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Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity.

Ng XY, Rosdi BA, Shahrudin S - Biomed Res Int (2015)

Bottom Line: Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics.However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time.Therefore, a computational tool for AMPs prediction is needed to resolve this problem.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical & Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia.

ABSTRACT
This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.

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The performance comparison of previously proposed methods with the proposed algorithm on independent test using Wang test set.
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Related In: Results  -  Collection


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fig6: The performance comparison of previously proposed methods with the proposed algorithm on independent test using Wang test set.

Mentions: Besides the jackknife test, the independent test was also used to evaluate the performance of the proposed algorithm. An independent test is used for demonstrating the performance of a predictor for practical application [24]. Table 7 and Figure 6 show the results of the independent test for the proposed algorithm and previously proposed methods from CAMP and Wang et al. [9]. As stated in [9], the normal training set is used to perform the independent test because all training data must be used in order to have a better performance upon testing. Per Table 7, the proposed algorithm in this project had the highest sensitivity at 87.59%. This results indicate that the proposed algorithm in this project is suitable to be used as an AMPs predictor.


Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity.

Ng XY, Rosdi BA, Shahrudin S - Biomed Res Int (2015)

The performance comparison of previously proposed methods with the proposed algorithm on independent test using Wang test set.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352747&req=5

fig6: The performance comparison of previously proposed methods with the proposed algorithm on independent test using Wang test set.
Mentions: Besides the jackknife test, the independent test was also used to evaluate the performance of the proposed algorithm. An independent test is used for demonstrating the performance of a predictor for practical application [24]. Table 7 and Figure 6 show the results of the independent test for the proposed algorithm and previously proposed methods from CAMP and Wang et al. [9]. As stated in [9], the normal training set is used to perform the independent test because all training data must be used in order to have a better performance upon testing. Per Table 7, the proposed algorithm in this project had the highest sensitivity at 87.59%. This results indicate that the proposed algorithm in this project is suitable to be used as an AMPs predictor.

Bottom Line: Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics.However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time.Therefore, a computational tool for AMPs prediction is needed to resolve this problem.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical & Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia.

ABSTRACT
This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.

Show MeSH