Bioprospecting of culturable marine biofilm bacteria for novel antimicrobial peptides

Abstract

Antimicrobial peptides (AMPs) have become a viable source of novel antibiotics that are effective against human pathogenic bacteria. In this study, we construct a bank of culturable marine biofilm bacteria constituting 713 strains and their nearly complete genomes and predict AMPs using ribosome profiling and deep learning. Compared with previous approaches, ribosome profiling has improved the identification and validation of small open reading frames (sORFs) for AMP prediction. Among the 80,430 expressed sORFs, 341 are identified as candidate AMPs with high probability. Most potential AMPs have less than 40% similarity in their amino acid sequence compared to those listed in public databases. Furthermore, these AMPs are associated with bacterial groups that are not previously known to produce AMPs. Therefore, our deep learning model has acquired characteristics of unfamiliar AMPs. Chemical synthesis of 60 potential AMP sequences yields 54 compounds with antimicrobial activity, including potent inhibitory effects on various drug-resistant human pathogens. This study extends the range of AMP compounds by investigating marine biofilm microbiomes using a novel approach, accelerating AMP discovery.

Keywords

antimicrobial peptide / deep learning / marine biofilm / marine resource / Ribo-seq

References

[1] Huemer Markus, Srikanth M. Shambat, Silvio D. Brugger, and Annelies S. Zinkernagel. 2020. “Antibiotic resistance and persistence-Implications for human health and treatment perspectives.” EMBO reports 21:e51034. https://doi.org/10.15252/embr.202051034

[2] Fisher, Robert A., Bridget Gollan, and Sophie Helaine. 2017. “Persistent Bacterial Infections and Persister Cells.” Nature Reviews Microbiology 15: 453-464. https://doi.org/10.1038/nrmicro.2017.42

[3] Nordström, Randi, and Martin Malmsten. 2017. “Delivery Systems for Antimicrobial Peptides.” Advances in Colloid and Interface Science 242: 17-34. https://doi.org/10.1016/j.cis.2017.01.005

[4] Brogden, Kim A. 2005. “Antimicrobial Peptides: Pore Formers or Metabolic Inhibitors in Bacteria?” Nature Reviews Microbiology 3: 238-250. https://doi.org/10.1038/nrmicro1098

[5] Pierre, Joseph F., Brian M. Peters, Diana La Torre, Ashley M. Sidebottom, Yun Tao, Xiaorong Zhu, Candace M. Cham, et al. 2023. “Peptide YY: A Paneth Cell Antimicrobial Peptide That Maintains Candida Gut Commensalism.” Science 381: 502-508. https://doi.org/10.1126/science.abq3178

[6] Heng, Nicholas C. K., and John R. Tagg. 2006. “What's in a Name? Class Distinction for Bacteriocins.” Nature Reviews Microbiology 4: 160160. https://doi.org/10.1038/nrmicro1273-c1

[7] Das, Payel, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, et al. 2021. “Accelerated Antimicrobial Discovery via Deep Generative Models and Molecular Dynamics Simulations.” Nature Biomedical Engineering 5: 613-623. https://doi.org/10.1038/s41551-021-00689-x

[8] Spänig, Sebastian, and Dominik Heider. 2019. “Encodings and Models for Antimicrobial Peptide Classification for Multi-Resistant Pathogens.” BioData Mining 12: 7. https://doi.org/10.1186/s13040-019-0196-x

[9] Veltri, Daniel, Uday Kamath, and Amarda Shehu. 2018. “Deep Learning Improves Antimicrobial Peptide Recognition.” Bioinformatics 34: 2740-2747. https://doi.org/10.1093/bioinformatics/bty179

[10] Stokes, Jonathan M., Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, et al. 2020. “A Deep Learning Approach to Antibiotic Discovery.” Cell 181: 475-483. https://doi.org/10.1016/j.cell.2020.04.001

[11] Ma, Yue, Zhengyan Guo, Binbin Xia, Yuwei Zhang, Xiaolin Liu, Ying Yu, Na Tang, et al. 2022. “Identification of Antimicrobial Peptides From the Human Gut Microbiome Using Deep Learning.” Nature Biotechnology 40: 921-931. https://doi.org/10.1038/s41587-022-01226-0

[12] Nakatsuji, Teruaki, Tiffany H. Chen, Saisindhu Narala, Kimberly A. Chun, Aimee M. Two, Tong Yun, Faiza Shafiq, et al. 2017. “Antimicrobials From Human Skin Commensal Bacteria Protect against Staphylococcus aureus and Are Deficient in Atopic Dermatitis.” Science Translational Medicine 9: eaah4680. https://doi.org/10.1126/scitranslmed.aah4680

[13] Wang, Guangshun, Xia Li, and Zhe Wang. 2016. “APD3: The Antimicrobial Peptide Database as a Tool for Research and Education.” Nucleic Acids Research 44: D1087-D1093. https://doi.org/10.1093/nar/gkv1278

[14] Lazzaro, Brian P., Michael Zasloff, and Jens Rolff. 2020. “Antimicrobial Peptides: Application Informed By Evolution.” Science 368: eaau5480. https://doi.org/10.1126/science.aau5480

[15] Sperstad, Sigmund V., Tor Haug, Hans-Matti Blencke, Olaf B. Styrvold, Chun Li, and Klara Stensvåg. 2011. “Antimicrobial Peptides From Marine Invertebrates: Challenges and Perspectives in Marine Antimicrobial Peptide Discovery.” Biotechnology Advances 29: 519-530. https://doi.org/10.1016/j.biotechadv.2011.05.021

[16] Desriac, Florie, Camille Jégou, Eric Balnois, Benjamin Brillet, Patrick Chevalier, and Yannick Fleury. 2013. “Antimicrobial Peptides from Marine Proteobacteria.” Marine Drugs 11: 3632-3660. https://doi.org/10.3390/md11103632

[17] Zhang, Fan, Miao Zhao, Doug R. Braun, Spencer S. Ericksen, Jeff S. Piotrowski, Justin Nelson, Jian Peng, et al. 2020. “A Marine Microbiome Antifungal Targets Urgent-Threat Drug-Resistant Fungi.” Science 370: 974-978. https://doi.org/10.1126/science.abd6919

[18] Zhang, Weipeng, Yong Wang, Salim Bougouffa, Renmao Tian, Huiluo Cao, Yongxin Li, Lin Cai, et al. 2015. “Synchronized Dynamics of Bacterial Niche-Specific Functions during Biofilm Development in a Cold Seep Brine Pool.” Environmental Microbiology 17: 4089-4104. https://doi.org/10.1111/1462-2920.12978

[19] Ding, Wei, Shougang Wang, Peng Qin, Shen Fan, Xiaoyan Su, Peiyan Cai, Jie Lu, et al. 2023. “Anaerobic Thiosulfate Oxidation by the Roseobacter Group Is Prevalent in Marine Biofilms.” Nature Communications 14: 2033. https://doi.org/10.1038/s41467-023-37759-4

[20] Su, Xiaoyan, Han Cui, and Weipeng Zhang. 2023. “Copiotrophy in a Marine-Biofilm-Derived Roseobacteraceae Bacterium Can Be Supported by Amino Acid Metabolism and Thiosulfate Oxidation.” International Journal of Molecular Sciences 24: 8617. https://doi.org/10.3390/ijms24108617

[21] Cui, Han, Shen Fan, Wei Ding, and Weipeng Zhang. 2024. “Genomic Analysis of Novel Sulfitobacter Bacterial Strains Isolated From Marine Biofilms.” Marine Drugs 22: 289. https://doi.org/10.3390/md22070289

[22] Qin, Peng, Han Cui, Panxin Li, Shuaitao Wang, Shen Fan, Jie Lu, Meng Sun, et al. 2023. “Early Stage of Biofilm Assembly on Microplastics Is Structured by Substrate Size and Bacterial Motility.” iMeta 2: e121. https://doi.org/10.1002/imt2.121

[23] Wei, Zhongcheng, Liang Zhao, Shuaitao Wang, Lirong Chang, Jiaoxia Shi, Xiangfu Kong, Moli Li, et al. 2024. “Paralytic Shellfish Toxins Producing Dinoflagellates Cause Dysbacteriosis in Scallop Gut Microbial Biofilms.” Ecotoxicology and Environmental Safety 273: 116146. https://doi.org/10.1016/j.ecoenv.2024.116146

[24] Wang, Yongming, Yi Shu, Yue Sun, Qifan Zeng, Weipeng Zhang, Zhenmin Bao, and Wei Ding. 2024. “Acute Nitrite Exposure Causes Gut Microbiota Dysbacteriosis and Proliferation of Pathogenic Photobacterium in Shrimp.” Ecotoxicology and Environmental Safety 283: 116829. https://doi.org/10.1016/j.ecoenv.2024.116829

[25] Wang, Ruojun, Wei Ding, Lexin Long, Yi Lan, Haoya Tong, Subhasish Saha, Yue Him Wong, et al. 2020. “Exploring the Influence of Signal Molecules on Marine Biofilms Development.” Frontiers in Microbiology 11: 571400. https://doi.org/10.3389/fmicb.2020.571400

[26] Wang, Ruojun, Weipeng Zhang, Wei Ding, Zhicong Liang, Lexin Long, Wai Chuen Wong, and Pei-Yuan Qian. 2022. “Profiling Signal Transduction in Global Marine Biofilms.” Frontiers in Microbiology 12: 768926. https://doi.org/10.3389/fmicb.2021.768926

[27] Mishra, Biswajit, and Guangshun Wang. 2012. “Ab Initio Design of Potent anti-MRSA Peptides Based on Database Filtering Technology.” Journal of the American Chemical Society 134: 12426-12429. https://doi.org/10.1021/ja305644e

[28] Singh, Vishakha, Sameer Shrivastava, Sanjay Kumar Singh, Abhinav Kumar, and Sonal Saxena. 2022. “StaBle-ABPpred: A Stacked Ensemble Predictor Based on biLSTM and Attention Mechanism for Accelerated Discovery of Antibacterial Peptides.” Briefings in Bioinformatics 23: bbab439. https://doi.org/10.1093/bib/bbab439

[29] Randall, Justin R., Luiz C. Vieira, Claus O. Wilke, and Bryan W. Davies. 2024. “Deep Mutational Scanning and Machine Learning for the Analysis of Antimicrobial-Peptide Features Driving Membrane Selectivity.” Nature Biomedical Engineering 8: 842-853. https://doi.org/10.1038/s41551-024-01243-1

[30] Zhang, Weipeng, Wei Ding, Yong-Xin Li, Chunkit Tam, Salim Bougouffa, Ruojun Wang, Bite Pei, et al. 2019. “Marine Biofilms Constitute a Bank of Hidden Microbial Diversity and Functional Potential.” Nature Communications 10: 517. https://doi.org/10.1038/s41467-019-08463-z

[31] Zhang, Xu, Shelley A. Deeke, Zhibin Ning, Amanda E. Starr, James Butcher, Jennifer Li, Janice Mayne, et al. 2018. “Metaproteomics Reveals Associations between Microbiome and Intestinal Extracellular Vesicle Proteins in Pediatric Inflammatory Bowel Disease.” Nature Communications 9: 2873. https://doi.org/10.1038/s41467-018-05357-4

[32] Zhang, Xu, Leyuan Li, James Butcher, Alain Stintzi, and Daniel Figeys. 2019. “Advancing Functional and Translational Microbiome Research Using Meta-Omics Approaches.” Microbiome 7: 154. https://doi.org/10.1186/s40168-019-0767-6

[33] Ndah, Elvis, Veronique Jonckheere, Adam Giess, Eivind Valen, Gerben Menschaert, and Petra Van Damme. 2017. “REPARATION: Ribosome Profiling Assisted (Re-)Annotation of Bacterial Genomes.” Nucleic Acids Research 45: e168. https://doi.org/10.1093/nar/gkx758

[34] Friedman, Robin C., Stefan Kalkhof, Olivia Doppelt-Azeroual, Stephan A. Mueller, Martina Chovancová, Martin von Bergen, and Benno Schwikowski. 2017. “Common and Phylogenetically Widespread Coding for Peptides by Bacterial Small RNAs.” BMC Genomics 18: 553. https://doi.org/10.1186/s12864-017-3932-y

[35] Orr, Mona Wu, Yuanhui Mao, Gisela Storz, and Shu-Bing Qian. 2020. “Alternative ORFs and Small ORFs: Shedding Light on the Dark Proteome.” Nucleic Acids Research 48: 1029-1042. https://doi.org/10.1093/nar/gkz734

[36] Clark, Sam, Thomas A. Jowitt, Lynda K. Harris, Christopher G. Knight, and Curtis B. Dobson. 2021. “The Lexicon of Antimicrobial Peptides: A Complete Set of Arginine and Tryptophan Sequences.” Communications Biology 4: 605. https://doi.org/10.1038/s42003-021-02137-7

[37] Zeaki, Nikoleta, Sophia Johler, Panagiotis N. Skandamis, and Jenny Schelin. 2019. “The Role of Regulatory Mechanisms and Environmental Parameters in Staphylococcal Food Poisoning and Resulting Challenges to Risk Assessment.” Frontiers in Microbiology 10: 1307. https://doi.org/10.3389/fmicb.2019.01307

[38] Fu, Limin, Beifang Niu, Zhengwei Zhu, Sitao Wu, and Weizhong Li. 2012. “CD-HIT: Accelerated for Clustering the Next-Generation Sequencing Data.” Bioinformatics 28: 3150-3152. https://doi.org/10.1093/bioinformatics/bts565

[39] Bolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Eric J. Alm, et al. 2019. “Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2.” Nature Biotechnology 37: 852-857. https://doi.org/10.1038/s41587-019-0209-9

[40] Patel, Ravi K., and Mukesh Jain. 2012. “NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data.” PloS One 7: e30619. https://doi.org/10.1371/journal.pone.0030619

[41] Bankevich, Anton, Sergey Nurk, Dmitry Antipov, Alexey A. Gurevich, Mikhail Dvorkin, Alexander S. Kulikov, Valery M. Lesin, et al. 2012. “SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing.” Journal of Computational Biology 19: 455-477. https://doi.org/10.1089/cmb.2012.0021

[42] Chklovski, Alex, Donovan H. Parks, Ben J. Woodcroft, and Gene W. Tyson. 2023. “CheckM2: A Rapid, Scalable and Accurate Tool for Assessing Microbial Genome Quality Using Machine Learning.” Nature Methods 20: 1203-1212. https://doi.org/10.1038/s41592-023-01940-w

[43] Chaumeil, Pierre-Alain, Aaron J. Mussig, Philip Hugenholtz, and Donovan H. Parks. 2022. “GTDB-Tk v2: Memory Friendly Classification With the Genome Taxonomy Database.” Bioinformatics 38: 5315-5316. https://doi.org/10.1093/bioinformatics/btac672

[44] Jain, Chirag, Luis M. Rodriguez-R, Adam M. Phillippy, Konstantinos T. Konstantinidis, and Srinivas Aluru. 2018. “High Throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries.” Nature Communications 9: 5114. https://doi.org/10.1038/s41467-018-07641-9

[45] Chen, Tong, Yong-Xin Liu, and Luqi Huang. 2022. “ImageGP: An Easy-to-Use Data Visualization Web Server for Scientific Researchers.” iMeta 1: e5. https://doi.org/10.1002/imt2.5

[46] Letunic, Ivica, and Peer Bork. 2024. “Interactive Tree of Life (iTOL) v6: Recent Updates to the Phylogenetic Tree Display and Annotation Tool.” Nucleic Acids Research 52: W78-W82. https://doi.org/10.1093/nar/gkae268

[47] Kanehisa, Minoru, Miho Furumichi, Yoko Sato, Masayuki Kawashima, and Mari Ishiguro-Watanabe. 2023. “KEGG for Taxonomy-Based Analysis of Pathways and Genomes.” Nucleic Acids Research 51: D587-D592. https://doi.org/10.1093/nar/gkac963

[48] Buchfink, Benjamin, Klaus Reuter, and Hajk-Georg Drost. 2021. “Sensitive Protein Alignments at Tree-Of-Life Scale Using DIAMOND.” Nature Methods 18: 366-368. https://doi.org/10.1038/s41592-021-01101-x

[49] Hyatt, Doug, Gwo-Liang Chen, Philip F. Locascio, Miriam L. Land, Frank W. Larimer, and Loren J. Hauser. 2010. “Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification.” BMC Bioinformatics 11: 119. https://doi.org/10.1186/1471-2105-11-119

[50] Langmead, Ben, and Steven L. Salzberg. 2012. “Fast Gapped-Read Alignment With Bowtie 2.” Nature Methods 9: 357-359. https://doi.org/10.1038/nmeth.1923

[51] Rice, Peter, Ian Longden, and Alan Bleasby. 2000. “EMBOSS: The European Molecular Biology Open Software Suite.” Trends in genetics 16: 276-277. https://doi.org/10.1016/s0168-9525(00)02024-2

[52] Shen, Wei, Shuai Le, Yan Li, and Fuquan Hu. 2016. “SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation.” PloS One 11: e0163962. https://doi.org/10.1371/journal.pone.0163962

[53] Pirtskhalava, Malak, Anthony A. Amstrong, Maia Grigolava, Mindia Chubinidze, Evgenia Alimbarashvili, Boris Vishnepolsky, Andrei Gabrielian, et al. 2021. “DBAASP v3: Database of Antimicrobial/Cytotoxic Activity and Structure of Peptides as a Resource for Development of New Therapeutics.” Nucleic Acids Research 49: D288-D297. https://doi.org/10.1093/nar/gkaa991

[54] Jhong, Jhih-Hua, Lantian Yao, Yuxuan Pang, Zhongyan Li, Chia-Ru Chung, Rulan Wang, Shangfu Li, et al. 2022. “dbAMP 2.0: Updated Resource for Antimicrobial Peptides with an Enhanced Scanning Method for Genomic and Proteomic Data.” Nucleic Acids Research 50: D460-D470. https://doi.org/10.1093/nar/gkab1080

[55] Shi, Guobang, Xinyue Kang, Fanyi Dong, Yanchao Liu, Ning Zhu, Yuxuan Hu, Hanmei Xu, Xingzhen Lao, and Heng Zheng. 2022. “DRAMP 3.0: An Enhanced Comprehensive Data Repository of Antimicrobial Peptides.” Nucleic Acids Research 50: D488-D496. https://doi.org/10.1093/nar/gkab651

[56] Pang, Bo, Erik Nijkamp, and Yingnian Wu. 2020. “Deep learning with tensorflow: A review.” Journal of Educational and Behavioral Statistics 45: 227-248. https://doi.org/10.3102/1076998619872761

[57] Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825. https://doi.org/10.48550/arXiv.1201.0490

[58] Yao, Lantian, Jiahui Guan, Peilin Xie, Chia-Ru Chung, Junyang Deng, Yixian Huang, Ying-Chih Chiang, and Tzong-Yi Lee. 2024. “AMPActiPred: A Three-Stage Framework for Predicting Antibacterial Peptides and Activity Levels With Deep Forest.” Protein Science 33: e5006. https://doi.org/10.1002/pro.5006

[59] Li, Chenkai, Darcy Sutherland, S. Austin Hammond, Chen Yang, Figali Taho, Lauren Bergman, Simon Houston, et al. 2022. “AMPlify: Attentive Deep Learning Model for Discovery of Novel Antimicrobial Peptides Effective against WHO Priority Pathogens.” BMC Genomics 23: 77. https://doi.org/10.1186/s12864-022-08310-4

[60] Santos-Júnior, Célio Dias, Shaojun Pan, Xing-Ming Zhao, and Luis Pedro Coelho. 2020. “Macrel: Antimicrobial Peptide Screening in Genomes and Metagenomes.” PeerJ 8: e10555. https://doi.org/10.7717/peerj.10555

[61] Gawde, Ulka, Shuvechha Chakraborty, Faiza Hanif Waghu, Ram Shankar Barai, Ashlesha Khanderkar, Rishikesh Indraguru, Tanmay Shirsat, and Susan Idicula-Thomas. 2023. “CAMPR4: A Database of Natural and Synthetic Antimicrobial Peptides.” Nucleic Acids Research 51: D377-D383. https://doi.org/10.1093/nar/gkac933

[62] Li, Changjiang, Quan Zou, Cangzhi Jia, and Jia Zheng. 2023. “AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor With a Stacking Architecture, Multiple Features, and Multihead Attention.” Journal of Chemical Information and Modeling 64: 2393-2404. https://doi.org/10.1021/acs.jcim.3c01017

[63] Peters, Brian M., Mark E. Shirtliff, and Mary Ann Jabra-Rizk. 2010. “Antimicrobial Peptides: Primeval Molecules or Future Drugs?” PLoS Pathogens 6: e1001067. https://doi.org/10.1371/journal.ppat.1001067

[64] Fingerhut, Legana C. H. W., David J. Miller, Jan M. Strugnell, Norelle L. Daly, and Ira R. Cooke. 2020. “Ampir: An R Package for Fast Genome-Wide Prediction of Antimicrobial Peptides.” Bioinformatics 36: 5262-5263. https://doi.org/10.1093/bioinformatics/btaa653

[65] Müller, Alex T., Gisela Gabernet, Jan A. Hiss, and Gisbert Schneider. 2017. “modlAMP: Python for Antimicrobial Peptides.” Bioinformatics 33: 2753-2755. https://doi.org/10.1093/bioinformatics/btx285

[66] Mirdita, Milot, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger. 2022. “ColabFold: Making Protein Folding Accessible to All.” Nature Methods 19: 679-682. https://doi.org/10.1038/s41592-022-01488-1

RIGHTS & PERMISSIONS

2024 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.