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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 flowchart of the SVM-LZ complexity pairwise algorithm for stage 2.
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Related In: Results  -  Collection


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fig2: The flowchart of the SVM-LZ complexity pairwise algorithm for stage 2.

Mentions: Support vector machines- (SVMs-) pairwise algorithm was introduced in [19] with the aim of detecting remote protein evolutionary and structural relationships. This algorithm is the combination of the pairwise sequence similarity algorithm using BLAST and SVM classification. In this paper, a new concept of SVM-LZ complexity pairwise algorithm has been proposed. The SVM-LZ complexity pairwise algorithm is the integration of LZ complexity algorithm [17] and SVM-pairwise algorithm. LZ complexity algorithm is implemented to compute the pairwise similarity scores. Based on LZ complexity pairwise similarity scores, SVM classification is performed to predict AMPs sequences. In this study, the SVM-LZ complexity pairwise algorithm is implemented on those test sequences that cannot be predicted by the sequence alignment method. The flowchart of the SVM-LZ complexity pairwise algorithm is shown in Figure 2. Generally, this algorithm can be categorized into two substages, the generation of LZ complexity pairwise similarity scores as feature vectors and the prediction based on SVM classification.


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 flowchart of the SVM-LZ complexity pairwise algorithm for stage 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: The flowchart of the SVM-LZ complexity pairwise algorithm for stage 2.
Mentions: Support vector machines- (SVMs-) pairwise algorithm was introduced in [19] with the aim of detecting remote protein evolutionary and structural relationships. This algorithm is the combination of the pairwise sequence similarity algorithm using BLAST and SVM classification. In this paper, a new concept of SVM-LZ complexity pairwise algorithm has been proposed. The SVM-LZ complexity pairwise algorithm is the integration of LZ complexity algorithm [17] and SVM-pairwise algorithm. LZ complexity algorithm is implemented to compute the pairwise similarity scores. Based on LZ complexity pairwise similarity scores, SVM classification is performed to predict AMPs sequences. In this study, the SVM-LZ complexity pairwise algorithm is implemented on those test sequences that cannot be predicted by the sequence alignment method. The flowchart of the SVM-LZ complexity pairwise algorithm is shown in Figure 2. Generally, this algorithm can be categorized into two substages, the generation of LZ complexity pairwise similarity scores as feature vectors and the prediction based on SVM classification.

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