Publicado

2021-03-16

Generación de péptidos antimicrobianos mediante redes neuronales recurrentes

Synthetic antimicrobial peptides generation using recurrent neural networks

DOI:

https://doi.org/10.15446/dyna.v88n216.88799

Palabras clave:

resistencia antimicrobiana;, péptidos sintéticos;, virtual screening;, aprendizaje profundo (es)
antimicrobial resistance;, synthetic peptides;, virtual screening;, deep learning (en)

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Autores/as

Los péptidos antimicrobianos (AMP) han tomado importancia en el desarrollo de nuevos antibióticos debido a su papel como inhibidores, no solo de bacterias sino también de virus, hongos y parásitos, entre otros. Desde el descubrimiento de los AMP, se han reportado miles, sin embargo, muchos de ellos no son adecuados para aplicaciones terapéuticas debido a sus largas secuencias de aminoácidos, baja potencia antimicrobiana y altos costos de producción. En este trabajo, proponemos utilizar redes neuronales recurrentes (RNN) con células LSTM para generar péptidos más potentes y económicos. Realizamos diferentes experimentos generando AMP sintéticos entre 12 y 20 aminoácidos. Los resultados muestran que podemos usar RNN y mejorar el proceso de generación en comparación con el método de plantillas manuales.

The antimicrobial peptides (AMPs) have taken importance in the development of new antibiotics because of their role as an inhibitor, not only of bacteria but also of viruses, fungi and parasites, among others. Since the discovery of AMPs, thousands have been reported, however, many of them are not suitable for therapeutic applications due to their long amino acid sequences, low antimicrobial potency and high production costs. In this work, we propose to use recurrent neural networks (RNN) with LSTM cells in order to generate more potent and economical peptides. We perform different experiments generating synthetic AMPs between 12 and 20 amino acids. The results show that we can use RNN and improve the generation process compared with the template method.

Referencias

Fleischmann, C., Scherag, A., Adhikari, N.K., Hartog, C.S., Tsaganos, T., Schlattmann, P., Angus, D.C. and Reinhart, K., Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations, American Jorurnal of Respiratory and Criticatl Care Medicine, 193(3), pp. 259-272, 2016. DOI: 10.1164/rccm.201504-0781OC.

World Health Organization, Antimicrobial resistance, [online]. Feb 2018. Avialable at: https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance

Fair, R.J. and Tor, Y., Antibiotics and bacterial resistance in the 21st Century. Perspectives in Medicinal Chemistry, 6, pp. 25-64, 2014. DOI: 10.4137/PMC.S14459

Porto, W.F., Pires, A.S. and Franco, O.L., Computational tools for exploring sequence databases as a resource for antimicrobial peptides. Biotechnology Advances, 35(3), pp. 337-349, 2017. DOI: 10.1016/j.biotechadv.2017.02.001

Yoshida, M., Hinkley, T., Tsuda, S., Abul-Haija, Y.M., McBurney, R.T., Kulikov, V., Mathieson, J.S., Galianes-Reyes, S., Castro, M.D. and Cronin, L., Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides. Chem, 4(3), pp. 533-543, 2018. DOI: 10.1016/j.chempr.2018.01.005

Brogden, K.A., Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria. Nature Reviews Microbiology, 3, pp. 238-250, 2005. DOI: 10.1038/nrmicro1098

Yeaman, M.R. and Yount, N.Y. Mechanisms of antimicrobial peptide action and resistance. Pharmacological Reviews, 55(1), pp. 27-55, 2003. DOI: 10.1124/pr.55.1.2

Gutierrez, P. and Orduz, S., Péptidos antimicrobianos: estructura, función y aplicaciones. Actualidades Biologicas, 25(78), pp. 5-15, 2003.

Lee, E.Y., Lee, M.W., Fulan, Ferguson, A.L. and Wong, G.C., What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning? Interface Focus, 7, pp. 1-14, 2017. DOI: 10.1098/rsfs.2016.0153

Mojsoska, B. and Jenssen, H., Peptides and peptidomimetics for antimicrobial drug design. Pharmaceuticals, 8(3), pp. 366-415, 2015. DOI: 10.3390/ph8030366

Zasloff, M., Antimicrobial peptides of multicellular organisms. Nature, 415(6870), pp. 389-395, 2002. DOI: 10.1038/415389a

Walsh, C.T. and Wencewicz, T.A., Prospects for new antibiotics: a molecule-centered perspective. Journal of Antibiotics, 67, pp. 7-22, 2014. DOI: 10.1038/ja.2013.49

Wieland, T. and Bodanszky, M., World of peptides. A Brief history of peptide chemistry. Academic Press, 1995.

Téllez, G.A. and Castaño, J.C., Péptidos antimicrobianos. Infectio, 14(1), pp. 55-67, 2010. DOI: 10.1016/S0123-9392(10)70093-X

Fjell, C.D., Hiss, J.A., Hancock, R.E. and Schneider, G., Designing antimicrobial peptides: Form follows function, Nature Reviews Drug Discovery. 11, pp. 37-51, 2012. DOI: 10.1038/nrd3591

Loose, C., Jensen, K., Rigoutsos, I. and Stephanopoulos, G., A linguistic model for the rational design of antimicrobial peptides. Nature, 443, pp. 867-869, 2006. DOI: 10.1038/nature05233

Schneider, G., Schuchhardt, J. and Wrede, P., Artificial neural networks and simulated molecular evolution are potential tools for sequence-oriented protein design. Bioinformatics, 10(6), pp. 635-645, 1994. DOI: 10.1093/bioinformatics/10.6.635

Fjell, C.D., Jenssen, H., Cheung, W.A., Hancock, R.E.W. and Cherkasov, A., Optimization of antibacterial peptides by genetic algorithms and cheminformatics. Chemical Biology and Drug Design, 77(1), pp. 48-56, 2011. DOI: 10.1111/j.1747-0285.2010.01044.x

Müller, A.T., Hiss, J.A. and Schneider, G., Recurrent neural network model for constructive peptide design. Journal of Chemical Information and Modeling, 58(2), pp. 472-479, 2018. DOI: 10.1021/acs.jcim.7b00414

Grisoni, F., Neuhaus, C.S., Gabernet, G., Müller, A.T., Hiss, J.A. and Schneider, G., Designing anticancer peptides by constructive machine learning. ChemMedChem, 13(13), pp. 1300-1302, 2018. DOI: 10.1002/cmdc.201800204

Schneider, G., Schuchhardt, J. and Wrede, P., Peptide design in machina: development of artificial mitochondrial protein precursor cleavage sites by simulated molecular evolution. Biophysical Journal, 68(2), pp. 434-447,1995. DOI: 10.1016/S0006-3495(95)80205-5

Fjell, C.D., Jenssen, H., Hilpert, K., Cheung, W.A., Pante, N., Hancock, R.E.W. and Cherkasov, A., Identification of novel antibacterial peptides by chemoinformatics and machine learning. Journal of Medicinal Chemistry, 52(7), pp. 2006-2015, 2009. DOI: 10.1021/jm8015365

Lata, S., Mishra, N.K. and Raghava, G.P., AntiBP2: improved version of antibacterial peptide prediction. BMC Bioinformatics, 11(S19), pp. 1471-2105, 2010. DOI: 10.1186/1471-2105-11-S1-S19

Xiao, X., Wang, P., Lin, W.Z., Jia, J.H. and Chou, K.C., IAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Analytical Biochemistry, 436(2), pp. 168-177, 2013. DOI: 10.1016/j.ab.2013.01.019

Rondon-Villarreal, P., Sierra, D.A. and Torres, R., Classification of antimicrobial peptides by using the p-spectrum kernel and support vector machines. Advances in Intelligent Systems and Computing, 232, pp. 155-160, 2014. DOI: 10.1007/978-3-319-01568-2_23

Waghu, F.H., Gopi, L., Barai, R.S., Ramteke, P., Nizami, B. and Idicula-Thomas, S., CAMP: collection of sequences and structures of antimicrobial peptides. Nucleic Acids Research, 42(D1), pp. 1154-1158, 2014. DOI: 10.1093/nar/gkt1157

Bhadra, P., Yan, J., Li, J., Fong, S. and Siu, S.W., AmPEP: sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Scientific Reports, 8, pp. 1-10, 2018. DOI: 10.1038/s41598-018-19752-w

Veltri, D., Kamath, U. and Shehu, A., Deep learning improves antimicrobial peptide recognition. Bioinformatics, 34(16), pp. 2740-2747, 2018. DOI: 10.1093/bioinformatics/bty179

Das, P., Wadhawan, K., Chang, O., Sercu, T., Santos, C.D., Riemer, M., Chenthamarakshan, V., Padhi, I. and Mojsilovic, A., PepCVAE: Semisupervised targeted design of antimicrobial peptide sequences. [online]. 2018. Available at: http://arxiv.org/abs/1810.07743

Chung, C.-R., Kuo, T.-R., Wu, L.-C., Lee, T.-Y. and Horng, J.-T. Characterization and identification of antimicrobial peptides with different functional activities. Briefings in Bioinformatics, 21(3), pp. 1098-1114, 2020. DOI: 10.1093/bib/bbz043

Lee, H.T., Lee, C.C., Yang, J.R., Lai, J.Z. and Chang, K.Y., A large-scale structural classification of Antimicrobial peptides. BioMed Research International, 2015, pp. 1-6, 2015. DOI: 10.1155/2015/475062

Wang, G., Li, X. and Wang, Z., APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Research, 44(D1), pp. D1087-D1093, 2016. DOI: 10.1093/nar/gkv1278

Theolier, J., Fliss, I., Jean, J. and Hammami, R., MilkAMP: a comprehensive database of antimicrobial peptides of dairy origin. Dairy Science and Technology, 94(2), pp. 181-193, 2014. DOI: 10.1007/s13594-013-0153-2

Zhao, X., Wu, H., Lu, H., Li, G. and Huang, Q., Lamp: a database linking antimicrobial peptides. PLoS ONE, 8(6), pp. 1-6, 06 2013. DOI: 10.1371/journal.pone.0066557

Yen, T.Y., Joshi, R.K., Yan, H., Seto, N.O., Palcic, M.M. and Macher, B.A., Characterization of cysteine residues and disulfide bonds in proteins by liquid chromatography/electrospray ionization tandem mass spectrometry. Journal of Mass Spectrometry, 35(8), pp. 990- 1002, 2000. DOI: 10.1002/1096-9888(200008)35:8<990::AID-JMS27>3.0.CO;2-K

Sutskever, I., Vinyals, O. and Le, Q.V., Sequence to sequence learning with neural networks. Proceedings of the 27th International Conference on Neural Information Processing Systems, 2, pp. 3104-3112, 2014. DOI: 10.5555/2969033.2969173

Dugar, P., Attention seq2seq models. Towards Data Science, [online]. 2019. Disponible en: https://towardsdatascience.com/day-1-2-attention-seq2seq-models-65df3f49e263.

Gete, H., Neural natural language generation with unstructured contextual information. MSc. Thesis, Universidad del País Vasco, España, 2018.

Tieleman, T. and Hinton, G., Neural Networks for Machine Learning. COURSERA, 2012.

Kingma, D.P. and Ba, J.A., A method for stochastic optimization. International Conference on Learning Representations, [online]. 2014, pp. 1-15. Available at: https://arxiv.org/pdf/1412.6980.pdf

Brownlee, J., Difference between a batch and an epoch in a neural network. machine learning mastery, [online]. 2018. Available at: https://machinelearningmastery.com/difference-between-a-batch-and-an-epoch/

Müller, A.T., Gabernet, G., Hiss, J.A. and Schneider, G., modlAMP: Python for antimicrobial peptides. Bioinformatics, 33(17), pp. 2753-2755, 2017. DOI: 10.1093/bioinformatics/btx285

Dathe, M. and Wieprecht, T., Structural features of helical antimicrobial peptides: their potential to modulate activity on model membranes and biological cells. Biochimica et Biophysica Acta - Biomembranes, 1462(1-2), pp. 71-87, 1999. DOI: 10.1016/S0005-2736(99)00201-1

World Health Organization. The Evolving threat of antimicrobial resistance: options for action. [online]. 2012. Avialable at: https://apps.who.int/iris/handle/10665/44812

Osorio, D., Rondón-Villarreal, P. and Torres, R., Peptides: a package for data mining of antimicrobial peptides. The R Journal, 7(1), pp. 4-14, 2015. DOI: 10.32614/rj-2015-001

Dathe, M., Nikolenko, H., Meyer, J., Beyermann, M., Bienert, M., Optimization of the antimicrobial activity of magainin peptides by modification of charge. FEBS Lett. 501(2), pp. 146-50, 2001.

Su, X., Xu, J., Yin, Y., Quan, X. and Zhang, H., Antimicrobial peptide identification using multi-scale convolutional network. BMC Bioinformatics, 20(730), pp. 1-10, 2019. DOI: 10.1186/s12859-019-3327-y

Yan, J., Bahadra, P., Li, A., Sethiya, P., Quin, L., Tai, H.K., Wong, K.H. and Siu, S.W.I., Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning. Molecular Therapy-Nucleic Acids, 20(5), pp. 882-894, 2020. DOI: 10.1016/j.omtn.2020.05.006

Burdukiewicz, M., Sidorczuk, K., Rafacz, D., Pietluch, F., Chilimoniuk, J., Rödiger, S. and Gagat, P., Proteomic Screening for prediction and design of antimicrobial peptides with AmpGram. International Journal of Molecular Sciences, 21(12), 4310, pp. 1-13, 2020. DOI: 10.3390/ijms21124310

Cómo citar

IEEE

[1]
A. Veléz, C. A. Mera, S. Orduz, y J. W. Branch, «Generación de péptidos antimicrobianos mediante redes neuronales recurrentes», DYNA, vol. 88, n.º 216, pp. 210–219, feb. 2021.

ACM

[1]
Veléz, A., Mera, C.A., Orduz, S. y Branch, J.W. 2021. Generación de péptidos antimicrobianos mediante redes neuronales recurrentes. DYNA. 88, 216 (feb. 2021), 210–219. DOI:https://doi.org/10.15446/dyna.v88n216.88799.

ACS

(1)
Veléz, A.; Mera, C. A.; Orduz, S.; Branch, J. W. Generación de péptidos antimicrobianos mediante redes neuronales recurrentes. DYNA 2021, 88, 210-219.

APA

Veléz, A., Mera, C. A., Orduz, S., & Branch, J. W. (2021). Generación de péptidos antimicrobianos mediante redes neuronales recurrentes. DYNA, 88(216), 210–219. https://doi.org/10.15446/dyna.v88n216.88799

ABNT

VELÉZ, A.; MERA, C. A.; ORDUZ, S.; BRANCH, J. W. Generación de péptidos antimicrobianos mediante redes neuronales recurrentes. DYNA, [S. l.], v. 88, n. 216, p. 210–219, 2021. DOI: 10.15446/dyna.v88n216.88799. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/88799. Acesso em: 11 ago. 2022.

Chicago

Veléz, Andres, Carlos Andres Mera, Sergio Orduz, y John Willian Branch. 2021. «Generación de péptidos antimicrobianos mediante redes neuronales recurrentes». DYNA 88 (216):210-19. https://doi.org/10.15446/dyna.v88n216.88799.

Harvard

Veléz, A., Mera, C. A., Orduz, S. y Branch, J. W. (2021) «Generación de péptidos antimicrobianos mediante redes neuronales recurrentes», DYNA, 88(216), pp. 210–219. doi: 10.15446/dyna.v88n216.88799.

MLA

Veléz, A., C. A. Mera, S. Orduz, y J. W. Branch. «Generación de péptidos antimicrobianos mediante redes neuronales recurrentes». DYNA, vol. 88, n.º 216, febrero de 2021, pp. 210-9, doi:10.15446/dyna.v88n216.88799.

Turabian

Veléz, Andres, Carlos Andres Mera, Sergio Orduz, y John Willian Branch. «Generación de péptidos antimicrobianos mediante redes neuronales recurrentes». DYNA 88, no. 216 (febrero 22, 2021): 210–219. Accedido agosto 11, 2022. https://revistas.unal.edu.co/index.php/dyna/article/view/88799.

Vancouver

1.
Veléz A, Mera CA, Orduz S, Branch JW. Generación de péptidos antimicrobianos mediante redes neuronales recurrentes. DYNA [Internet]. 22 de febrero de 2021 [citado 11 de agosto de 2022];88(216):210-9. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/88799

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