Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities
Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-04-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.669328/full |
| _version_ | 1818724913814437888 |
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| author | Gai-Fang Dong Lei Zheng Sheng-Hui Huang Jing Gao Yong-Chun Zuo |
| author_facet | Gai-Fang Dong Lei Zheng Sheng-Hui Huang Jing Gao Yong-Chun Zuo |
| author_sort | Gai-Fang Dong |
| collection | DOAJ |
| description | Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu.cn/iampraac. |
| first_indexed | 2024-12-17T21:33:58Z |
| format | Article |
| id | doaj.art-0890b15c05024d0e95a113eaef69dbb2 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-17T21:33:58Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-0890b15c05024d0e95a113eaef69dbb22022-12-21T21:31:48ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-04-011210.3389/fgene.2021.669328669328Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional ActivitiesGai-Fang Dong0Lei Zheng1Sheng-Hui Huang2Jing Gao3Yong-Chun Zuo4Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, ChinaInner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, ChinaThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, ChinaAntimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu.cn/iampraac.https://www.frontiersin.org/articles/10.3389/fgene.2021.669328/fullantimicrobial peptideidentificationreduced amino acid alphabettwo-stage classifiersupporting vector machine |
| spellingShingle | Gai-Fang Dong Lei Zheng Sheng-Hui Huang Jing Gao Yong-Chun Zuo Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities Frontiers in Genetics antimicrobial peptide identification reduced amino acid alphabet two-stage classifier supporting vector machine |
| title | Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities |
| title_full | Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities |
| title_fullStr | Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities |
| title_full_unstemmed | Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities |
| title_short | Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities |
| title_sort | amino acid reduction can help to improve the identification of antimicrobial peptides and their functional activities |
| topic | antimicrobial peptide identification reduced amino acid alphabet two-stage classifier supporting vector machine |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.669328/full |
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