Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity.
Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity.
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DOI：
10.1155/2015/212715
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发表时间：
2015
影响因子：
--
通讯作者：
Shahrudin S
中科院分区：
文献类型：
--
作者：
Ng XY;Rosdi BA;Shahrudin S
这项研究试图建立一种新的方法来预测对免疫系统重要的抗菌肽(AMP)。最近，研究人员对设计基于AMPS的替代药物很感兴趣，因为他们发现大量细菌菌株已经对可用的抗生素产生了抗药性。然而，研究人员在AMPS的设计过程中遇到了障碍，因为从蛋白质序列中提取AMP的实验成本很高，需要很长的设置时间。因此，需要一种用于AMPS预测的计算工具来解决这一问题。本文提出了一种将序列比对和支持向量机相结合的序列比对预测算法--(支持向量机-)LZ复杂性配对算法。实验结果表明，当使用训练集中的所有序列时，该算法的灵敏度分别为95.28%和87.59%，而当仅使用相似度小于70%的序列时，分别获得88.74%和78.70%的灵敏度。应用所提出的算法可以使研究人员以更高的灵敏度从未知的蛋白质肽序列中有效地预测AMP。
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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影响因子：
2.5
作者：
Sang, Yongming;Blecha, Frank
通讯作者：
Blecha, Frank
影响因子：
3.5
作者：
Liu, Taigang;Zheng, Xiaoqi;Wang, Jun
通讯作者：
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DOI：
10.1093/protein/12.2.107
发表时间：
1999-02-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
Chou, KC;Elrod, DW
通讯作者：
Elrod, DW
影响因子：
3.7
作者：
Muh HC;Tong JC;Tammi MT
通讯作者：
Tammi MT
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影响因子：
2.9
作者：
Xiao, Xuan;Wang, Pu;Chou, Kuo-Chen
通讯作者：
Chou, Kuo-Chen