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AlignMiner: a Web-based tool for detection of divergent regions in multiple sequence alignments of conserved sequences.

Guerrero D, Bautista R, Villalobos DP, Cantón FR, Claros MG - Algorithms Mol Biol (2010)

Bottom Line: It accepts alignments (protein or nucleic acid) obtained using any of a variety of algorithms, which does not appear to have a significant impact on the final results.Users do not need to wait until execution is complete and can.even inspect their results on a different computer.In silico and experimental proof-of-concept cases have shown that AlignMiner can be successfully used to designing specific polymerase chain reaction primers as well as potential epitopes for antibodies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Plataforma Andaluza de Bioinformática (Universidad de Málaga), Severo Ochoa, 34, 29590 Málaga, Spain. claros@uma.es.

ABSTRACT

Background: Multiple sequence alignments are used to study gene or protein function, phylogenetic relations, genome evolution hypotheses and even gene polymorphisms. Virtually without exception, all available tools focus on conserved segments or residues. Small divergent regions, however, are biologically important for specific quantitative polymerase chain reaction, genotyping, molecular markers and preparation of specific antibodies, and yet have received little attention. As a consequence, they must be selected empirically by the researcher. AlignMiner has been developed to fill this gap in bioinformatic analyses.

Results: AlignMiner is a Web-based application for detection of conserved and divergent regions in alignments of conserved sequences, focusing particularly on divergence. It accepts alignments (protein or nucleic acid) obtained using any of a variety of algorithms, which does not appear to have a significant impact on the final results. AlignMiner uses different scoring methods for assessing conserved/divergent regions, Entropy being the method that provides the highest number of regions with the greatest length, and Weighted being the most restrictive. Conserved/divergent regions can be generated either with respect to the consensus sequence or to one master sequence. The resulting data are presented in a graphical interface developed in AJAX, which provides remarkable user interaction capabilities. Users do not need to wait until execution is complete and can.even inspect their results on a different computer. Data can be downloaded onto a user disk, in standard formats. In silico and experimental proof-of-concept cases have shown that AlignMiner can be successfully used to designing specific polymerase chain reaction primers as well as potential epitopes for antibodies. Primer design is assisted by a module that deploys several oligonucleotide parameters for designing primers "on the fly".

Conclusions: AlignMiner can be used to reliably detect divergent regions via several scoring methods that provide different levels of selectivity. Its predictions have been verified by experimental means. Hence, it is expected that its usage will save researchers' time and ensure an objective selection of the best-possible divergent region when closely related sequences are analysed. AlignMiner is freely available at http://www.scbi.uma.es/alignminer.

No MeSH data available.


Correlation between the most divergent amino acid sequences and antigenicity of the AtGS1 protein MSA. (A) Similarity plot obtained using the Entropy method; the most divergent regions being are highlighted. (B) Aligned sequences for the two divergent regions together (underlined in black) and their score in relation to other divergent regions. (C) Localisation of each divergent region in the alignment where: (i) nucleotides in bold are the predicted epitopes for B-cells; (ii) an "e" denotes predicted solvent accessibility for this position; and (iii) red-boxed amino acids correspond to the sequence of the matching divergent region. It is clearly seen that divergent sequences overlap with the predicted epitopes and the solvent-accessible amino acids.
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Figure 7: Correlation between the most divergent amino acid sequences and antigenicity of the AtGS1 protein MSA. (A) Similarity plot obtained using the Entropy method; the most divergent regions being are highlighted. (B) Aligned sequences for the two divergent regions together (underlined in black) and their score in relation to other divergent regions. (C) Localisation of each divergent region in the alignment where: (i) nucleotides in bold are the predicted epitopes for B-cells; (ii) an "e" denotes predicted solvent accessibility for this position; and (iii) red-boxed amino acids correspond to the sequence of the matching divergent region. It is clearly seen that divergent sequences overlap with the predicted epitopes and the solvent-accessible amino acids.

Mentions: Identification of divergent regions among proteins can also be performed. It may be hypothesised that the most divergent regions could be epitopes for production of specific, even monoclonal, antibodies that can serve to distinguish very closely-related protein isoforms. As an example, the five glutamine synthetase (GS1) enzyme isoforms of A. thaliana (AtGS1, Table 1) were aligned with MultAlin using default parameters. The Entropy scoring method was used since it identified the longest divergent regions (Figure 4). The resulting divergent regions were sorted by score and the best ones were selected (Figure 7B). Each GS1 sequence was additionally inspected for solvent-accessible positions and highly antigenic regions using the SCRATCH Protein Predictor Web suite [38]. It appeared that the most highly-divergent Entropy-derived regions corresponded to the most solvent-accessible and most antigenic portions of the protein (Figure 7C). These sequences can then be used to challenge mice or rabbits and obtain specific antibodies against any one of the aligned sequences.


AlignMiner: a Web-based tool for detection of divergent regions in multiple sequence alignments of conserved sequences.

Guerrero D, Bautista R, Villalobos DP, Cantón FR, Claros MG - Algorithms Mol Biol (2010)

Correlation between the most divergent amino acid sequences and antigenicity of the AtGS1 protein MSA. (A) Similarity plot obtained using the Entropy method; the most divergent regions being are highlighted. (B) Aligned sequences for the two divergent regions together (underlined in black) and their score in relation to other divergent regions. (C) Localisation of each divergent region in the alignment where: (i) nucleotides in bold are the predicted epitopes for B-cells; (ii) an "e" denotes predicted solvent accessibility for this position; and (iii) red-boxed amino acids correspond to the sequence of the matching divergent region. It is clearly seen that divergent sequences overlap with the predicted epitopes and the solvent-accessible amino acids.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902484&req=5

Figure 7: Correlation between the most divergent amino acid sequences and antigenicity of the AtGS1 protein MSA. (A) Similarity plot obtained using the Entropy method; the most divergent regions being are highlighted. (B) Aligned sequences for the two divergent regions together (underlined in black) and their score in relation to other divergent regions. (C) Localisation of each divergent region in the alignment where: (i) nucleotides in bold are the predicted epitopes for B-cells; (ii) an "e" denotes predicted solvent accessibility for this position; and (iii) red-boxed amino acids correspond to the sequence of the matching divergent region. It is clearly seen that divergent sequences overlap with the predicted epitopes and the solvent-accessible amino acids.
Mentions: Identification of divergent regions among proteins can also be performed. It may be hypothesised that the most divergent regions could be epitopes for production of specific, even monoclonal, antibodies that can serve to distinguish very closely-related protein isoforms. As an example, the five glutamine synthetase (GS1) enzyme isoforms of A. thaliana (AtGS1, Table 1) were aligned with MultAlin using default parameters. The Entropy scoring method was used since it identified the longest divergent regions (Figure 4). The resulting divergent regions were sorted by score and the best ones were selected (Figure 7B). Each GS1 sequence was additionally inspected for solvent-accessible positions and highly antigenic regions using the SCRATCH Protein Predictor Web suite [38]. It appeared that the most highly-divergent Entropy-derived regions corresponded to the most solvent-accessible and most antigenic portions of the protein (Figure 7C). These sequences can then be used to challenge mice or rabbits and obtain specific antibodies against any one of the aligned sequences.

Bottom Line: It accepts alignments (protein or nucleic acid) obtained using any of a variety of algorithms, which does not appear to have a significant impact on the final results.Users do not need to wait until execution is complete and can.even inspect their results on a different computer.In silico and experimental proof-of-concept cases have shown that AlignMiner can be successfully used to designing specific polymerase chain reaction primers as well as potential epitopes for antibodies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Plataforma Andaluza de Bioinformática (Universidad de Málaga), Severo Ochoa, 34, 29590 Málaga, Spain. claros@uma.es.

ABSTRACT

Background: Multiple sequence alignments are used to study gene or protein function, phylogenetic relations, genome evolution hypotheses and even gene polymorphisms. Virtually without exception, all available tools focus on conserved segments or residues. Small divergent regions, however, are biologically important for specific quantitative polymerase chain reaction, genotyping, molecular markers and preparation of specific antibodies, and yet have received little attention. As a consequence, they must be selected empirically by the researcher. AlignMiner has been developed to fill this gap in bioinformatic analyses.

Results: AlignMiner is a Web-based application for detection of conserved and divergent regions in alignments of conserved sequences, focusing particularly on divergence. It accepts alignments (protein or nucleic acid) obtained using any of a variety of algorithms, which does not appear to have a significant impact on the final results. AlignMiner uses different scoring methods for assessing conserved/divergent regions, Entropy being the method that provides the highest number of regions with the greatest length, and Weighted being the most restrictive. Conserved/divergent regions can be generated either with respect to the consensus sequence or to one master sequence. The resulting data are presented in a graphical interface developed in AJAX, which provides remarkable user interaction capabilities. Users do not need to wait until execution is complete and can.even inspect their results on a different computer. Data can be downloaded onto a user disk, in standard formats. In silico and experimental proof-of-concept cases have shown that AlignMiner can be successfully used to designing specific polymerase chain reaction primers as well as potential epitopes for antibodies. Primer design is assisted by a module that deploys several oligonucleotide parameters for designing primers "on the fly".

Conclusions: AlignMiner can be used to reliably detect divergent regions via several scoring methods that provide different levels of selectivity. Its predictions have been verified by experimental means. Hence, it is expected that its usage will save researchers' time and ensure an objective selection of the best-possible divergent region when closely related sequences are analysed. AlignMiner is freely available at http://www.scbi.uma.es/alignminer.

No MeSH data available.