Lazzaro, B. P., Zasloff, M. & Rolff, J. Antimicrobial peptides: Application informed by evolution. Science 368, eaau5480 (2020).
Google Scholar
León-Buitimea, A., Garza-Cárdenas, C. R., Garza-Cervantes, J. A., Lerma-Escalera, J. A. & Morones-Ramírez, J. R. The demand for new antibiotics: antimicrobial peptides, nanoparticles, and combinatorial therapies as future strategies in antibacterial agent design. Front. Microbiol. 11, 1699 (2020).
Google Scholar
Toke, O. Antimicrobial peptides: New candidates in the fight against bacterial infections. Pept. Sci. 80, 717–735 (2005).
Google Scholar
Mylonakis, E., Podsiadlowski, L., Muhammed, M. & Vilcinskas, A. Diversity, evolution and medical applications of insect antimicrobial peptides. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150290 (2016).
Google Scholar
Vizioli, J. & Salzet, M. Antimicrobial peptides from animals: Focus on invertebrates. Trends Pharmacol. Sci. 23, 494–496 (2002).
Google Scholar
Lee, E. Y., Wong, G. C. L. & Ferguson, A. L. Machine learning-enabled discovery and design of membrane-active peptides. Bioorg. Med. Chem. 26, 2708–2718 (2018).
Google Scholar
Porto, W. F., Pires, A. S. & Franco, O. L. Computational tools for exploring sequence databases as a resource for antimicrobial peptides. Biotechnol. Adv. 35, 337–349 (2017).
Google Scholar
Wu, Q. et al. Recent progress in machine learning-based prediction of peptide activity for drug discovery. Curr. Topics Med. Chem. 19, 4–16 (2018).
Google Scholar
Lei, J. et al. The antimicrobial peptides and their potential clinical applications. Am. J. Transl. Res. 11, 3919–3931 (2019).
Google Scholar
Porto, W. F., Pires, Á. S. & Franco, O. L. CS-AMPPred: An updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides. PLoS ONE 7, e51444 (2012).
Google Scholar
Xiao, X., Wang, P., Lin, W.-Z., Jia, J.-H. & Chou, K.-C. iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal. Biochem. 436, 168–177 (2013).
Google Scholar
Porto, W., Ferreira, K. C. V., Ribeiro, S. M. & Franco, O. L. Sense the moment: A highly sensitive antimicrobial activity predictor based on hydrophobic moment. bioRxiv https://doi.org/10.1101/2020.07.15.205419 (2020).
Google Scholar
Gabere, M. N. & Noble, W. S. Empirical comparison of web-based antimicrobial peptide prediction tools. Bioinformatics 33, 1921–1929 (2017).
Google Scholar
Hollox, E. J. & Abujaber, R. Evolution and diversity of defensins in vertebrates. In Evolutionary Biology: Self/Nonself Evolution, Species and Complex Traits Evolution, Methods and Concepts (ed. Pontarotti, P.) 27–50 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-61569-1_2.
Google Scholar
Montero-Alejo, V. et al. Panusin represents a new family of β-defensin-like peptides in invertebrates. Dev. Comp. Immunol. 67, 310–321 (2017).
Google Scholar
Patil, A., Hughes, A. L. & Zhang, G. Rapid evolution and diversification of mammalian α-defensins as revealed by comparative analysis of rodent and primate genes. Physiol. Genom. 20, 1–11 (2004).
Google Scholar
Patil, A. A., Cai, Y., Sang, Y., Blecha, F. & Zhang, G. Cross-species analysis of the mammalian β-defensin gene family: Presence of syntenic gene clusters and preferential expression in the male reproductive tract. Physiol. Genom. 23, 5–17 (2005).
Google Scholar
Tassanakajon, A., Somboonwiwat, K. & Amparyup, P. Sequence diversity and evolution of antimicrobial peptides in invertebrates. Dev. Comp. Immunol. 48, 324–341 (2015).
Google Scholar
Shelomi, M., Jacobs, C., Vilcinskas, A. & Vogel, H. The unique antimicrobial peptide repertoire of stick insects. Dev. Comp. Immunol. 103, 103471 (2020).
Google Scholar
NCBI Protein database. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information https://www.ncbi.nlm.nih.gov/protein/ (2020).
NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46, D8–D13 (2018).
Google Scholar
Wang, G., Li, X. & Wang, Z. APD3: The antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 44, D1087–D1093 (2016).
Google Scholar
The UniProt Consortium. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).
Google Scholar
Juretić, D. et al. Knowledge-based computational methods for identifyingor designing novel, non-homologous antimicrobial peptides. Eur. Biophys. J. 40, 371–385 (2011).
Google Scholar
Ahmed, T. A. E. & Hammami, R. Recent insights into structure–function relationships of antimicrobial peptides. J. Food Biochem. 43, e12546 (2019).
Google Scholar
Torres, M. D. T. et al. Structure-function-guided exploration of the antimicrobial peptide polybia-CP identifies activity determinants and generates synthetic therapeutic candidates. Commun. Biol. 1, 1–16 (2018).
Google Scholar
Starr, T. N., Picton, L. K. & Thornton, J. W. Alternative evolutionary histories in the sequence space of an ancient protein. Nature 549, 409–413 (2017).
Google Scholar
Cytryńska, M., Mak, P., Zdybicka-Barabas, A., Suder, P. & Jakubowicz, T. Purification and characterization of eight peptides from Galleria mellonella immune hemolymph. Peptides 28, 533–546 (2007).
Google Scholar
Mercer, D. K. et al. Antimicrobial susceptibility testing of antimicrobial peptides to better predict efficacy. Front. Cell. Infect. Microbiol. 10, 326 (2020).
Google Scholar
Meurer, M. et al. Antimicrobial susceptibility testing of antimicrobial peptides requires new and standardized testing structures. ACS Infect. Dis. https://doi.org/10.1021/acsinfecdis.1c00210 (2021).
Google Scholar
Kang, X. et al. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci. Data 6, 148 (2019).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Lee, H.-T., Lee, C.-C., Yang, J.-R., Lai, J. Z. C. & Chang, K. Y. A large-scale structural classification of antimicrobial peptides. BioMed. Res. Int. 2015, e475062 (2015).
Burdukiewicz, M. et al. Proteomic screening for prediction and design of antimicrobial peptides with AmpGram. Int. J. Mol. Sci. 21, 4310 (2020).
Google Scholar
Veltri, D., Kamath, U. & Shehu, A. Deep learning improves antimicrobial peptide recognition. Bioinformatics 34, 2740–2747 (2018).
Google Scholar
Waghu, F. H., Barai, R. S., Gurung, P. & Idicula-Thomas, S. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res. 44, D1094-1097 (2016).
Google Scholar
Joseph, S., Karnik, S., Nilawe, P., Jayaraman, V. K. & Idicula-Thomas, S. ClassAMP: A prediction tool for classification of antimicrobial peptides. IEEE/ACM Trans. Comput. Biol. Bioinform. 9, 1535–1538 (2012).
Google Scholar
Meher, P. K., Sahu, T. K., Saini, V. & Rao, A. R. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep. 7, 42362 (2017).
Google Scholar
Kavousi, K. et al. IAMPE: NMR-assisted computational prediction of antimicrobial peptides. J. Chem. Inf. Model. 60, 4691–4701 (2020).
Google Scholar
Powers, D. M. W. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011).
Google Scholar
Gorman, B. mltools: Machine Learning Tools. R package version 0.3.5. https://CRAN.R-project.org/package=mltools (2018).
Chicco, D. & Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 21, 6 (2020).
Google Scholar
