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研究生:郭庭榕
研究生(外文):Ting-Rung Kuo
論文名稱:抗微生物肽及其功能類型辨識系統
論文名稱(外文):Identification of anti-microbial peptides and their functional types
指導教授:洪炯宗洪炯宗引用關係
指導教授(外文):Jorng-Tzong Horng
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:63
中文關鍵詞:微生物生物資訊機器學習
外文關鍵詞:microbialbioinformaticsmachine-learning
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由於抗生素的濫用,導致微生物的抗藥性迅速增加,使得較不易產生抗藥性的抗菌肽在治療醫學中的角色日趨重要。抗菌肽是先天性免疫系統的重要組成部分,且廣泛出現在大多數的生物體上。此外抗菌肽具有廣泛的抗菌活性,如對抗病毒、寄生蟲、細菌與真菌等等。然而針對同時區分抗菌肽多種功能類型的文獻較為缺乏,甚至鮮少研究進一步分析其相關特徵之重要性。因此本研究基於兩階段架構建立八個分類器辨識抗菌肽及其功能類型,並使用前向選擇演算法尋找重要特徵,其中,第一階段的抗菌肽分類器於測試集的曲面下面積為0.9894;第二階段的抗寄生蟲、抗病毒、抗癌症、哺乳動物細胞、抗真菌、抗革蘭氏陽細菌以及抗革蘭氏陰細菌分類器在獨立測試集的曲面下面積分別為0.7474、0.9397、0.8150、0.8515、0.8533、0.8725及0.8576。此外我們發現第一殘基的疏水性、正規化的范德瓦爾斯體積、極性、極化率、電荷、二級結構以及可溶性是分辨抗菌肽與非抗菌肽的重要特徵。除上述提到七種物理化學性質外,偽胺基酸組成亦為區分抗菌肽的之不同類型的重要的特徵。最後我們建構一個網站,提供肽序列之抗菌肽及其功能類型預測。
Owing to the abuse of antibiotics, the infection resistance of microbial pathogens against chemical antibiotics increases rapidly. Antimicrobial peptides (AMPs) are essential components of the innate immune system with the lower possibility on the emergence of resistance and produced by virtually all organisms known on earth, hence become the attractive candidates for development as therapeutics. AMPs are able to resist various pathogenic microorganisms, such as viruses, parasites, bacteria, and fungi. However, little research dedicates to differentiate the multiple functional types of AMPs simultaneously or even analyze those features that may highly related to distinguish them. In this study, we construct 8 classifiers under two-stage framework to identify the AMPs with their functional types. Moreover, we adopted forward selection strategy to find some important features that may associate with the functional types of AMPs. In the first stage, the resulting area under curves (AUC) of AMP classifier is 0.9894 on the testing set. In the second stage, the AUCs of parasitic, viral, cancer, mammalian, fungal, gram-positive bacterial and gram-negative bacterial are 0.7474, 0.9397, 0.8150, 0.8515, 0.8533, 0.8725 and 0.8576 on the independent testing set, respectably. Besides, we found that hydrophobicity, normalized van der Waals volume, polarity, polarizability, charge, secondary structure and solvent accessibility in the first residue were important patterns to identify AMPs and non-AMPs. In addition to these seven properties, pseudo amino acid composition was also the important factors to distinguish different functional types of AMPs. We developed a web-server called AMPfun to provide our classifiers for AMP and their functional types prediction.
中文摘要 i
Abstract ii
致謝 iii
Table of Contents iv
List of Figures v
List of Tables vi
Chapter 1. Introduction 1
1.1. Background 1
1.2. Related Works 3
1.3. Motivation 5
1.4. Goal 5
Chapter 2. Materials and Methods 6
2.1. Collection and Preprocessing of Dataset 6
2.2. Features Extraction 11
2.2.1. N-gram Detection 11
2.2.2. Motif Discovery 12
2.2.3. Binary Profiling of Positional Features 13
2.2.4. Composition of Various Features 13
2.2.5. Features Encoding by Physical-Chemical Properties 14
2.2.6. Forward Feature Selection Algorithm 15
2.3. Construction of Predictive Models 15
2.3.1. Decision Tree 16
2.3.2. Random Forest 17
2.3.3. Support Vector Machine 21
2.4. Evaluation Metrics 22
2.5. Implementation of Web-Based Prediction Tool 24
Chapter 3. Results 26
3.1. Sequence-Based Characterization AMPs 26
3.2. Performance of Classification between AMPs and non-AMPs 28
3.3. Sequence-Based Characterization Seven Functional Types of AMPs 32
3.4. Performance of Identifying AMPs and Their Functional Types 33
3.5. Comparison with Existing Tools 42
Chapter 4. Discussions and Conclusion 43
4.1. Discussions 43
4.2. Conclusion 47
References 49
1. Ventola, C.L., The antibiotic resistance crisis: part 1: causes and threats. Pharmacy and Therapeutics, 2015. 40(4): p. 277.
2. Harder, J. and J.-M. Schröder, Antimicrobial peptides: role in human health and disease. 2015: Springer.
3. De la Fuente-núñez, C., et al., Antimicrobial peptides: Role in human disease and potential as immunotherapies. Pharmacology & therapeutics, 2017. 178: p. 132-140.
4. Bahar, A.A. and D. Ren, Antimicrobial peptides. Pharmaceuticals, 2013. 6(12): p. 1543-1575.
5. Zhang, L.-J. and R.L. Gallo, Antimicrobial peptides. Current Biology, 2016. 26(1): p. R14-R19.
6. Jenssen, H., P. Hamill, and R.E. Hancock, Peptide antimicrobial agents. Clinical microbiology reviews, 2006. 19(3): p. 491-511.
7. Silva, O., et al., An anti-infective synthetic peptide with dual antimicrobial and immunomodulatory activities. Scientific reports, 2016. 6: p. 35465.
8. Anunthawan, T., et al., Cationic amphipathic peptides KT2 and RT2 are taken up into bacterial cells and kill planktonic and biofilm bacteria. Biochimica et Biophysica Acta (BBA)-Biomembranes, 2015. 1848(6): p. 1352-1358.
9. Haney, E.F., et al., High throughput screening methods for assessing antibiofilm and immunomodulatory activities of synthetic peptides. Peptides, 2015. 71: p. 276-285.
10. Bhadra, P., et al., AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Scientific reports, 2018. 8(1): p. 1697.
11. Chang, K.Y., et al., Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields. PloS one, 2015. 10(3): p. e0119490.
12. Wang, G., X. Li, and Z. Wang, APD3: the antimicrobial peptide database as a tool for research and education. Nucleic acids research, 2015. 44(D1): p. D1087-D1093.
13. Waghu, F.H., et al., CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides. Nucleic acids research, 2015. 44(D1): p. D1094-D1097.
14. Lee, H.-T., et al., A large-scale structural classification of antimicrobial peptides. BioMed research international, 2015. 2015.
15. Meher, P.K., et al., Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific reports, 2017. 7: p. 42362.
16. Thomas, S., et al., CAMP: a useful resource for research on antimicrobial peptides. Nucleic acids research, 2009. 38(suppl_1): p. D774-D780.
17. Fan, L., et al., DRAMP: a comprehensive data repository of antimicrobial peptides. Scientific reports, 2016. 6: p. 24482.
18. Qureshi, A., et al., AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses. Nucleic acids research, 2013. 42(D1): p. D1147-D1153.
19. Tyagi, A., et al., CancerPPD: a database of anticancer peptides and proteins. Nucleic acids research, 2014. 43(D1): p. D837-D843.
20. Mehta, D., et al., ParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structures. Database, 2014. 2014: p. bau051.
21. Thakur, N., A. Qureshi, and M. Kumar, AVPpred: collection and prediction of highly effective antiviral peptides. Nucleic acids research, 2012. 40(W1): p. W199-W204.
22. Manavalan, B., et al., MLACP: machine-learning-based prediction of anticancer peptides. Oncotarget, 2017. 8(44): p. 77121.
23. Agrawal, P., et al., In silico approach for prediction of antifungal peptides. Frontiers in microbiology, 2018. 9: p. 323.
24. Xiao, X., et al., iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical biochemistry, 2013. 436(2): p. 168-177.
25. Lin, W. and D. Xu, Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types. Bioinformatics, 2016. 32(24): p. 3745-3752.
26. Chen, W., et al., iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget, 2016. 7(13): p. 16895.
27. Lata, S., N.K. Mishra, and G.P. Raghava, AntiBP2: improved version of antibacterial peptide prediction. BMC bioinformatics, 2010. 11(1): p. S19.
28. Tyagi, A., et al., In silico models for designing and discovering novel anticancer peptides. Scientific reports, 2013. 3: p. 2984.
29. Consortium, U., UniProt: the universal protein knowledgebase. Nucleic acids research, 2016. 45(D1): p. D158-D169.
30. Li, W. and A. Godzik, Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 2006. 22(13): p. 1658-1659.
31. Vens, C., M.-N. Rosso, and E.G. Danchin, Identifying discriminative classification-based motifs in biological sequences. Bioinformatics, 2011. 27(9): p. 1231-1238.
32. Cao, D.-S., Q.-S. Xu, and Y.-Z. Liang, propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics, 2013. 29(7): p. 960-962.
33. Chou, K.C., Prediction of protein cellular attributes using pseudo‐amino acid composition. Proteins: Structure, Function, and Bioinformatics, 2001. 43(3): p. 246-255.
34. Pedregosa, F., et al., Scikit-learn: Machine learning in Python. Journal of machine learning research, 2011. 12(Oct): p. 2825-2830.
35. Han, J., J. Pei, and M. Kamber, Data mining: concepts and techniques. 2011: Elsevier.
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