Peptide Libraries with Antimicrobial Activity: Comparison
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Please note this is a comparison between Version 2 by Rita Xu and Version 1 by Juan Cruz.

In this review, we describe how by coupling emerging

Authors describe how by coupling emerging

in silico

and experimental tools it is possible to create novel peptide libraries with potential antimicrobial activity. This is in response to the growing public health concern pose by multiresistant microbial strains that take millions of lives annually on a global scale. The 

in silico

tools include emerging artificial intelligence algorithms that allow searching for novel sequences in extremely large databases. Once identified, the required membrane activity can be estimated by looking at the interactions with model lipid bilayers via molecular dynamics (MD) simulations. Experimentally, the sequences can be expressed on the surface of yeasts by the surface display technology and subsequently screened in a high-throughput manner aided by microfluidic systems capable of separating out the most active peptides by precisely monitoring changes in optical properties in-line and real-time. 

  • Antimicrobial peptides
  • recurrent neural networks
  • MD simulations
  • microfluidic separation systems
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References

  1. Naylor, N.R.; Atun, R.; Zhu, N.; Kulasabanathan, K.; Silva, S.; Chatterjee, A.; Knight, G.M.; Robotham, J.V. Estimating the burden of antimicrobial resistance: A systematic literature review. Antimicrob. Resist. Infect. Control 2018, 7.
  2. Stokowski, L.A. Antimicrobial Resistance: A Primer. Available online: (accessed on 3 November 2020).
  3. Mulani, M.S.; Kamble, E.E.; Kumkar, S.N.; Tawre, M.S.; Pardesi, K.R. Emerging Strategies to Combat ESKAPE Pathogens in the Era of Antimicrobial Resistance: A Review. Front. Microbiol. 2019, 10, 539.
  4. Ventola, C.L. The Antibiotic Resistance Crisis: Part 1: Causes and Threats. Pharm. Ther. 2015, 40, 277–283.
  5. World Health Organization (WHO). Antimicrobial Resistance. Available online: (accessed on 3 November 2020).
  6. Sakeena, M.H.F.; Bennett, A.A.; McLachlan, A.J. Enhancing pharmacists’ role in developing countries to overcome the challenge of antimicrobial resistance: A narrative review. Antimicrob. Resist. Infect. Control 2018, 7.
  7. Center for Disease Control and Prevention (CDC). Antibiotic-Resistant Germs: New Threats. Available online: (accessed on 3 November 2020).
  8. El-Mahallawy, H.A.; Hassan, S.S.; El-Wakil, M.; Moneer, M.M. Bacteremia due to ESKAPE pathogens: An emerging problem in cancer patients. J. Egypt. Natl. Cancer Inst. 2016, 28, 157–162.
  9. Marturano, J.E.; Lowery, T.J. ESKAPE Pathogens in Bloodstream Infections Are Associated with Higher Cost and Mortality but Can Be Predicted Using Diagnoses Upon Admission. Open Forum Infect. Dis. 2019, 6.
  10. Pendleton, J.N.; Gorman, S.P.; Gilmore, B.F. Clinical relevance of the ESKAPE pathogens. Expert Rev. Anti Infect. Ther. 2013, 11, 297–308.
  11. Tang, M.W.; Shafer, R.W. HIV-1 Antiretroviral Resistance. Drugs 2012, 72, e1–e25.
  12. Goldhill, D.H.; Te Velthuis, A.J.; Fletcher, R.A.; Langat, P.; Zambon, M.; Lackenby, A.; Barclay, W.S. The mechanism of resistance to favipiravir in influenza. Proc. Natl. Acad. Sci. USA 2018, 115, 11613–11618.
  13. Lázár, V.; Martins, A.; Spohn, R.; Daruka, L.; Grézal, G.; Fekete, G.; Számel, M.; Jangir, P.K.; Kintses, B.; Csörgő, B.; et al. Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides. Nat. Microbiol. 2018, 3, 718–731.
  14. Bechinger, B.; Gorr, S.-U. Antimicrobial Peptides: Mechanisms of Action and Resistance. J. Dent. Res. 2016, 96, 254–260.
  15. Anunthawan, T.; De La Fuente-Núñez, C.; Hancock, R.E.; Klaynongsruang, S. Cationic amphipathic peptides KT2 and RT2 are taken up into bacterial cells and kill planktonic and biofilm bacteria. Biochim. Biophys. Acta Biomembr. 2015, 1848, 1352–1358.
  16. Malanovic, N.; Lohner, K. Gram-positive bacterial cell envelopes: The impact on the activity of antimicrobial peptides. Biochim. Biophys. Acta Biomembr. 2016, 1858, 936–946.
  17. Bechinger, B. The SMART model: Soft Membranes Adapt and Respond also Transiently, in the presence of antimicrobial peptides. J. Pept. Sci. 2014, 21, 346–355.
  18. Boas, L.C.P.V.; Campos, M.L.; Berlanda, R.L.A.; de Carvalho Neves, N.; Franco, O.L. Antiviral peptides as promising therapeutic drugs. Cell. Mol. Life Sci. 2019, 76, 3525–3542.
  19. Waghu, F.H.; Joseph, S.; Ghawali, S.; Martis, E.A.; Madan, T.; Venkatesh, K.V.; Idicula-Thomas, S. Designing antibacterial peptides with enhanced killing kinetics. Front. Microbiol. 2018, 9, 325.
  20. Torres, M.D.; Sothiselvam, S.; Lu, T.K.; de la Fuente-Nunez, C. Peptide design principles for antimicrobial applications. J. Mol. Biol. 2019, 431, 3547–3567.
  21. Dean, S.N.; Walper, S.A. Variational Autoencoder for Generation of Antimicrobial Peptides. ACS Omega 2020, 5, 20746–20754.
  22. Lin, E.; Lin, C.H.; Lane, H.Y. Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design Dimensionality Reduction, and De Novo Peptide and Protein Design. Molecules 2020, 25, 3250.
  23. Kalafatovic, D.; Mauša, G.; Todorovski, T.; Giralt, E. Algorithm-supported, mass and sequence diversity-oriented random peptide library design. J. Cheminform. 2019, 11, 25.
  24. Henninot, A.; Collins, J.C.; Nuss, J.M. The current state of peptide drug discovery: Back to the future? J. Med. Chem. 2018, 61, 1382–1414.
  25. Liu, R.; Li, X.; Lam, K.S. Combinatorial chemistry in drug discovery. Curr. Opin. Chem. Biol. 2017, 38, 117–126.
  26. Hamzeh-Mivehroud, M.; Alizadeh, A.A.; Morris, M.B.; Church, W.B.; Dastmalchi, S. Phage display as a technology delivering on the promise of peptide drug discovery. Drug Discov. Today 2013, 18, 1144–1157.
  27. Guralp, S.A.; Murgha, Y.E.; Rouillard, J.M.; Gulari, E. From design to screening: A new antimicrobial peptide discovery pipeline. PLoS ONE 2013, 8, e59305.
  28. Lee, S.Y.; Choi, J.H.; Xu, Z. Microbial cell-surface display. Trends Biotechnol. 2003, 21, 45–52.
  29. Lane, N.; Kahanda, I. DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. In Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Switzerland, 2020; pp. 60–69.
  30. Müller, A.T.; Hiss, J.A.; Schneider, G. Recurrent Neural Network Model for Constructive Peptide Design. J. Chem. Inf. Model. 2018, 58, 472–479.
  31. Goh, G.B.; Siegel, C.; Vishnu, A.; Hodas, N. Using Rule-Based Labels for Weak Supervised Learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 19–23 August 2018; Association for Computing Machinery: New York, NY, USA, 2018.
  32. Tiwary, S.; Levy, R.; Gutenbrunner, P.; Soto, F.S.; Palaniappan, K.K.; Deming, L.; Berndl, M.; Brant, A.; Cimermancic, P.; Cox, J. High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nat. Methods 2019, 16, 519–525.
  33. Goodfellow, I.; Bengio, Y.; Courville, A.; Bengio, Y. Deep Learning; MIT Press Cambridge: Cambridge, MA, USA, 2016; Volume 1.
  34. Wahnström, G. Molecular Dynamics Lecture Notes; Chalmers University of Technology: Gothenburg, Sweden, 2018.
  35. Feig, M.; Nawrocki, G.; Yu, I.; Wang, P.-H.; Sugita, Y. Challenges and opportunities in connecting simulations with experiments via molecular dynamics of cellular environments. J. Phys. Conf. Ser. 2018, 1036.
  36. Allen, M.P. Introduction to Molecular Dynamics Simulation. In Computational Soft Matter: From Synthetic Polymers to Proteins; John von Neumann Institute for Computing (NIC): Jülich, Germany, 2004.
  37. Rathore, N.; Pablo, J.J. de Monte Carlo simulation of proteins through a random walk in energy space. J. Chem. Phys. 2002, 116, 7225–7230.
  38. Gofman, Y.; Haliloglu, T.; Ben-Tal, N. Monte-Carlo Simulations of Peptide-Membrane Interactions: Web-Server. Biophys. J. 2010, 98, 487a.
  39. Karplus, M.; McCammon, J.A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 2002, 9, 646–652.
  40. Reif, M.; Zacharias, M. Computer Modelling and Molecular Dynamics Simulation of Biomolecules. In Biomolecular and Bioanalytical Techniques; John Wiley & Sons Ltd.: New York, NY, USA, 2019; pp. 501–535.
  41. Alder, B.J.; Wainwright, T.E. Phase Transition for a Hard Sphere System. J. Chem. Phys. 1957, 27, 1208–1209.
  42. Hollingsworth, S.A.; Dror, R.O. Molecular Dynamics Simulation for All. Neuron 2018, 99, 1129–1143.
  43. Hernández, E.R.; Zetina, L.M.M.; Vega, G.T.; Rocha, M.G.; Ochoa, L.F.R.; Fernandez, R.L. Molecular Dynamics: From basic techniques to applications (A Molecular Dynamics Primer). In AIP Conference Proceedings; AIP: College Park, MD, USA, 2008.
  44. Aliaga, L.C.R.; Lima, L.V.P.C.; Domingues, G.M.B.; Bastos, I.N.; Evangelakis, G.A. Experimental and molecular dynamics simulation study on the glass formation of Cu-Zr-Al alloys. Mater. Res. Express 2019, 6.
  45. Chen, J. The Development and Comparison of Molecular Dynamics Simulation and Monte Carlo Simulation. IOP Conf. Ser. Earth Environ. Sci. 2018, 128.
  46. Neyts, E.C.; Bogaerts, A. Combining molecular dynamics with Monte Carlo simulations: Implementations and applications. Theor. Chem. Acc. Belg. 2012, 132.
  47. Kikuchi, K.; Yoshida, M.; Maekawa, T.; Watanabe, H. Metropolis Monte Carlo method as a numerical technique to solve the FokkerPlanck equation. Chem. Phys. Lett. 1991, 185, 335–338.
  48. Cuendet, M.A.; van Gunsteren, W.F. On the calculation of velocity-dependent properties in molecular dynamics simulations using the leapfrog integration algorithm. J. Chem. Phys. 2007, 127.
  49. Case, D.A.; Cheatham, T.E.; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem. 2005, 26, 1668–1688.
  50. Spoel, D.V.D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast flexible, and free. J. Comput. Chem. 2005, 26, 1701–1718.
  51. Brooks, B.R.; Brooks, C.L.; Mackerell, A.D.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545–1614.
  52. Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781–1802.
  53. Plimpton, S. Fast Parallel Algorithms for Short-Range Molecular Dynamics. J. Comput. Phys. 1995, 117, 1–19.
  54. Smith, W.; Yong, C.W.; Rodger, P.M. DL_POLY: Application to molecular simulation. Mol. Simul. 2002, 28, 385–471.
  55. FrantzDale, B.; Plimpton, S.J.; Shephard, M.S. Software components for parallel multiscale simulation: An example with LAMMPS. Eng. Comput. 2009, 26, 205–211.
  56. Hernández-Rodríguez, M.; Rosales-Hernández, M.C.; Mendieta-Wejebe, J.E.; Martínez-Archundia, M.; Basurto, J.C. Current Tools and Methods in Molecular Dynamics (MD) Simulations for Drug Design. Curr. Med. Chem. 2016, 23, 3909–3924.
  57. Martinez-Seara, H.; Róg, T. Molecular Dynamics Simulations of Lipid Bilayers: Simple Recipe of How to Do It. In Methods in Molecular Biology; Humana Press: Totowa, NJ, USA, 2012; pp. 407–429.
  58. Langham, A.; Kaznessis, Y.N. Molecular Simulations of Antimicrobial Peptides. In Methods in Molecular Biology; Humana Press: Totowa, NJ, USA, 2009; pp. 267–285.
  59. Shahane, G.; Ding, W.; Palaiokostas, M.; Orsi, M. Physical properties of model biological lipid bilayers: Insights from all-atom molecular dynamics simulations. J. Mol. Model. 2019, 25.
  60. Bharadwaj, P.; Solomon, T.; Malajczuk, C.J.; Mancera, R.L.; Howard, M.; Arrigan, D.W.M.; Newsholme, P.; Martins, R.N. Role of the cell membrane interface in modulating production and uptake of Alzheimers beta amyloid protein. Biochim. Biophys. Acta Biomembr. 2018, 1860, 1639–1651.
  61. Szlasa, W.; Zendran, I.; Zalesińska, A.; Tarek, M.; Kulbacka, J. Lipid composition of the cancer cell membrane. J. Bioenerg. Biomembr. 2020, 52, 321–342.
  62. Revin, V.V.; Gromova, N.V.; Revina, E.S.; Martynova, M.I.; Seikina, A.I.; Revina, N.V.; Imarova, O.G.; Solomadin, I.N.; Tychkov, A.Y.; Zhelev, N. Role of Membrane Lipids in the Regulation of Erythrocytic Oxygen-Transport Function in Cardiovascular Diseases. BioMed Res. Int. 2016, 2016, 3429604.
  63. Fu, Y.; Luo, J.; Qin, J.; Yang, M. Screening techniques for the identification of bioactive compounds in natural products. J. Pharm. Biomed. Anal. 2019, 168, 189–200.
  64. Kim, H.S.; Hsu, S.-C.; Han, S.-I.; Thapa, H.R.; Guzman, A.R.; Browne, D.R.; Tatli, M.; Devarenne, T.P.; Stern, D.B.; Han, A. High-throughput droplet microfluidics screening platform for selecting fast-growing and high lipid-producing microalgae from a mutant library. Plant Direct 2017, 1, e00011.
  65. Barata, D.; van Blitterswijk, C.; Habibovic, P. High-throughput screening approaches and combinatorial development of biomaterials using microfluidics. Acta Biomater. 2016, 34, 1–20.
  66. Kaushik, A.M.; Hsieh, K.; Wang, T.-H. Droplet microfluidics for high-sensitivity and high-throughput detection and screening of disease biomarkers. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 2018, 10.
  67. Zhu, X.-D.; Shi, X.; Wang, S.-W.; Chu, J.; Zhu, W.-H.; Ye, B.-C.; Zuo, P.; Wang, Y.-H. High-throughput screening of high lactic acid-producing Bacillus coagulans by droplet microfluidic based flow cytometry with fluorescence activated cell sorting. RSC Adv. 2019, 9, 4507–4513.
  68. Longwell, C.K.; Labanieh, L.; Cochran, J.R. High-throughput screening technologies for enzyme engineering. Curr. Opin. Biotechnol. 2017, 48, 196–202.
  69. Prodanović, R.; Ung, W.L.; Đurđić, K.I.; Fischer, R.; Weitz, D.A.; Ostafe, R. A high-throughput screening system based on droplet microfluidics for glucose oxidase gene libraries. Molecules 2020, 25, 2418.
  70. Mashaghi, S.; Abbaspourrad, A.; Weitz, D.A.; van Oijen, A.M. Droplet microfluidics: A tool for biology chemistry and nanotechnology. TrAC Trends Anal. Chem. 2016, 82, 118–125.
  71. Li, X.; Yang, X.; Liu, L.; Zhou, P.; Zhou, J.; Shi, X.; Wang, Y. A microarray platform designed for high-throughput screening the reaction conditions for the synthesis of micro/nanosized biomedical materials. Bioact. Mater. 2020, 5, 286–296.
  72. Holland-Moritz, D.A.; Wismer, M.K.; Mann, B.F.; Farasat, I.; Devine, P.; Guetschow, E.D.; Mangion, I.; Welch, C.J.; Moore, J.C.; Sun, S.; et al. Mass Activated Droplet Sorting (MADS) Enables High-Throughput Screening of Enzymatic Reactions at Nanoliter Scale. Angew. Chem. Int. Ed. 2020, 59, 4470–4477.
  73. Lim, J.W.; Shin, K.S.; Moon, J.; Lee, S.K.; Kim, T. A Microfluidic Platform for High-Throughput Screening of Small Mutant Libraries. Anal. Chem. 2016, 88, 5234–5242.
  74. Che, Y.-J.; Wu, H.-W.; Hung, L.-Y.; Liu, C.-A.; Chang, H.-Y.; Wang, K.; Lee, G.-B. An integrated microfluidic system for screening of phage-displayed peptides specific to colon cancer cells and colon cancer stem cells. Biomicrofluidics 2015, 9.
  75. Mahlapuu, M.; Håkansson, J.; Ringstad, L.; Björn, C. Antimicrobial Peptides: An Emerging Category of Therapeutic Agents. Front. Cell. Infect. Microbiol. 2016, 6.
  76. Kumar, P.; Kizhakkedathu, J.; Straus, S. Antimicrobial Peptides: Diversity Mechanism of Action and Strategies to Improve the Activity and Biocompatibility In Vivo. Biomolecules 2018, 8, 4.
  77. Mojsoska, B.; Jenssen, H. Peptides and Peptidomimetics for Antimicrobial Drug Design. Pharmaceuticals 2015, 8, 366–415.
  78. Waghu, F.H.; Barai, R.S.; Gurung, P.; Idicula-Thomas, S. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides: Table 1. Nucleic Acids Res. 2015, 44, D1094–D1097.
  79. Pirtskhalava, M.; Amstrong, A.A.; Grigolava, M.; Chubinidze, M.; Alimbarashvili, E.; Vishnepolsky, B.; Gabrielian, A.; Rosenthal, A.; Hurt, D.E.; Tartakovsky, M. DBAASP v3: Database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res. 2020.
  80. Kang, X.; Dong, F.; Shi, C.; Liu, S.; Sun, J.; Chen, J.; Li, H.; Xu, H.; Lao, X.; Zheng, H. DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci. Data 2019, 6.
  81. Zheng, Z.; Tharmalingam, N.; Liu, Q.; Jayamani, E.; Kim, W.; Fuchs, B.B.; Zhang, R.; Vilcinskas, A.; Mylonakis, E. Synergistic efficacy of Aedes aegypti antimicrobial peptide cecropin A2 and tetracycline against Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 2017, 61.
  82. Martin, G.E.; Boudreau, R.M.; Couch, C.; Becker, K.A.; Edwards, M.J.; Caldwell, C.C.; Gulbins, E.; Seitz, A. Sphingosine’s role in epithelial host defense: A natural antimicrobial and novel therapeutic. Biochimie 2017, 141, 91–96.
  83. Sedaghati, M.; Ezzatpanah, H.; Boojar, M.M.A.; Ebrahimi, M.T.; Kobarfard, F. Isolation and identification of some antibacterial peptides in the plasmin-digest of ββββ-casein. LWT Food Sci. Technol. 2016, 68, 217–225.
  84. Harmouche, N.; Aisenbrey, C.; Porcelli, F.; Xia, Y.; Nelson, S.E.D.; Chen, X.; Raya, J.; Vermeer, L.; Aparicio, C.; Veglia, G.; et al. Solution and solid-state nuclear magnetic resonance structural investigations of the antimicrobial designer peptide GL13K in membranes. Biochemistry 2017, 56, 4269–4278.
  85. Speck-Planche, A.; Kleandrova, V.V.; Ruso, J.M.; DS Cordeiro, M.N. First multitarget chemo-Bioinformatic model to enable the discovery of antibacterial peptides against multiple gram-positive pathogens. J. Chem. Inf. Model. 2016, 56, 588–598.
  86. Bayer, A.; Lammel, J.; Tohidnezhad, M.; Lippross, S.; Behrendt, P.; Klüter, T.; Pufe, T.; Cremer, J.; Jahr, H.; Rademacher, F.; et al. The antimicrobial peptide human beta-defensin-3 is induced by platelet-released growth factors in primary keratinocytes. Mediat. Inflamm. 2017, 2017.
  87. Juretić, D.; Vukičević, D.; Tossi, A. Tools for designing amphipathic helical antimicrobial peptides. In Antimicrobial Peptides; Springer: Cham, Switzerland, 2017; pp. 23–34.
  88. Seyfi, R.; Kahaki, F.A.; Ebrahimi, T.; Montazersaheb, S.; Eyvazi, S.; Babaeipour, V.; Tarhriz, V. Antimicrobial peptides (AMPs): Roles, functions and mechanism of action. Int. J. Pept. Res. Ther. 2019, 1451–1463.
  89. Wang, C.-K.; Shih, L.-Y.; Chang, K.Y. Large-scale analysis of antimicrobial activities in relation to amphipathicity and charge reveals novel characterization of antimicrobial peptides. Molecules 2017, 22, 2037.
  90. Chew, M.-F.; Poh, K.-S.; Poh, C.-L. Peptides as therapeutic agents for dengue virus. Int. J. Med. Sci. 2017, 14, 1342–1359.
  91. Sadredinamin, M.; Mehrnejad, F.; Hosseini, P.; Doustdar, F. Antimicrobial Peptides (AMPs). Nov. Biomed. 2016, 4, 70–76.
  92. Da Mata, É.C.G.; Mourão, C.B.F.; Rangel, M.; Schwartz, E.F. Antiviral activity of animal venom peptides and related compounds. J. Venom. Anim. Toxins Incl. Trop. Dis. 2017, 23, 3.
  93. Rautenbach, M.; Troskie, A.M.; Vosloo, J.A. Antifungal peptides: To be or not to be membrane active. Biochimie 2016, 130, 132–145.
  94. Faruck, M.O.; Yusof, F.; Chowdhury, S. An overview of antifungal peptides derived from insect. Peptides 2016, 80, 80–88.
  95. Muhialdin, B.J.; Hassan, Z.; Bakar, F.A.; Saari, N. Identification of antifungal peptides produced by Lactobacillus plantarum IS10 grown in the MRS broth. Food Control 2016, 59, 27–30.
  96. Mor, A. Multifunctional host defense peptides: Antiparasitic activities. FEBS J. 2009, 276, 6474–6482.
  97. Lacerda, A.F.; Pelegrini, P.B.; de Oliveira, D.M.; Vasconcelos, É.A.; Grossi-de-Sá, M.F. Anti-parasitic Peptides from Arthropods and their Application in Drug Therapy. Front. Microbiol. 2016, 7.
  98. Pretzel, J.; Mohring, F.; Rahlfs, S.; Becker, K. Antiparasitic Peptides. In Advances in Biochemical Engineering/Biotechnology; Springer: Berlin/Heidelberg, Germany, 2013; pp. 157–192.
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