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AntiBP2: improved version of antibacterial peptide prediction

BACKGROUND: Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antiba...

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Autores principales: Lata, Sneh, Mishra, Nitish K, Raghava, Gajendra PS
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3009489/
https://www.ncbi.nlm.nih.gov/pubmed/20122190
http://dx.doi.org/10.1186/1471-2105-11-S1-S19
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author Lata, Sneh
Mishra, Nitish K
Raghava, Gajendra PS
author_facet Lata, Sneh
Mishra, Nitish K
Raghava, Gajendra PS
author_sort Lata, Sneh
collection PubMed
description BACKGROUND: Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using Support Vector Machine (SVM). RESULTS: During analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the N and C terminus. These observation and increased data of antibacterial peptide in APD encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. First, the binary patterns of the 15 N terminus residues were used for predicting antibacterial peptide using SVM and achieved accuracy of 85.46% with 0.705 Mathew's Correlation Coefficient (MCC). Then we used the binary pattern of 15 C terminus residues and achieved accuracy of 85.05% with 0.701 MCC, latter on we developed prediction method by combining N & C terminus and achieved an accuracy of 91.64% with 0.831 MCC. Finally we developed SVM based model using amino acid composition of whole peptide and achieved 92.14% accuracy with MCC 0.843. In this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. We further classify antibacterial peptides according to their sources and achieved an overall accuracy of 98.95%. We further classify antibacterial peptides in their respective family and got a satisfactory result. CONCLUSION: Among antibacterial peptides, there is preference for certain residues at N and C terminus, which helps to discriminate them from non-antibacterial peptides. Amino acid composition of antibacterial peptides helps to demarcate them from non-antibacterial peptide and their further classification in source and family. Antibp2 will be helpful in discovering efficacious antibacterial peptide, which we hope will be helpful against antibiotics resistant bacteria. We also developed user friendly web server for the biological community.
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spelling pubmed-30094892010-12-23 AntiBP2: improved version of antibacterial peptide prediction Lata, Sneh Mishra, Nitish K Raghava, Gajendra PS BMC Bioinformatics Research BACKGROUND: Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using Support Vector Machine (SVM). RESULTS: During analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the N and C terminus. These observation and increased data of antibacterial peptide in APD encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. First, the binary patterns of the 15 N terminus residues were used for predicting antibacterial peptide using SVM and achieved accuracy of 85.46% with 0.705 Mathew's Correlation Coefficient (MCC). Then we used the binary pattern of 15 C terminus residues and achieved accuracy of 85.05% with 0.701 MCC, latter on we developed prediction method by combining N & C terminus and achieved an accuracy of 91.64% with 0.831 MCC. Finally we developed SVM based model using amino acid composition of whole peptide and achieved 92.14% accuracy with MCC 0.843. In this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. We further classify antibacterial peptides according to their sources and achieved an overall accuracy of 98.95%. We further classify antibacterial peptides in their respective family and got a satisfactory result. CONCLUSION: Among antibacterial peptides, there is preference for certain residues at N and C terminus, which helps to discriminate them from non-antibacterial peptides. Amino acid composition of antibacterial peptides helps to demarcate them from non-antibacterial peptide and their further classification in source and family. Antibp2 will be helpful in discovering efficacious antibacterial peptide, which we hope will be helpful against antibiotics resistant bacteria. We also developed user friendly web server for the biological community. BioMed Central 2010-01-18 /pmc/articles/PMC3009489/ /pubmed/20122190 http://dx.doi.org/10.1186/1471-2105-11-S1-S19 Text en Copyright ©2010 Lata et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Lata, Sneh
Mishra, Nitish K
Raghava, Gajendra PS
AntiBP2: improved version of antibacterial peptide prediction
title AntiBP2: improved version of antibacterial peptide prediction
title_full AntiBP2: improved version of antibacterial peptide prediction
title_fullStr AntiBP2: improved version of antibacterial peptide prediction
title_full_unstemmed AntiBP2: improved version of antibacterial peptide prediction
title_short AntiBP2: improved version of antibacterial peptide prediction
title_sort antibp2: improved version of antibacterial peptide prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3009489/
https://www.ncbi.nlm.nih.gov/pubmed/20122190
http://dx.doi.org/10.1186/1471-2105-11-S1-S19
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AT raghavagajendraps antibp2improvedversionofantibacterialpeptideprediction