Prediction of antimicrobial peptides based on sequence alignment and secondary structure sequence and segment sequence
| dc.contributor.author | Soh Meng Wah | |
| dc.date.accessioned | 2021-04-16T07:18:58Z | |
| dc.date.available | 2021-04-16T07:18:58Z | |
| dc.date.issued | 2015-08-01 | |
| dc.description.abstract | Antimicrobial peptides (AMPs) are natural peptides that are important for immune system. Researchers are interested in designing alternative drugs with AMPs because more bacteria are becoming resistant to the available antibiotics. However, the experiments to extract AMP from protein sequences are time consuming and costly. Thus, a computational tool with more effective and accurately predicting novel AMPs is highly demanded to provide more candidates and useful insights for drug design. In this study, a new algorithm is proposed as a computational tool by integrating the sequence alignment method and the secondary structure sequence (SSS) and segment sequence (SS). The sequence alignment is accomplished by the classification of test sequences based on the maximum high-scoring segment pairs (HSPs) score predicted by Basic Local Alignment Search Tool for protein (BLASTP). The results of sequence alignment phase are in 91.02% for normal dataset, 80.88% on <0.7 sequence similarity train set and 96.02% for CAMP dataset. Sequence alignment method is not able to predict all sequences and the unpredicted sequences is then predicted by utilizing the SSS and SS features. Feature extraction and feature selection is performed to obtain the features. These features are used to train the SVM model which is then be used to classify the sequences to whether it is AMP or non-AMP. The overall results of independent test is 83.27% for normal dataset, 71.83% for sequence with <0.7 similarity dataset and 91.49% for CAMP dataset. In comparison of second phase with past research that combines with sequence alignment method, this research has relatively low yield (<27%) contributed by the prediction utilizing SSS and SS features only. This indicates that the proposed algorithm is not suitable to be used as AMPs predictor. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/12912 | |
| dc.language.iso | en | en_US |
| dc.title | Prediction of antimicrobial peptides based on sequence alignment and secondary structure sequence and segment sequence | en_US |
| dc.type | Thesis | en_US |
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