中文版 | English


Alternative Title
Name pinyin
GUAN Shenghui
School number
0710 生物学
Subject category of dissertation
07 理学
Mentor unit
Publication Years
Submission date
Place of Publication




Training classes
Enrollment Year
Year of Degree Awarded
References List

[1] DADGOSTAR P. Antimicrobial Resistance: Implications and Costs [J]. Infect Drug Resist, 2019, 12: 3903-10.
[2] JIM O N. Tackling drug-resistant infections globally: final report and recommendations [J]. 2016.
[3] TURNER N A, SHARMA-KUINKEL B K, MASKARINEC S A, et al. Methicillin-resistant Staphylococcus aureus: an overview of basic and clinical research [J]. Nature Reviews Microbiology, 2019, 17(4): 203-18.
[4] SPOHN R, DARUKA L, LÁZÁR V, et al. Integrated evolutionary analysis reveals antimicrobial peptides with limited resistance [J]. Nature communications, 2019, 10(1): 4538.
[5] LAU J L, DUNN M K. Therapeutic peptides: Historical perspectives, current development trends, and future directions [J]. Bioorganic & Medicinal Chemistry, 2018, 26(10): 2700-7.
[6] GOMES B, AUGUSTO M T, FELÍCIO M R, et al. Designing improved active peptides for therapeutic approaches against infectious diseases [J]. Biotechnology Advances, 2018, 36(2): 415-29.
[7] MARR A K, GOODERHAM W J, HANCOCK R E W. Antibacterial peptides for therapeutic use: obstacles and realistic outlook [J]. Current Opinion in Pharmacology, 2006, 6(5): 468-72.
[8] MAHLAPUU M, HÅKANSSON J, RINGSTAD L, et al. Antimicrobial Peptides: An Emerging Category of Therapeutic Agents [J]. Front Cell Infect Microbiol, 2016, 6: 194.
[9] JOHN JUMPER R E, ALEXANDER PRITZEL. AlphaFold2: High Accuracy Protein Structure Prediction Using Deep Learning [M]. 2020.
[10] HALL C W, MAH T-F. Molecular mechanisms of biofilm-based antibiotic resistance and tolerance in pathogenic bacteria [J]. FEMS Microbiology Reviews, 2017, 41(3): 276-301.
[11] HANEY E F, TRIMBLE M J, CHENG J T, et al. Critical Assessment of Methods to Quantify Biofilm Growth and Evaluate Antibiofilm Activity of Host Defence Peptides [J]. Biomolecules, 2018, 8(2): 29.
[12] BJARNSHOLT T, CIOFU O, MOLIN S, et al. Applying insights from biofilm biology to drug development — can a new approach be developed? [J]. Nature Reviews Drug Discovery, 2013, 12(10): 791-808.
[13] BOMAN H G, AGERBERTH B, BOMAN A. Mechanisms of action on Escherichia coli of cecropin P1 and PR-39, two antibacterial peptides from pig intestine [J]. Infect Immun, 1993, 61(7): 2978-84.
[14] 陈红伟, 张阳, 程鹏, 等. 抗生物膜肽研究进展 [J]. 生物技术通报, 2021, 37(2): 216-23.
[15] HUAN Y, KONG Q, MOU H, et al. Antimicrobial Peptides: Classification, Design, Application and Research Progress in Multiple Fields [J]. Frontiers in Microbiology, 2020, 11(2559).
[16] 苟萍, 唐馨, 毛新芳, 等. 抗菌肽的研究现状和挑战 [J]. 中国生物工程杂志, 2019, 39(8): 86-94.
[17] 温赛, 刘怀然, 韩煦, 等. 综述人工合成型抗菌肽及其药学应用研究进展 [J]. 中国生物工程杂志, 2016, 36(8): 89-98.
[18] WANG G, LI X, WANG Z. APD3: the antimicrobial peptide database as a tool for research and education [J]. Nucleic Acids Research, 2015, 44(D1): D1087-D93.
[19] JHONG J-H, CHI Y-H, LI W-C, et al. dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data [J]. Nucleic Acids Research, 2019, 47(D1): D285-D97.
[20] HAMMAMI R, ZOUHIR A, LE LAY C, et al. BACTIBASE second release: a database and tool platform for bacteriocin characterization [J]. BMC microbiology, 2010, 10: 22.
[21] WANG C K L, KAAS Q, CHICHE L, et al. CyBase: a database of cyclic protein sequences and structures, with applications in protein discovery and engineering [J]. Nucleic acids research, 2008, 36(Database issue): D206-D10.
[22] HAMMAMI R, BEN HAMIDA J, VERGOTEN G, et al. PhytAMP: a database dedicated to antimicrobial plant peptides [J]. Nucleic acids research, 2009, 37(Database issue): D963-D8.
[23] WHITMORE L, WALLACE B A. The Peptaibol Database: a database for sequences and structures of naturally occurring peptaibols [J]. Nucleic acids research, 2004, 32(Database issue): D593-D4.
[24] WU H, LU H, HUANG J, et al. EnzyBase: a novel database for enzybiotic studies [J]. BMC Microbiology, 2012, 12(1): 54.
[25] NOVKOVIĆ M, SIMUNIĆ J, BOJOVIĆ V, et al. DADP: the database of anuran defense peptides [J]. Bioinformatics, 2012, 28(10): 1406-7.
[26] QURESHI A, THAKUR N, TANDON H, et al. AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses [J]. Nucleic acids research, 2014, 42(Database issue): D1147-D53.
[27] PIRTSKHALAVA M, AMSTRONG A A, GRIGOLAVA M, et al. DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics [J]. Nucleic acids research, 2021, 49(D1): D288-D97.
[28] ZHAO X, WU H, LU H, et al. LAMP: A Database Linking Antimicrobial Peptides [J]. PLOS ONE, 2013, 8(6): e66557.
[29] PIOTTO S P, SESSA L, CONCILIO S, et al. YADAMP: yet another database of antimicrobial peptides [J]. International Journal of Antimicrobial Agents, 2012, 39(4): 346-51.
[30] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative Adversarial Nets [Z]. NIPS'14. MIT Press. 2014: 2672–80
[31] KINGMA D P, WELLING M. Auto-encoding variational bayes [J]. arXiv preprint arXiv:13126114, 2013.
[32] SHI X, CHEN Z, WANG H, et al. Convolutional LSTM Network: a machine learning approach for precipitation nowcasting [Z]. Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. Montreal, Canada; MIT Press. 2015: 802–10
[33] MIRZA M, OSINDERO S. Conditional Generative Adversarial Nets [J]. arXiv e-prints, 2014: arXiv:1411.784.
[34] TONG X C, LIU X H, TAN X Q, et al. Generative Models for De Novo Drug Design [J]. Journal of Medicinal Chemistry, 2021, 64(19): 14011-27.
[35] GHISLIERI M, CERONE G L, KNAFLITZ M, et al. Long short-term memory (LSTM) recurrent neural network for muscle activity detection [J]. J Neuroeng Rehabil, 2021, 18(1).
[37] VAN OORT C M, FERRELL J B, REMINGTON J M, et al. AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides [J]. Journal of Chemical Information and Modeling, 2021, 61(5): 2198-207.
[38] HAWKINS-HOOKER A, DEPARDIEU F, BAUR S, et al. Generating functional protein variants with variational autoencoders [J]. PLOS Computational Biology, 2021, 17(2): e1008736.
[39] MÜLLER A T, HISS J A, SCHNEIDER G. Recurrent Neural Network Model for Constructive Peptide Design [J]. Journal of Chemical Information and Modeling, 2018, 58(2): 472-9.
[40] MA Y, GUO Z, XIA B, et al. Identification of antimicrobial peptides from the human gut microbiome using deep learning [J]. Nature Biotechnology, 2022.
[41] BERMAN H M, BHAT T N, BOURNE P E, et al. The Protein Data Bank and the challenge of structural genomics [J]. Nat Struct Biol, 2000, 7: 957-9.
[42] BATEMAN A, MARTIN M J, ORCHARD S, et al. UniProt: the universal protein knowledgebase in 2021 [J]. Nucleic Acids Research, 2021, 49(D1): D480-D9.
[43] KAPLAN A, HAENLEIN M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence [J]. Business Horizons, 2019, 62(1): 15-25.
[44] JORDAN M I, MITCHELL T M. Machine learning: Trends, perspectives, and prospects [J]. Science, 2015, 349(6245): 255-60.
[45] ROSENBLATT F. The perceptron: A probabilistic model for information storage and organization in the brain [J]. Psychological Review, 1958, 65(6): 386-408.
[46] WERBOS P J. Applications of advances in nonlinear sensitivity analysis; proceedings of the System Modeling and Optimization, Berlin, Heidelberg, F, 1982 [C]. Springer Berlin Heidelberg.
[47] QUINLAN J R. Induction of decision trees [J]. Machine Learning, 1986, 1(1): 81-106.
[48] CORTES C, VAPNIK V. Support-vector networks [J]. Machine Learning, 1995, 20(3): 273-97.
[49] BREIMAN L. Random Forests [J]. Machine Learning, 2001, 45(1): 5-32.
[50] HOCHREITER S. Untersuchungen zu dynamischen neuronalen Netzen, F, 1991 [C].
[51] KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet Classification with Deep Convolutional Neural Networks [J]. Neural Information Processing Systems, 2012, 25.
[52] GLOROT X, BORDES A, BENGIO Y. Deep Sparse Rectifier Neural Networks [M]. 2010.
[53] HINTON G E, OSINDERO S, TEH Y-W. A Fast Learning Algorithm for Deep Belief Nets [J]. Neural Computation, 2006, 18(7): 1527-54.
[54] SILVER D, HUANG A, MADDISON C, et al. Mastering the game of Go with deep neural networks and tree search [J]. Nature, 2016, 529: 484-9.
[55] BAHDANAU D, CHO K, BENGIO Y. Neural Machine Translation by Jointly Learning to Align and Translate [J]. arXiv e-prints, 2014: arXiv:1409.0473.
[56] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding; proceedings of the NAACL, F, 2019 [C]. Association for Computational Linguistics.
[57] PAN S J, YANG Q. A Survey on Transfer Learning [J]. IEEE Transactions on Knowledge & Data Engineering, 2010, 22(10): 1345-59.
[58] ERHAN D, BENGIO Y, COURVILLE A, et al. Why Does Unsupervised Pre-training Help Deep Learning? [J]. J Mach Learn Res, 2010, 11: 625–60.
[59] YOSINSKI J, CLUNE J, BENGIO Y, et al. How transferable are features in deep neural networks? [J]. arXiv e-prints, 2014: arXiv:1411.792.
[60] BROWN T B, MANN B, RYDER N, et al. Language Models are Few-Shot Learners [J]. arXiv e-prints, 2020: arXiv:2005.14165.
[61] HANSCH C, MALONEY P P, FUJITA T, et al. Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients; proceedings of the Nature, F, 1962 [C].
[62] CRAMER R D. The inevitable QSAR renaissance [J]. Journal of Computer-Aided Molecular Design, 2012, 26(1): 35-8.
[63] DURANT J L, LELAND B A, HENRY D R, et al. Reoptimization of MDL Keys for Use in Drug Discovery [J]. Journal of Chemical Information and Computer Sciences, 2002, 42(6): 1273-80.
[64] RAO R, BHATTACHARYA N, THOMAS N, et al. Evaluating Protein Transfer Learning with TAPE [J]. Adv Neural Inf Process Syst, 2019, 32: 9689-701.
[65] GOTOH O. An improved algorithm for matching biological sequences [J]. Journal of Molecular Biology, 1982, 162(3): 705-8.
[66] MÜLLER A T, GABERNET G, HISS J A, et al. modlAMP: Python for antimicrobial peptides [J]. Bioinformatics, 2017, 33(17): 2753-5.

Academic Degree Assessment Sub committee
Domestic book classification number
Data Source
Document TypeThesis
DepartmentDepartment of Biology
Recommended Citation
GB/T 7714
官盛晖. 一个全面的抗菌肽数据库以及基于此数据库的生成模型[D]. 深圳. 南方科技大学,2022.
Files in This Item:
File Name/Size DocType Version Access License
11930163-官盛晖-生物系.pdf(7548KB) Restricted Access--Fulltext Requests
Related Services
Recommend this item
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[官盛晖]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[官盛晖]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[官盛晖]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.