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C-PAmP: large scale analysis and database construction containing high scoring computationally predicted antimicrobial peptides for all the available plant species.

Niarchou A, Alexandridou A, Athanasiadis E, Spyrou G - PLoS ONE (2013)

Bottom Line: We have compiled a major repository of predicted plant antimicrobial peptides using a highly performing classification algorithm.Our repository is accessible from the web and supports multiple querying options to optimise data retrieval.We hope it will greatly benefit drug design research by significantly limiting the range of plant peptides to be experimentally tested for antimicrobial activity.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Research Foundation of the Academy of Athens, Athens, Greece.

ABSTRACT

Background: Antimicrobial peptides are a promising alternative to conventional antibiotics. Plants are an important source of such peptides; their pharmacological properties are known since antiquity. Access to relevant information, however, is not straightforward, as there are practically no major repositories of experimentally validated and/or predicted plant antimicrobial peptides. PhytAMP is the only database dedicated to plant peptides with confirmed antimicrobial action, holding 273 entries. Data on such peptides can be otherwise retrieved from generic repositories.

Description: We present C-PAmP, a database of computationally predicted plant antimicrobial peptides. C-PAmP contains 15,174,905 peptides, 5-100 amino acids long, derived from 33,877 proteins of 2,112 plant species in UniProtKB/Swiss-Prot. Its web interface allows queries based on peptide/protein sequence, protein accession number and species. Users can view the corresponding predicted peptides along with their probability score, their classification according to the Collection of Anti-Microbial Peptides (CAMP), and their PhytAMP id where applicable. Moreover, users can visualise protein regions with a high concentration of predicted antimicrobial peptides. In order to identify potential antimicrobial peptides we used a classification algorithm, based on a modified version of the pseudo amino acid concept. The classifier tested all subsequences ranging from 5 to 100 amino acids of the plant proteins in UniProtKB/Swiss-Prot and stored those classified as antimicrobial with a high probability score (>90%). Its performance measures across a 10-fold cross-validation are more than satisfactory (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90) and it succeeded in classifying 99.5% of the PhytAMP peptides correctly.

Conclusions: We have compiled a major repository of predicted plant antimicrobial peptides using a highly performing classification algorithm. Our repository is accessible from the web and supports multiple querying options to optimise data retrieval. We hope it will greatly benefit drug design research by significantly limiting the range of plant peptides to be experimentally tested for antimicrobial activity.

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Snapshots from the C-PAmP Database.
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pone-0079728-g002: Snapshots from the C-PAmP Database.

Mentions: As previously mentioned, we created a database in Apache CouchDB format (see Figure 2). Our database contains 15,174,905 antimicrobial sequences, whose probability of being antimicrobial is at least 90%. These sequences are derived from 33,877 proteins found in 2,112 plant species. It is worth noting that since proteins were scanned using a sliding window, many of these peptides overlap, or are subsets of one-another, so the number of unique subsequences of proteins is significantly lower.


C-PAmP: large scale analysis and database construction containing high scoring computationally predicted antimicrobial peptides for all the available plant species.

Niarchou A, Alexandridou A, Athanasiadis E, Spyrou G - PLoS ONE (2013)

Snapshots from the C-PAmP Database.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3823563&req=5

pone-0079728-g002: Snapshots from the C-PAmP Database.
Mentions: As previously mentioned, we created a database in Apache CouchDB format (see Figure 2). Our database contains 15,174,905 antimicrobial sequences, whose probability of being antimicrobial is at least 90%. These sequences are derived from 33,877 proteins found in 2,112 plant species. It is worth noting that since proteins were scanned using a sliding window, many of these peptides overlap, or are subsets of one-another, so the number of unique subsequences of proteins is significantly lower.

Bottom Line: We have compiled a major repository of predicted plant antimicrobial peptides using a highly performing classification algorithm.Our repository is accessible from the web and supports multiple querying options to optimise data retrieval.We hope it will greatly benefit drug design research by significantly limiting the range of plant peptides to be experimentally tested for antimicrobial activity.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Research Foundation of the Academy of Athens, Athens, Greece.

ABSTRACT

Background: Antimicrobial peptides are a promising alternative to conventional antibiotics. Plants are an important source of such peptides; their pharmacological properties are known since antiquity. Access to relevant information, however, is not straightforward, as there are practically no major repositories of experimentally validated and/or predicted plant antimicrobial peptides. PhytAMP is the only database dedicated to plant peptides with confirmed antimicrobial action, holding 273 entries. Data on such peptides can be otherwise retrieved from generic repositories.

Description: We present C-PAmP, a database of computationally predicted plant antimicrobial peptides. C-PAmP contains 15,174,905 peptides, 5-100 amino acids long, derived from 33,877 proteins of 2,112 plant species in UniProtKB/Swiss-Prot. Its web interface allows queries based on peptide/protein sequence, protein accession number and species. Users can view the corresponding predicted peptides along with their probability score, their classification according to the Collection of Anti-Microbial Peptides (CAMP), and their PhytAMP id where applicable. Moreover, users can visualise protein regions with a high concentration of predicted antimicrobial peptides. In order to identify potential antimicrobial peptides we used a classification algorithm, based on a modified version of the pseudo amino acid concept. The classifier tested all subsequences ranging from 5 to 100 amino acids of the plant proteins in UniProtKB/Swiss-Prot and stored those classified as antimicrobial with a high probability score (>90%). Its performance measures across a 10-fold cross-validation are more than satisfactory (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90) and it succeeded in classifying 99.5% of the PhytAMP peptides correctly.

Conclusions: We have compiled a major repository of predicted plant antimicrobial peptides using a highly performing classification algorithm. Our repository is accessible from the web and supports multiple querying options to optimise data retrieval. We hope it will greatly benefit drug design research by significantly limiting the range of plant peptides to be experimentally tested for antimicrobial activity.

Show MeSH