ultrametric trees were generated using Mesquite Maddison and Maddison, , following a birth-death process with the default birth rate of 0. The tree height of each of these trees was then adjusted to be 0. Overall, these parameters resulted in MSAs which looks biologically reasonable based on visual comparison of the alignments' total length, number and length of indels. The average length of the simulated MSAs were The simulated dataset and all calculated results can be downloaded here.
Each simulated MSA was next provided as input for each of the conservation inference methodologies. It is noteworthy that in rate4site, the phylogenetic tree was inferred from the MSA rather than using the tree that was used in the simulations.
Spearman correlation between the true evolutionary rate used during the simulation of each position and the inferred conservation score was calculated for each of the methods.
The results are shown in table 1. The data in the table 1 shows that Rate4Site is far superior to all examined alternatives; the Spearman correlations between the true simulated rates and those inferred by Rate4Site are significantly higher than these of the other entropy based methods. The last row in the table includes comparison to rates derived from a set of highly similar sequences the influenza neuraminidase , as a particularly difficult test case.
Rate4Site is slightly more accurate than the alternatives also in this case. Table 1: Average Spearman correlation coefficients of different conservation methods and the true evolutionary rate. Number of sequences. Shannon entropy. Property entropy. Property relative entropy. Relative entropy. JS divergence. Sum of pairs. Neuraminidase sequences.
IEEE Trans. Nucleic Acids Res. Bioinformatics , 23 , Methods , 9 , Bioinformatics , 27 , Bioinformatics , 19 , Nature , , Bioinformatics , 8 , In, Munro,H. Bioinformatics , 22 , There are many different algorithms for searching sequence databases, but BLAST algorithms are some of the most popular, because of their speed.
BLAST searches begin with a query sequence that will be matched against sequence databases specified by the user. As the algorithms work through the data, they compute the probability that each potential match may have arisen by chance alone, which would not be consistent with an evolutionary relationship. Words above a threshold value for statistical significance are then used to search databases.
Because there are only four possible nucleotides in DNA, a sequence of this length would be expected to occur randomly once in every , or , nucleotides, which is far longer than any genome. Because proteins contain 20 different amino acids, a tripeptide sequence would be expected to arise randomly once in every tripeptides, which is longer than any protein.
Synonymous substitutions do not affect the function of a protein and would therefore not be selected against during evolution. The results obtained in a BLASTP search depend on the scoring matrix used to assign numerical values to different words. The BLOSUM62 matrix was developed by analyzing the frequencies of amino acid substitutions in clusters of related proteins. Investigators computationally determined the frequencies of all amino acid substitutions that had occurred in these conserved blocks of proteins.
The BLOSUM62 score for a particular substitution is a log-odds score that provides a measure of the biological probability of a substitution relative to the chance probability of the substitution.
For a substitution of amino acid i for amino acid j, the score is expressed:. The BLOSUM62 matrix on the following page is consistent with strong evolutionary pressure to conserve protein function.
As expected, the most common substitution for any amino acid is itself. Overall, positive scores shaded are less common than negative scores, suggesting that most substitutions negatively affect protein function.
The evolutionary tree, which was calculated by the server or uploaded by the user, is shown using an interactive Java applet written for that purpose. For proteins in which the 3D structure was not provided by the user, an up-to-date version of the Protein Data Bank 13 is searched for relevant homologues. If a structure of at least one homologous protein is available, the user may map the conservation scores on the structure.
This option should ease the procedure for the non-expert users, who may be unfamiliar with the 3D structure homologue. This option can also be useful for analyzing proteins that share the same sequence but differ in their 3D structure for example, two structures solved in different conformations or with different ligands.
The analysis revealed, as expected, that the functional regions of this protein are highly conserved. In addition, other amino acid residues, which are in contact with the DNA i. The amino-acids and the nucleotides are colored by their conservation grades using the color-coding bar, with turquoise-through-maroon indicating variable-through-conserved.
Positions, for which the inferred conservation level was assigned with low confidence, are marked with light yellow. The figure reveals that the functionally important regions on both the DNA and the protein are highly conserved. ConSurf was also applied to nucleic acid sequences from yeast, which are the known binding sites of GAL4 and their adjacent neighborhood Figure 2.
Despite increasing interest in the non-coding fraction of transcriptomes, the number, the level of conservation, and functions, if any, of many non-protein-coding transcripts remain to be discovered. However, it has already been shown that many of the non-coding sequences are connected to regulatory processes.
The new version of ConSurf offers estimations of the evolutionary rate for each position of nucleic acid sequences in the same manner used for amino acid residues. For that purpose, four evolutionary models were implemented in the Rate4Site program: i the Juke and Cantor 69 model JC69 , which assumes equal base frequencies and equal substitution rates The GTR parameters consist of an equilibrium base frequency vector, giving the frequency at which each base occurs at each site, and the rate matrix When enough data i.
However, the Tamura 92 model is recommended in cases in which the data are not sufficient for reliable estimation of the model parameters and thus it is the default option for analyzing nucleic acid sequences in ConSurf.
The LG substitution matrix, which incorporates variability of evolutionary rates across sites in the matrix estimation was shown to outperform other substitutions matrices for proteins The accuracy of conservation scores is directly influenced by the amount and quality of sequence data available in the MSA and the relatedness between the homologous sequences themselves and the sequence of interest. For example, using homologous sequences with different functions might blur the signal.
One of the important changes in the new version of ConSurf is the addition of a clear and intuitive interface that helps controlling which of the sequences are included in the analysis. These improvements include:. A variety of sequence databases. 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 resources.
Removing redundant sequences. The user can specify the level of redundant sequences for removal. Only one sequence the longest from each cluster is used for the analysis. 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 sequences found and the sequence of interest enables the user to filter out sequences that share significant alignment with the protein of interest, however, might have different function or structure.
Better alignments. Jobs have unique identifiers, which depending on the job type can be used in queries e. Job identifiers and the related data are kept for 7 days, and are then deleted. To add sequences to your alignment, a text box just after the alignment results allows you to do so, in FASTA format:. To rerun the alignment with fewer sequences, check the box for "Result info" under "Display", and scroll down to the bottom of the page.
Use the checkboxes to select the sequences you want to realign:.
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