Research on antimicrobial peptide prediction model based on deep learning and protein language model

Abstract

Keywords:
• antimicrobial peptide,
• prediction model,
• foodborne pathogen,
• protein language model,
• deep learning network

References

1. [1]

   WHITE D G,ZHAO S H,SIMJEE S,et al.Antimicrobial resistance of foodborne pathogens[J].Microbes and Infection,2002,4(4):405-412.

2. [2]

   KUMAR P,KIZHAKKEDATHU J N,STRAUS S K.Antimicrobial peptides:Diversity,mechanism of action and strategies to improve the activity and biocompatibility in vivo[J].Biomolecules,2018,8(1):4.

3. [3]

   ZHANG R N,WU D,GAO Y T.Progress on the design and optimization of antimicrobial peptides[J].Journal of Biomedical Engineering,2022,39(6):1247-1253.

4. [4]

   JOSEPH S,KARNIK S,NILAWE P,et al.ClassAMP:A prediction tool for classification of antimicrobial peptides[J].IEEE-ACM Transactions on Computational Biology and Bioinformatics,2012,9(5):1535-1538.

5. [5]

   SHARMA R,SHRIVASTAVA S,SINGH S K,et al.AniAMPpred:Artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom[J].Briefings in Bioinformatics,2021,22(6):bbab242.

6. [6]

   KAVOUSI K,BAGHERI M,BEHROUZI S,et al.IAMPE:NMR-assisted computational prediction of antimicrobial peptides[J].Journal of Chemical Information and Modeling,2020,60(10):4691-4701.

7. [7]

   CHUNG C R,KUO T R,WU L C,et al.Characterization and identification of antimicrobial peptides with different functional activities[J].Briefings in Bioinformatics,2020,21(3):1098-1114.

8. [8]

   XU J,LI F Y,LI C,et al.iAMPCN:A deep-learning approach for identifying antimicrobial peptides and their functional activities[J].Briefings in Bioinformatics,2023,24(4):bbad240.

9. [9]

   YAN K,LYU H W,GUO Y C,et al.sAMPpred-GAT:Prediction of antimicrobial peptide by graph attention network and predicted peptide structure[J].Bioinformatics,2023,39(1):btac715.

10. [10]

    FERRUZ N,HCKER B.Controllable protein design with language models[J].Nature Machine Intelligence,2022,4(6):521-532.

11. [11]

    WANG G S,LI X,WANG Z.APD3:The antimicrobial peptide database as a tool for research and education[J].Nucleic Acids Research,2016,44:D1087-D1093.

12. [12]

    ZHAO X W,WU H Y,LU H R,et al.LAMP:A database linking antimicrobial peptides[J].PLoS ONE,2013,8(6):e66557.

13. [13]

    THOMAS S,KARNIK S,BARAI R S,et al.CAMP:A useful resource for research on antimicrobial peptides[J].Nucleic Acids Research,2010,38:D774-D780.

14. [14]

    GOGOLADZE G,GRIGOLAVA M,VISHNEPOLSKY B,et al.DBAASP:Database of antimicrobial activity and structure of peptides[J].FEMS Microbiology Letters,2014,357(1):63-68.

15. [15]

    KANG X Y,DONG F Y,SHI C,et al.DRAMP 2.0,an updated data repository of antimicrobial peptides[J].Scientific Data,2019,6:148.

16. [16]

    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:D285-D297.

17. [17]

    FU L M,NIU B F,ZHU Z W,et al.CD-HIT:Accelerated for clustering the next-generation sequencing data[J].Bioinformatics,2012,28(23):3150-3152.

18. [18]

    ZHANG Y,LIN J Y,ZHAO L M,et al.A novel antibacterial peptide recognition algorithm based on BERT[J].Briefings in Bioinformatics,2021,22(6):bbab200.

19. [19]

    XU J,LI F Y,LEIER A,et al.Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides[J].Briefings in Bioinformatics,2021,22(5):bbab083.

20. [20]

    ALLEY E C,KHIMULYA G,BISWAS S,et al.Unified rational protein engineering with sequence-based deep representation learning[J].Nature Methods,2019,16(12):1315-1322.

21. [21]

    BRANDES N,OFER D,PELEG Y,et al.ProteinBERT:A universal deep-learning model of protein sequence and function[J].Bioinformatics,2022,38(8):2102-2110.

22. [22]

    HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.

23. [23]

    YU Z X,HUANG F,ZHAO X H,et al.Predicting drug-disease associations through layer attention graph convolutional network[J].Briefings in Bioinformatics,2021,22(4):bbaa243.

Proportional views