Diseño racional y validación in silico e in vitro de péptidos antimicrobianos sintéticos inspirados en proteomas de especies de la biodiversidad colombiana

Vista sencilla de ítem
dc.contributor.advisorOrduz Peralta, Sergio
dc.contributor.authorMesa Gómez, Andrea
dc.contributor.orcidMesa, Andrea [0000000253873732]
dc.contributor.orcidOrdúz Peralta, Sergio [0000000175873816]
dc.contributor.researchgroupBiología Funcional
dc.date.accessioned2026-02-26T20:16:20Z
dc.date.available2026-02-26T20:16:20Z
dc.date.issued2025-11-20
dc.descriptionIlustraciones
dc.description.abstractLa creciente resistencia de los microrganismos a los antibióticos y la baja eficacia de los antivirales representan una crisis de salud pública con un impacto significativo, estimándose que para 2050 esta problemática podría causar alrededor de 10 millones de muertes anuales. Ante esta situación, los péptidos antimicrobianos y antivirales han emergido como alternativas prometedoras debido a su alta especificidad y menor propensión a generar resistencia. Colombia por su biodiversidad ofrece un recurso valioso para la identificación de péptidos con potencial terapéutico. En este estudio, se utilizaron plataformas bioinformáticas para analizar el proteoma de 20 organismos, obteniendo 17,483,597 péptidos, de los cuales se seleccionaron ocho con alta predicción de actividad antimicrobiana (>95 %) y antiviral (>70 %), además de cumplir con parámetros favorables de bioseguridad y estabilidad biológica. Posteriormente, estos péptidos fueron optimizados mediante modificaciones estructurales para mejorar su estabilidad y bioseguridad. La validación in silico, mediante acoplamiento molecular con la proteína Spike del SARS-CoV-2, evidenció que los péptidos Vp-P1 y Pl-P4 establecen interacciones fuertes con el dominio viral, sugiriendo una posible inhibición de la entrada del virus en células huésped. Asimismo, los péptidos Pl-P4M y Am-P5 mostraron interacciones cercanas, estables y fuertes con la tríada catalítica del complejo NS2B-NS3 del virus del Dengue, indicando su potencial inhibitorio en el procesamiento de la poliproteína viral. Además, la evaluación in vitro contra microorganismos evidenció que siete de los diez péptidos presentaron actividad significativa (MIC <10 µM). Estos hallazgos resaltan su potencial para estudios preclínicos, reafirmando la importancia de las estrategias utilizadas en el desarrollo de nuevos compuestos contra patógenos de interés clínico. Palabras clave: Biodiversidad, Péptidos, Antivirales, Antimicrobianos, Acoplamiento molecular . (Texto tomado de la fuente)spa
dc.description.abstractThe increasing resistance of microorganisms to antibiotics and the low efficacy of antiviral drugs represent a public health crisis with significant impact. It is estimated that by 2050, this issue could cause around ten million deaths annually. In response to this challenge, antimicrobial and antiviral peptides have emerged as promising alternatives due to their high specificity and lower propensity to induce resistance. Colombia's biodiversity offers a valuable resource for the identification of peptides with therapeutic potential. In this study, bioinformatics platforms were used to analyze the proteome of 20 organisms, yielding 17,483,597 peptides, from which eight were selected based on highly predicted antimicrobial activity (>95%) and antiviral activity (>70%), as well as favorable biosafety and biological stability parameters. These peptides were subsequently optimized through structural modifications to enhance their stability and biosafety. The in silico validation, performed via molecular docking with the SARS-CoV-2 Spike protein, revealed that the Vp-P1 and Pl-P4 peptides establish strong interactions with the viral domain, suggesting a potential inhibition of viral entry into host cells. Additionally, the Pl-P4M and Am-P5 peptides exhibited close, stable, and strong interactions with the catalytic triad of the NS2B-NS3 complex of the Dengue virus, indicating their inhibitory potential in viral polyprotein processing. Furthermore, in vitro evaluation against microorganisms demonstrated that seven out of ten peptidesexhibited significant activity (MIC <10 µM). These findings highlight their potential for preclinical studies, reaffirming the importance of the straeng
dc.description.curricularareaBiotecnología.Sede Medellín
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ciencias – Biotecnología
dc.format.extent1 recurso en línea (156 páginas)
dc.format.mimetypeapplication/pdf
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/89691
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia, Sede Medellín
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellín
dc.publisher.facultyFacultad de Ciencias
dc.publisher.placeMedellín, Colombia
dc.publisher.programMedellín - Ciencias - Maestría en Ciencias - Biotecnología
dc.relation.referencesAbduraman, M. A., Hariono, M., Yusof, R., Rahman, N. A., Wahab, H. A., & Tan, M. L. (2018). Development of a NS2B/NS3 protease inhibition assay using AlphaScreen® beads for screening of anti-dengue activities. Heliyon, 4(12), e01023. https://doi.org/10.1016/j.heliyon.2018.e01023
dc.relation.referencesAgarwal, G., & Gabrani, R. (2021). Antiviral Peptides: Identification and validation. International Journal of Peptide Research and Therapeutics, 27(1), 149–168. https://doi.org/10.1007/s10989-020-10072-0
dc.relation.referencesAgüero-Chapin, G., Galpert-Cañizares, D., Domínguez-Pérez, D., Marrero-Ponce, Y., Pérez-Machado, G., Teijeira, M., & Antunes, A. (2022). Emerging computational approaches for antimicrobial peptide discovery. Antibiotics (Basel, Switzerland), 11(7), 936. https://doi.org/10.3390/antibiotics11070936
dc.relation.referencesAhmed, A., Siman-Tov, G., Hall, G., Bhalla, N., & Narayanan, A. (2019). Human antimicrobial peptides as therapeutics for viral infections. Viruses, 11(8), 704. https://doi.org/10.3390/v11080704
dc.relation.referencesAlbagi, S. O., Al-Nour, M. Y., Elhag, M., Tageldein Idris Abdelihalim, A., Musa Haroun, E., Adam Essa, M. E., Abubaker, M., Deka, H., Ghosh, A., & Hassan, M. A. (2020). A multiple peptides vaccine against COVID-19 designed from the nucleocapsid phosphoprotein (N) and spike glycoprotein (S) via the immunoinformatics approach. Informatics in Medicine Unlocked, 21, 100476. https://doi.org/10.1016/j.imu.2020.100476
dc.relation.referencesAlmeida, M. S., Cabral, K. M., Kurtenbach, E., Almeida, F. C., & Valente, A. P. (2002). Solution structure of pisum sativum defensin 1 by high resolution nmr: plant defensins, identical backbone with different mechanisms of action. Journal of Molecular Biology, 315(4), 749–757. https://doi.org/10.1006/jmbi.2001.5252
dc.relation.referencesAlnomasy, S. F., Alotaibi, B. S., Aldosari, Z. M., Mujamammi, A. H., Alzamami, A., Anand, P., Akhter, Y., Khan, F. R., & Hasan, M. R. (2023). Molecular interactions of Zyesami with the SARS-CoV-2 nsp10/nsp16 protein complex. Combinatorial Chemistry & High Throughput Screening, 26(6), 1196–1203. https://doi.org/10.2174/1386207325666220816141028
dc.relation.referencesAl-Hamdani, Y. S., & Tkatchenko, A. (2019). Understanding non-covalent interactions in larger molecular complexes from first principles. The Journal of chemical physics, 150(1), 010901. https://doi.org/10.1063/1.5075487
dc.relation.referencesAnusha, G., & Monisha, M. (2023). Molecular modeling and screening of antiviral peptides for Influenza A virus polymerase basic protein 2(PB2) protein using Hpepdock software for the therapy of Influenza A. Cardiometry. https://doi.org/10.18137/cardiometry.2022.25.16931701.
dc.relation.referencesAslam, B., Wang, W., Arshad, M. I., Khurshid, M., Muzammil, S., Rasool, M. H., Nisar, M. A., Alvi, R. F., Aslam, M. A., Qamar, M. U., Salamat, M., & Baloch, Z. (2018). Antibiotic resistance: a rundown of a global crisis. Infection and Drug Resistance, 11, 1645–1658. https://doi.org/10.2147/IDR.S173867
dc.relation.referencesAzuma, E., Choda, N., Odaki, M., Yano, Y., & Matsuzaki, K. (2020). Improvement of therapeutic index by the combination of enhanced peptide cationicity and proline introduction. ACS Infectious Diseases, 6(8), 2271–2278. https://doi.org/10.1021/acsinfecdis.0c00387
dc.relation.referencesBacalum, M., Radu, M. (2015). Cationic antimicrobial peptides cytotoxicity on mammalian cells: An analysis using therapeutic index integrative concept. International Journal of Peptide Research and Therapeutics, 21(1): 47-55. https://doi.org/10.1007/s10989-014-9430-z
dc.relation.referencesBadani, H., Garry, R. F., & Wimley, W. C. (2014). Peptide entry inhibitors of enveloped viruses: the importance of interfacial hydrophobicity. Biochimica et Biophysica Acta, 1838(9), 2180–2197. https://doi.org/10.1016/j.bbamem.2014.04.015
dc.relation.referencesBertrand, X., & Dowzicky, M. J. (2012). Antimicrobial susceptibility among gram-negative isolates collected from intensive care units in North America, Europe, the Asia-Pacific Rim, Latin America, the Middle East, and Africa between 2004 and 2009 as part of the Tigecycline evaluation and surveillance trial. Clinical Therapeutics, 34(1), 124–137. https://doi.org/10.1016/j.clinthera.2011.11.023
dc.relation.referencesBiswas, S., Mahmud, S., Mita, M. A., Afrose, S., Hasan, M. R., Sultana Shimu, M. S., Saleh, M. A., Mostafa-Hedeab, G., Alqarni, M., Obaidullah, A. J., & Batiha, G. E. (2022). Molecular docking and dynamics studies to explore effective inhibitory peptides against the spike receptor-binding domain of SARS-CoV-2. Frontiers in Molecular Biosciences, 8, 791642. https://doi.org/10.3389/fmolb.2021.791642
dc.relation.referencesBoman, H. G. (2003). Antibacterial peptides: basic facts and emerging concepts. Journal of Internal Medicine, 254(3), 197–215. https://doi.org/10.1046/j.1365-2796.2003.01228.x
dc.relation.referencesBonifacio-Velez de Villa, E. I., Montoya-Alfaro, M. E., Negrón-Ballarte, L. P., & Solis-Calero, C. (2025). Predicting antimicrobial peptide activity: a machine learning-based quantitative structure-activity relationship approach. Pharmaceutics, 17(8), 993. https://doi.org/10.3390/pharmaceutics17080993
dc.relation.referencesBurley, S. K., Berman, H. M., Kleywegt, G. J., Markley, J. L., Nakamura, H., & Velankar, S. (2019). Protein Data Bank (PDB): The single global macromolecular structure archive. Methods in Molecular Biology, 1958, 1-28. https://doi.org/10.1007/978-1-4939-9173-0_1
dc.relation.referencesCabarcas-Montalvo, M., Maldonado-Rojas, W., Montes-Grajales, D., Bertel-Sevilla, A., Wagner-Döbler, I., Sztajer, H., Reck, M., Flechas-Alarcon, M., Ocazionez, R., & Olivero-Verbel, J. (2016). Discovery of antiviral molecules for Dengue: In silico search and biological evaluation. European Journal of Medicinal Chemistry, 110, 87–97. https://doi.org/10.1016/j.ejmech.2015.12.030
dc.relation.referencesCai, Y., Zhang, J., Xiao, T., Lavine, C. L., Rawson, S., Peng, H., Zhu, H., Anand, K., Tong, P., Gautam, A., Lu, S., Sterling, S. M., Walsh, R. M., Jr, Rits-Volloch, S., Lu, J., Wesemann, D. R., Yang, W., Seaman, M. S., & Chen, B. (2021). Structural basis for enhanced infectivity and immune evasion of SARS-CoV-2 variants. Science, 373(6555), 642–648. https://doi.org/10.1126/science.abi9745
dc.relation.referencesCândido, E. S., Pinto, M. F., Pelegrini, P. B., Lima, T. B., Silva, O. N., Pogue, R., Grossi-de-Sá, M. F., & Franco, O. L. (2011). Plant storage proteins with antimicrobial activity: novel insights into plant defense mechanisms. The Federation of American Societies for Experimental Biology Journal: 25(10), 3290–3305. https://doi.org/10.1096/fj.11-184291
dc.relation.referencesCao, F., Ma, G., Song, M., Zhu, G., Mei, L., & Qin, Q. (2021). Evaluating the effects of hydrophobic and cationic residues on antimicrobial peptide self-assembly. Soft Matter, 17(16), 4445–4451. https://doi.org/10.1039/d1sm00096a
dc.relation.referencesCardoso, M. H., Orozco, R. Q., Rezende, S. B., Rodrigues, G., Oshiro, K., Cândido, E. S., & Franco, O. L. (2020). Computer-aided design of antimicrobial peptides: Are we generating effective drug candidates?. Frontiers in Microbiology, 10, 3097. https://doi.org/10.3389/fmicb.2019.03097
dc.relation.referencesCenters for Disease Control and Prevention (CDC). (2019). Antibiotic resistance threats in the United States, 2019. U.S. Department of Health and Human Services. https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf
dc.relation.referencesCiemny, M. P., Kurcinski, M., Blaszczyk, M., Kolinski, A., & Kmiecik, S. (2017). Modeling EphB4-EphrinB2 protein-protein interaction using flexible docking of a short linear motif. Biomedical Engineering Online, 16(Suppl 1), 71. https://doi.org/10.1186/s12938-017-0362-7
dc.relation.referencesCiemny, M., Kurcinski, M., Kamel, K., Kolinski, A., Alam, N., Schueler-Furman, O., & Kmiecik, S. (2018). Protein-peptide docking: Opportunities and challenges. Drug Discovery Today, 23(8), 1530–1537. https://doi.org/10.1016/j.drudis.2018.05.006
dc.relation.referencesChaparro, P., Camerano, R., Santana, D., & Zabaleta, K. (2023). Resistencia antimicrobiana: un problema invisible (Policy Brief). Observatorio Nacional de Salud, Instituto Nacional de Salud. https://www.ins.gov.co/Direcciones/ONS/publicaciones%20alternas/Policy%20Brief%20Resistencia%20antimicrobiana%20un%20problema%20invisible.pdf
dc.relation.referencesChen, C., Hu, J., Zhang, S., Zhou, P., Zhao, X., Xu, H., Zhao, X., Yaseen, M., & Lu, J. R. (2012). Molecular mechanisms of antibacterial and antitumor actions of designed surfactant-like peptides. Biomaterials, 33(2), 592–603. https://doi.org/10.1016/j.biomaterials.2011.09.059
dc.relation.referencesChen, H., & Hoover, D. G. (2003). Bacteriocins and their food applications. Comprehensive Reviews in Food Science and Food Safety, 2(3), 82-100. https://doi.org/10.1111/j.1541-4337.2003.tb00016.x
dc.relation.referencesChen, J., Hao, D., Mei, K., Li, X., Li, T., Ma, C., Xi, X., Li, L., Wang, L., Zhou, M., Chen, T., Liu, J., & Wu, Q. (2021). In Vitro and In Vivo studies on the antibacterial activity and safety of a new antimicrobial peptide Dermaseptin-AC. Microbiology Spectrum, 9(3), e0131821. https://doi.org/10.1128/Spectrum.01318-21
dc.relation.referencesChen, S.-Y., Chang, C.-K., & Lan, C.-Y. (2023). Antimicrobial peptide LL-37 disrupts plasma membrane and calcium homeostasis in Candida albicans via the Rim101 pathway. Microbiology Spectrum, 11(6), e0255123. https://doi.org/10.1128/spectrum.02551-23
dc.relation.referencesChikindas, M. L., Weeks, R., Drider, D., Chistyakov, V. A., & Dicks, L. M. (2018). Functions and emerging applications of bacteriocins. Current Opinion in Biotechnology, 49, 23–28. https://doi.org/10.1016/j.copbio.2017.07.011
dc.relation.referencesClark, T. (2023). How deeply should we analyze non-covalent interactions?. Journal of molecular modeling, 29(3), 66. https://doi.org/10.1007/s00894-023-05460-4
dc.relation.referencesColFungi. (2024). Useful fungi of Colombia. Facilitated by the royal botanic gardens, Kew. https://colfungi.org. Consultado el 9 de septiembre de 2024.
dc.relation.referencesColPlantA. (2024). Useful plants of Colombia. Facilitated by the royal botanic gardens, Kew. https://colplanta.org. Consultado el 9 de septiembre de 2024.
dc.relation.referencesConlon, J. M. (2011). The contribution of skin antimicrobial peptides to the system of innate immunity in anurans. Cell and Tissue Research, 343(1), 201–212. https://doi.org/10.1007/s00441-010-1014-4
dc.relation.referencesCotter, P. D., Ross, R. P., & Hill, C. (2013). Bacteriocins - a viable alternative to antibiotics?. Nature Reviews Microbiology, 11(2), 95–105. https://doi.org/10.1038/nrmicro2937
dc.relation.referencesCui, Z., Crawford, M. A., Rumble, B. A., Krogh, M. M., Hughes, M. A., & Letteri, R. A. (2023). Antimicrobial Peptide-Poly (ethylene glycol) Conjugates: Connecting Molecular Architecture, Solution Properties, and Functional Performance. ACS polymers Au, 4(1), 45–55. https://doi.org/10.1021/acspolymersau.3c00026
dc.relation.referencesDebroy, B., Chowdhury, S., & Pal, K. (2023). Designing a novel and combinatorial multi-antigenic epitope-based vaccine "MarVax" against Marburg virus-a reverse vaccinology and immunoinformatics approach. Journal, Genetic Engineering & Biotechnology, 21(1), 143. https://doi.org/10.1186/s43141-023-00575-w
dc.relation.referencesDeshpande, A., Harris, B. D., Martinez-Sobrido, L., Kobie, J. J., & Walter, M. R. (2021). Epitope Classification and RBD Binding Properties of Neutralizing Antibodies Against SARS-CoV-2 Variants of Concern. Frontiers in immunology, 12, 691715. https://doi.org/10.3389/fimmu.2021.691715
dc.relation.referencesDeslouches, B., Phadke, S. M., Lazarevic, V., Cascio, M., Islam, K., Montelaro, R. C., & Mietzner, T. A. (2005). De novo generation of cationic antimicrobial peptides: influence of length and tryptophan substitution on antimicrobial activity. Antimicrobial Agents and Chemotherapy, 49(1), 316–322. https://doi.org/10.1128/AAC.49.1.316-322.200
dc.relation.referencesDeb, R., Torres, M. D. T., Boudný, M., Koběrská, M., Cappiello, F., Popper, M., Bendová, K. D., Drabinová, M., Hanáčková, A., Jeannot, K., Petřík, M., Mangoni, M. L., Novotná, G. B., Mráz, M., de la Fuente-Nunez, C., & Vácha, R. (2024). Computational design of pore-forming peptides with potent antimicrobial and anticancer activities. Journal of Medicinal Chemistry, 67(16), 14040-14061. https://doi.org/10.1021/acs.jmedchem.4c00912
dc.relation.referencesDe Azevedo, W. F. (2019). Docking screens for drug discovery (1st ed. 2019). Springer New York: Imprint: Humana. https://doi.org/10.1007/978-1-4939-9752-7
dc.relation.referencesDe Moura Cavalheiro, M. C., de Oliveira, C. F. R., de Araújo Boleti, A. P., Rocha, L. S., Jacobowski, A. C., Pedron, C. N., de Oliveira Júnior, V. X., & Macedo, M. L. R. (2024). Evaluating the antimicrobial efficacy of a designed synthetic peptide against pathogenic bacteria. Journal of Microbiology and Biotechnology, 34(11), 2231–2244. https://doi.org/10.4014/jmb.2405.05011
dc.relation.referencesDe Oliveira, K. B. S., Leite, M. L., Melo, N. T. M., Lima, L. F., Barbosa, T. C. Q., Carmo, N. L., Melo, D. A. B., Paes, H. C., & Franco, O. L. (2024). Antimicrobial Peptide Delivery Systems as Promising Tools Against Resistant Bacterial Infections. Antibiotics (Basel, Switzerland), 13(11), 1042. https://doi.org/10.3390/antibiotics13111042
dc.relation.referencesDiekema, D. J., BootsMiller, B. J., Vaughn, T. E., Woolson, R. F., Yankey, J. W., Ernst, E. J., Flach, S. D., Ward, M. M., Franciscus, C. L., Pfaller, M. A., & Doebbeling, B. N. (2004). Antimicrobial resistance trends and outbreak frequency in United States hospitals. Clinical Infectious Diseases, 38(1), 78–85. https://doi.org/10.1086/380457
dc.relation.referencesDimitrov, I., Bangov, I., Flower, D. R., & Doytchinova, I. (2014). AllerTOP v.2--a server for in silico prediction of allergens. Journal of Molecular Modeling, 20(6), 2278. https://doi.org/10.1007/s00894-014-2278-5
dc.relation.referencesDong, R., Liu, R., Liu, Z., Liu, Y., Zhao, G., Li, H., Hou, S., Ma, X., Kang, H., Liu, J., Guo, F., Zhao, P., Wang, J., Wang, C., Wu, X., Ye, S., & Zhu, C. (2025). Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning. eLife. https://doi.org/10.7554/eLife.97330
dc.relation.referencesDuque-Salazar, G., Mendez-Otalvaro, E., Ceballos-Arroyo, A.M., Orduz, S. (2020). Design of antimicrobial and cytolytic peptides by computational analysis of bacterial, algal, and invertebrate proteomes. Amino Acids 52(10):1403-1412. https://doi.org/10.1007/s00726-020-02900-w.
dc.relation.referencesDürr, U. H., Sudheendra, U. S., & Ramamoorthy, A. (2006). LL-37, the only human member of the cathelicidin family of antimicrobial peptides. Biochimica et Biophysica Acta, 1758(9), 1408–1425. https://doi.org/10.1016/j.bbamem.2006.03.030
dc.relation.referencesErbel, P., Schiering, N., D'Arcy, A., Renatus, M., Kroemer, M., Lim, S. P., Yin, Z., Keller, T. H., Vasudevan, S. G., & Hommel, U. (2006). Structural basis for the activation of flaviviral NS3 proteases from dengue and West Nile virus. Nature Structural & Molecular Biology, 13(4), 372–373. https://doi.org/10.1038/nsmb1073
dc.relation.referencesEscobar, J. A., Olarte, N. M., Castro, B., Valderrama, I. A., Garzón, M. I., Martinez, L., Barrero, E. R., Marquez, R. A., Moncada, M. V., Vanegas, N. (2013). Outbreak of NDM-1-producing Klebsiella pneumoniae in a neonatal unit in Colombia. Antimicrobial Agents and Chemotherapy. 57(4):1957–60.
dc.relation.referencesEspeche, J. C., Martínez, M. M. B., Maturana, P., Cutró, A., Semorile, L., Maffía, P., & Hollmann, A. (2020). Unraveling the mechanism of action of “de novo” designed peptide P1 with model membranes and gram-positive and gram-negative bacteria. Archives of Biochemistry and Biophysics, 693. https://doi.org/10.1016/j.abb.2020.108549.
dc.relation.referencesEvans, H. G., Guthrie, J. W., Jujjavarapu, M., Hendrickson, N., Eitel, A., Park, Y., Garvey, J., Newman, R., Esckilsen, D., & Heyl, D. L. (2017). D-Amino Acid Analogues of the Antimicrobial Peptide CDT Exhibit Anti- Cancer Properties in A549, a Human Lung Adenocarcinoma Cell Line. Protein and peptide letters, 24(7), 590–598. https://doi.org/10.2174/0929866524666170621093647
dc.relation.referencesFAO. (2021). Action plan on antimicrobial resistance 2021–2025. Food and Agriculture Organization of the United Nations (FAO). https://www.fao.org/antimicrobial-resistance
dc.relation.referencesFalcigno, L., D'Auria, G., Palmieri, G., Gogliettino, M., Agrillo, B., Tatè, R., Dardano, P., Nicolais, L., & Balestrieri, M. (2021). Key Physicochemical Determinants in the Antimicrobial Peptide RiLK1 Promote Amphipathic Structures. International journal of molecular sciences, 22(18), 10011. https://doi.org/10.3390/ijms221810011
dc.relation.referencesFjell, C. D., Hiss, J. A., Hancock, R. E., & Schneider, G. (2011). Designing antimicrobial peptides: form follows function. Nature Reviews Drug Discovery, 11(1), 37–51. https://doi.org/10.1038/nrd3591
dc.relation.referencesFrederiksen, N., Hansen, P. R., Zabicka, D., Tomczak, M., Urbas, M., Domraceva, I., Björkling, F., & Franzyk, H. (2020). Alternating cationic-hydrophobic peptide/peptoid hybrids: influence of hydrophobicity on antibacterial activity and cell selectivity. ChemMedChem, 15(24), 2544–2561. https://doi.org/10.1002/cmdc.202000526
dc.relation.referencesFrieri, M., Kumar, K., & Boutin, A. (2017). Antibiotic resistance. Journal of Infection and Public Health, 10(4), 369–378. https://doi.org/10.1016/j.jiph.2016.08.007
dc.relation.referencesGagat, P., Ostrówka, M., Duda-Madej, A., & Mackiewicz, P. (2024). Enhancing antimicrobial peptide activity through modifications of charge, hydrophobicity, and structure. International Journal of Molecular Sciences, 25(19), 10821. https://doi.org/10.3390/ijms251910821
dc.relation.referencesGanz, T. (2003). Defensins: antimicrobial peptides of innate immunity. Nature Reviews Immunology, 3(9), 710–720. https://doi.org/10.1038/nri1180
dc.relation.referencesGasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M. R., Appel, R. D., & Bairoch, A. (2005). Protein identification and analysis tools on the ExPASy server. En J. M. Walker (Ed.), The Proteomics Protocols Handbook (pp. 571–607). Humana Press.
dc.relation.referencesGarg, S. K., Singh, O., Juneja, D., Tyagi, N., Khurana, A. S., Qamra, A., Motlekar, S., & Barkate, H. (2017). Resurgence of polymyxin b for MDR/XDR gram-negative infections: An overview of current evidence. Critical Care Research and Practice, 2017, 3635609. https://doi.org/10.1155/2017/3635609
dc.relation.referencesGautam, A., Chaudhary, K., Kumar, R., et al. (2013). In silico approaches for designing highly effective cell penetrating peptides. Journal of Translational Medicine, 11, 74. https://doi.org/10.1186/1479-5876-11-74
dc.relation.referencesGautier, R., Douguet, D., Antonny, B., & Drin, G. (2008). HELIQUEST: a web server to screen sequences with specific alpha-helical properties. Bioinformatics (Oxford, England), 24(18), 2101–2102. https://doi.org/10.1093/bioinformatics/btn392
dc.relation.referencesGawde, U., Chakraborty, S., Waghu, F. H., Barai, R. S., Khanderkar, A., Indraguru, R., Shirsat, T., & Idicula-Thomas, S. (2023). CAMPR4: a database of natural and synthetic antimicrobial peptides. Nucleic Acids Research, 51(D1), D377–D383. https://doi.org/10.1093/nar/gkac933
dc.relation.referencesGBD 2021 Antimicrobial Resistance Collaborators (2024). Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050. Lancet (London, England), 404(10459), 1199–1226. https://doi.org/10.1016/S0140-6736(24)01867-1
dc.relation.referencesGBIF (2024). Global Biodiversity Information Facility. https://www.gbif.org
dc.relation.referencesGhita, B. T., Bennani, L., Berrada, S., Benboubker, M., & Bennani, B. (2020). Molecular serotyping and antibiotic resistance patterns of Escherichia coli isolated in hospital catering service in Morocco. International Journal of Microbiology, 2020, 5961521. https://doi.org/10.1155/2020/5961521
dc.relation.referencesGómez, E, Orduz S. (2017). PepMultiFinder 1.0. Un algoritmo para buscar péptidos bioactivos en proteomas o listas de secuencias de proteínas. Dirección Nacional de Derechos de Autor. Ministerio del Interior. Registro 13-59-134. Marzo 17, 2017
dc.relation.referencesGómez, S.R., Chaves, M.E., Ramírez, W., Santamaría, M., Andrade, G., Solano, C., & Aranguren, S. (Eds.). (2021). Evaluación nacional de biodiversidad y servicios ecosistémicos de Colombia. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Programa de Naciones Unidas para el Desarrollo, Centro Mundial de Monitoreo para la Conservación del Programa de las Naciones Unidas para el Medio Ambiente, & Ministerio Federal de Medio Ambiente, Conservación de la Naturaleza y Seguridad Nuclear de la República Federal de Alemania.
dc.relation.referencesGorr, S. U., Flory, C. M., & Schumacher, R. J. (2019). In vivo activity and low toxicity of the second-generation antimicrobial peptide DGL13K. PloS One, 14(5), e0216669. https://doi.org/10.1371/journal.pone.0216669
dc.relation.referencesGreco, I., Molchanova, N., Holmedal, E., Jenssen, H., Hummel, B. D., Watts, J. L., Håkansson, J., Hansen, P. R., & Svenson, J. (2020). Correlation between hemolytic activity, cytotoxicity and systemic in vivo toxicity of synthetic antimicrobial peptides. Scientific reports, 10(1), 13206. https://doi.org/10.1038/s41598-020-69995-9
dc.relation.referencesGuarracino, D. A., Iannaccone, J., Cabrera, A., & Kancharla, S. (2022). Harnessing the therapeutic potential and biological activity of antiviral peptides. ChemBioChem, 23(20), e202200415. https://doi.org/10.1002/cbic.202200415
dc.relation.referencesGuedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014). Receptor-ligand molecular docking. Biophysical Reviews, 6(1), 75–87. https://doi.org/10.1007/s12551-013-0130-2
dc.relation.referencesGupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., Open Source Drug Discovery Consortium, & Raghava, G. P. (2013). In silico approach for predicting toxicity of peptides and proteins. PloS One, 8(9), e73957. https://doi.org/10.1371/journal.pone.0073957
dc.relation.referencesHadi-Alijanvand, H., Di Paola, L., Hu, G., Leitner, D. M., Verkhivker, G. M., Sun, P., Poudel, H., & Giuliani, A. (2022). Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach. ACS omega, 7(20), 17024–17042. https://doi.org/10.1021/acsomega.2c00154
dc.relation.referencesHamley, I. W. (2017). Small bioactive peptides for biomaterials design and therapeutics. Chemical Reviews, 117(24), 14015-14041. doi:10.1021/acs.chemrev.7b00522
dc.relation.referencesHan, P., Li, L., Liu, S., Wang, Q., Zhang, D., Xu, Z., Han, P., Li, X., Peng, Q., Su, C., Huang, B., Li, D., Zhang, R., Tian, M., Fu, L., Gao, Y., Zhao, X., Liu, K., Qi, J., Gao, G. F., … Wang, P. (2022). Receptor binding and complex structures of human ACE2 to spike RBD from omicron and delta SARS-CoV-2. Cell, 185(4), 630–640.e10. https://doi.org/10.1016/j.cell.2022.01.001
dc.relation.referencesHandelsman, J. (2004). Metagenomics: application of genomics to uncultured microorganisms. Microbiology and Molecular Biology Reviews, 68(4), 669–685. https://doi.org/10.1128/MMBR.68.4.669-685.2004
dc.relation.referencesHarvey, A. L., Edrada-Ebel, R., & Quinn, R. J. (2015). The re-emergence of natural products for drug discovery in the genomics era. Nature Reviews Drug Discovery, 14(2), 111-129. https://doi.org/10.1038/nrd4510
dc.relation.referencesHe, S., & Deber, C. M. (2024). Interaction of designed cationic antimicrobial peptides with the outer membrane of gram-negative bacteria. Scientific reports, 14(1), 1894. https://doi.org/10.1038/s41598-024-51716-1
dc.relation.referencesHeffron, J., & Mayer, B. K. (2021). Virus isoelectric point estimation: theories and methods. Applied and Environmental Microbiology, 87(3), e02319-20. https://doi.org/10.1128/AEM.02319-20
dc.relation.referencesHekkelman, M. L., Perrakis, A., & Joosten, R. P. (n.d.). DSSP: Assign secondary structure to proteins. PDB-REDO. Recuperado de https://pdb-redo.eu/dssp
dc.relation.referencesHicks, R. (2017). Preparation of membrane models of gram-negative bacteria and their interaction with antimicrobial peptides studied by CD and NMR. Methods in Molecular Biology, 1548, 231–245. https://doi.org/10.1007/978-1-4939-6737-7_16
dc.relation.referencesHincapié, O., Giraldo, P., Orduz, S. (2018). In silico design of polycationic antimicrobial peptides active against Pseudomonas aeruginosa and Staphylococcus aureus. Antonie van Leeuwenhoek Journal of Microbiology, 111(10), 1871-1882. https://doi.org/10.1007/s10482-018-1080-2.
dc.relation.referencesHirano, M., Yokoo, H., Ohoka, N., Ito, T., Misawa, T., Oba, M., Inoue, T., & Demizu, Y. (2024). Rational design of amphipathic antimicrobial peptides with alternating L-/D-amino acids that form helical structures. Chemical & Pharmaceutical Bulletin, 72(2), 149–154. https://doi.org/10.1248/cpb.c23-00465
dc.relation.referencesHollmann, A., Cardoso, N. P., Espeche, J. C., & Maffía, P. C. (2021). Review of antiviral peptides for use against zoonotic and selected non-zoonotic viruses. Peptides, 142, 170570. https://doi.org/10.1016/j.peptides.2021.170570
dc.relation.referencesHouyvet, B., Zanuttini, B., Corre, E., Le Corguillé, G., Henry, J., & Zatylny-Gaudin, C. (2018). Design of antimicrobial peptides from a cuttlefish database. Amino Acids, 50(11), 1573–1582. https://doi.org/10.1007/s00726-018-2633-4
dc.relation.referencesHuai, W., Ma, Q. B., Zheng, J. J., Zhao, Y., & Zhai, Q. R. (2019). Distribution and drug resistance of pathogenic bacteria in emergency patients. World Journal of Clinical Cases, 7(20), 3175–3184. https://doi.org/10.12998/wjcc.v7.i20.3175
dc.relation.referencesHuan, Y., Kong, Q., Mou, H., & Yi, H. (2020). Antimicrobial peptides: classification, design, application, and research progress in multiple fields. Frontiers in Microbiology, 11, 582779. https://doi.org/10.3389/fmicb.2020.582779
dc.relation.referencesHuang, Y., Yang, C., Xu, X. F., Xu, W., & Liu, S. W. (2020). Structural and functional properties of SARS-CoV-2 spike protein: potential antivirus drug development for COVID-19. Acta Pharmacologica Sinica, 41(9), 1141–1149. https://doi.org/10.1038/s41401-020-0485-4
dc.relation.referencesInstituto Nacional de Salud & Observatorio Nacional de Salud. (2022). Memorias reunión de expertos: Carga de la resistencia antimicrobiana en Colombia. Instituto Nacional de Salud, Bogotá, D.C. Disponible en: https://www.ins.gov.co/Direcciones/ONS/SiteAssets/memoriasRAM%20resistencia%20antimicrobiana.pdf
dc.relation.referencesInstituto Nacional de Salud. (2022). Protocolo de vigilancia en salud pública de resistencia bacteriana a los antimicrobianos en el ámbito hospitalario. Disponible en: https://doi.org/10.33610/infoeventos.70
dc.relation.referencesJiang, Z., Mant, C. T., Vasil, M., & Hodges, R. S. (2018). Role of positively charged residues on the polar and non-polar faces of amphipathic α-helical antimicrobial peptides on specificity and selectivity for Gram-negative pathogens. Chemical Biology & Drug Design, 91(1), 75–92. https://doi.org/10.1111/cbdd.13058
dc.relation.referencesJonas, O. B., Irwin, A., Berthe, F. C. J., Le Gall, F. G., & Marquez, P. V. (2017). Drug-resistant infections: A threat to our economic future (Vol. 2): Final report (English). Washington, D.C.: World Bank Group. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/323311493396993758/final-report
dc.relation.referencesJoo, H. S., Fu, C. I., & Otto, M. (2016). Bacterial strategies of resistance to antimicrobial peptides. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 371(1695), 20150292. https://doi.org/10.1098/rstb.2015.0292
dc.relation.referencesJumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
dc.relation.referencesKang, S. J., Kim, D. H., Mishig-Ochir, T., & Lee, B. J. (2012). Antimicrobial peptides: their physicochemical properties and therapeutic application. Archives of pharmacal research, 35(3), 409–413. https://doi.org/10.1007/s12272-012-0302-9
dc.relation.referencesKantroo, H. A., Mubarak, M. M., Chowdhary, R., Rai, R., & Ahmad, Z. (2024). Antifungal efficacy of ultrashort β-peptides against Candida species: mechanistic understanding and therapeutic implications. ACS Infectious Diseases, 10(11), 3736–3743. https://doi.org/10.1021/acsinfecdis.4c00476
dc.relation.referencesKausar, S., Said Khan, F., Ishaq Mujeeb Ur Rehman, M., Akram, M., Riaz, M., Rasool, G., Hamid Khan, A., Saleem, I., Shamim, S., & Malik, A. (2021). A review: Mechanism of action of antiviral drugs. International Journal of Immunopathology and Pharmacology, 35, 20587384211002621. https://doi.org/10.1177/20587384211002621
dc.relation.referencesKikuchi, T., Nagashima, G., Taguchi, K., Kuraishi, H., Nemoto, H., Yamanaka, M., Kawano, R., Ugajin, K., Tazawa, S., & Marumo, K. (2007). Contaminated oral intubation equipment associated with an outbreak of carbapenem-resistant Pseudomonas in an intensive care unit. The Journal of Hospital Infection, 65(1), 54–57. https://doi.org/10.1016/j.jhin.2006.07.017
dc.relation.referencesKim, H., Jang, J. H., Kim, S. C., & Cho, J. H. (2014). De novo generation of short antimicrobial peptides with enhanced stability and cell specificity. The Journal of Antimicrobial Chemotherapy, 69(1), 121–132. https://doi.org/10.1093/jac/dkt322
dc.relation.referencesKoehbach, J., & Craik, D. J. (2019). The vast structural diversity of antimicrobial peptides. Trends in Pharmacological Sciences, 40(7), 517–528. https://doi.org/10.1016/j.tips.2019.04.012
dc.relation.referencesKodedová, M., Valachovič, M., Csáky, Z., & Sychrová, H. (2019). Variations in yeast plasma-membrane lipid composition affect killing activity of three families of insect antifungal peptides. Cellular Microbiology, 21(12), e13093. https://doi.org/10.1111/cmi.13093
dc.relation.referencesKozlowski, L. P. (2016). IPC - Isoelectric point calculator. Biology Direct, 11(1), 55. https://doi.org/10.1186/s13062-016-0159-9
dc.relation.referencesKurcinski, M., Badaczewska-Dawid, A., Kolinski, M., Kolinski, A., & Kmiecik, S. (2020). Flexible docking of peptides to proteins using CABS-dock. Protein Science, 29(1), 211–222. https://doi.org/10.1002/pro.3771
dc.relation.referencesKurcinski, M., Jamroz, M., Blaszczyk, M., Kolinski, A., & Kmiecik, S. (2015). CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Research, 43(W1), W419–W424. https://doi.org/10.1093/nar/gkv456
dc.relation.referencesKwon, J. H., & Powderly, W. G. (2021). The post-antibiotic era is here. Science, 373(6554), 471. https://doi.org/10.1126/science.abl5997
dc.relation.referencesLamiable, A., Thévenet, P., Rey, J., Vavrusa, M., Derreumaux, P., & Tufféry, P. (2016). PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Research, 44(W1), W449–W454. https://doi.org/10.1093/nar/gkw329
dc.relation.referencesLampejo, T. (2020). Influenza and antiviral resistance: an overview. European Journal of Clinical Microbiology & Infectious Diseases, 39(7), 1201–1208. https://doi.org/10.1007/s10096-020-03840-9
dc.relation.referencesLan, J., Ge, J., Yu, J., Shan, S., Zhou, H., Fan, S., et al. (2020). Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature, 581(7807), 215–220. https://doi.org/10.1038/s41586-020-2180-5
dc.relation.referencesLandman, D., Babu, E., Shah, N., Kelly, P., Olawole, O., Bäcker, M., Bratu, S., & Quale, J. (2012). Transmission of carbapenem-resistant pathogens in New York City hospitals: progress and frustration. The Journal of Antimicrobial Chemotherapy, 67(6), 1427–1431. https://doi.org/10.1093/jac/dks063
dc.relation.referencesLarue, R. C., Xing, E., Kenney, A. D., Zhang, Y., Tuazon, J. A., Li, J., Yount, J. S., Li, P. K., & Sharma, A. (2021). Rationally Designed ACE2-Derived Peptides Inhibit SARS-CoV-2. Bioconjugate chemistry, 32(1), 215–223. https://doi.org/10.1021/acs.bioconjchem.0c00664
dc.relation.referencesLaskowski, R. A., & Swindells, M. B. (2011). LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. Journal of Chemical Information and Modeling, 51(10), 2778–2786. https://doi.org/10.1021/ci200227u
dc.relation.referencesLaxminarayan, R., Duse, A., Wattal, C., Zaidi, A. K., Wertheim, H. F., Sumpradit, N., Vlieghe, E., Hara, G. L., Gould, I. M., Goossens, H., Greko, C., So, A. D., Bigdeli, M., Tomson, G., Woodhouse, W., Ombaka, E., Peralta, A. Q., Qamar, F. N., Mir, F., Kariuki, S., Cars, O. (2013). Antibiotic resistance-the need for global solutions. The Lancet. Infectious Diseases, 13(12), 1057–1098. https://doi.org/10.1016/S1473-3099(13)70318-9
dc.relation.referencesLazzaro, B. P., Zasloff, M., & Rolff, J. (2020). Antimicrobial peptides: Application informed by evolution. Science, 368(6490), eaau5480. https://doi.org/10.1126/science.aau5480
dc.relation.referencesLe, C. F., Yusof, M. Y., Hassan, H., & Sekaran, S. D. (2015). In vitro properties of designed antimicrobial peptides that exhibit potent antipneumococcal activity and produces synergism in combination with penicillin. Scientific reports, 5, 9761. https://doi.org/10.1038/srep09761
dc.relation.referencesLee, Y. J., Shirkey, J. D., Park, J., Bisht, K., & Cowan, A. J. (2022). An Overview of Antiviral Peptides and Rational Biodesign Considerations. Biodesign research, 2022, 9898241. https://doi.org/10.34133/2022/9898241
dc.relation.referencesLei, J., Sun, L., Huang, S., Zhu, C., Li, P., He, J., Mackey, V., Coy, D. H., & He, Q. (2019). The antimicrobial peptides and their potential clinical applications. American Journal of Translational Research, 11(7), 3919–3931.
dc.relation.referencesLesiuk, M., Paduszyńska, M., & Greber, K. E. (2022). Synthetic antimicrobial immunomodulatory peptides: ongoing studies and clinical trials. Antibiotics (Basel, Switzerland), 11(8), 1062. https://doi.org/10.3390/antibiotics11081062
dc.relation.referencesLi, H. L., Chen, Y. N., Cai, J., Liao, T., & Zu, X. Y. (2023). Identification, Screening and Antibacterial Mechanism Analysis of Novel Antimicrobial Peptides from Sturgeon (Acipenser ruthenus) Spermary. Marine drugs, 21(7), 386. https://doi.org/10.3390/md21070386
dc.relation.referencesLi, Q., Kim, G., Jing, L., Ji, X., Elmore, D. E., & Radhakrishnan, M. L. (2023). Electrostatics-based computational design and experimental analysis of Buforin II antimicrobial peptide variants with increased dna affinities. ACS Omega, 8(37), 33701–33711. https://doi.org/10.1021/acsomega
dc.relation.referencesLi, S., Wang, Y., Xue, Z., Jia, Y., Li, R., He, C., Chen, H. (2021). The structure-mechanism relationship and mode of actions of antimicrobial peptides: A review. Trends in Food Science and Technology, 103-115. https://doi.org/10.1016/j.tifs.2021.01.005.
dc.relation.referencesLima, P. G., Oliveira, J. T. A., Amaral, J. L., Freitas, C. D. T., & Souza, P. F. N. (2021). Synthetic antimicrobial peptides: Characteristics, design, and potential as alternative molecules to overcome microbial resistance. Life Sciences, 278, 119647. https://doi.org/10.1016/j.lfs.2021.119647
dc.relation.referencesLiu, H., & Wilson, I. A. (2022). Protective neutralizing epitopes in SARS-CoV-2. Immunological reviews, 310(1), 76–92. https://doi.org/10.1111/imr.13084
dc.relation.referencesLiu, L., Wang, C., Zhang, M., Zhang, Z., Wu, Y., & Zhang, Y. (2022). An efficient evaluation system accelerates α-helical antimicrobial peptide discovery and its application to global human genome mining. Frontiers in Microbiology, 13, 870361. https://doi.org/10.3389/fmicb.2022.870361
dc.relation.referencesLiu, X., Ashforth, E., Ren, B., Song, F., Dai, H., Liu, M., Wang, J., Xie, Q., & Zhang, L. (2010). Bioprospecting microbial natural product libraries from the marine environment for drug discovery. The Journal of Antibiotics, 63(8), 415–422. https://doi.org/10.1038/ja.2010.56
dc.relation.referencesLiu, X., Jiang, L., Li, L., Lu, F., & Liu, F. (2023). Bionics design of affinity peptide inhibitors for SARS-CoV-2 RBD to block SARS-CoV-2 RBD-ACE2 interactions. Heliyon, 9(1), e12890. https://doi.org/10.1016/j.heliyon.2023.e12890
dc.relation.referencesLiu, P., Zeng, X., & Wen, X. (2021). Design and synthesis of new cationic antimicrobial peptides with low cytotoxicity. International Journal of Peptide Research and Therapeutics, 27, 831-840. https://doi.org/10.1007/s10989-020-10133-4
dc.relation.referencesLohner, K., & Leber, R. (2016). Antifungal host defense peptides. In: Epand, R. (eds) Host Defense Peptides and Their Potential as Therapeutic Agents. Springer, Cham. p27-55. https://doi.org/10.1007/978-3-319-32949-9_2.
dc.relation.referencesMahlapuu, M., Håkansson, J., Ringstad, L., & Björn, C. (2016). Antimicrobial peptides: an emerging category of therapeutic agents. Frontiers in Cellular and Infection Microbiology, 6, 194. https://doi.org/10.3389/fcimb.2016.00194
dc.relation.referencesMaleki Dizaj, S., Salatin, S., Khezri, K., Lee, J. Y., & Lotfipour, F. (2022). Targeting Multidrug Resistance With Antimicrobial Peptide-Decorated Nanoparticles and Polymers. Frontiers in microbiology, 13, 831655. https://doi.org/10.3389/fmicb.2022.831655
dc.relation.referencesMannar, D., Saville, J. W., Zhu, X., Srivastava, S. S., Berezuk, A. M., Zhou, S., Tuttle, K. S., Kim, A., Li, W., Dimitrov, D. S., & Subramaniam, S. (2021). Structural analysis of receptor binding domain mutations in SARS-CoV-2 variants of concern that modulate ACE2 and antibody binding. Cell Reports, 37(12), 110156. https://doi.org/10.1016/j.celrep.2021.110156
dc.relation.referencesManzo, G., Scorciapino, M. A., Wadhwani, P., Bürck, J., Montaldo, N. P., Pintus, M., Sanna, R., Casu, M., Giuliani, A., Pirri, G., Luca, V., Ulrich, A. S., & Rinaldi, A. C. (2015). Enhanced amphiphilic profile of a short β-stranded peptide improves its antimicrobial activity. PloS One, 10(1), e0116379. https://doi.org/10.1371/journal.pone.0116379
dc.relation.referencesMarcellini, L., Giammatteo, M., Aimola, P., & Mangoni, M. L. (2010). Fluorescence and electron microscopy methods for exploring antimicrobial peptides mode(s) of action. Methods in Molecular Biology, 618, 249–266. https://doi.org/10.1007/978-1-60761-594-1_16
dc.relation.referencesMarston, H. D., Dixon, D. M., Knisely, J. M., Palmore, T. N., & Fauci, A. S. (2016). Antimicrobial Resistance. Journal of the American Medical Association, 316(11), 1193–1204. https://doi.org/10.1001/jama.2016.11764
dc.relation.referencesMason, A. J., Marquette, A., & Bechinger, B. (2007). Zwitterionic phospholipids and sterols modulate antimicrobial peptide-induced membrane destabilization. Biophysical Journal, 93(12), 4289–4299. https://doi.org/10.1529/biophysj.107.116681
dc.relation.referencesMathur, D., Singh, S., Mehta, A., Agrawal, P., & Raghava, G. P. S. (2018). In silico approaches for predicting the half-life of natural and modified peptides in blood. PloS One, 13(6), e0196829. https://doi.org/10.1371/journal.pone.0196829
dc.relation.referencesMatsuzaki, K. (2019). Antimicrobial Peptides. Singapore: Springer
dc.relation.referencesManyi-Loh, C., Mamphweli, S., Meyer, E., & Okoh, A. (2018). Antibiotic use in agriculture and its consequential resistance in environmental sources: Potential public health implications. Molecules, 23(4), 795. https://doi.org/10.3390/molecules23040795
dc.relation.referencesMehraj, I., Hamid, A., Gani, U., Iralu, N., Manzoor, T., & Saleem Bhat, S. (2024). Combating antimicrobial resistance by employing antimicrobial peptides: immunomodulators and therapeutic agents against infectious diseases. ACS Applied Bio Materials, 7(4), 2023–2035. https://doi.org/10.1021/acsabm.3c01104
dc.relation.referencesMelo, M. C., Maasch, J. R., & De la Fuente, C. (2021). Accelerating antibiotic discovery through artificial intelligence. Communications Biology, 1050. https://doi.org/10.1038/s42003-021-02586-0.
dc.relation.referencesMendelson, M., Lewnard, J. A., Sharland, M., Cook, A., Pouwels, K. B., Alimi, Y., Mpundu, M., Wesangula, E., Weese, J. S., Røttingen, J. A., & Laxminarayan, R. (2024). Ensuring progress on sustainable access to effective antibiotics at the 2024 un general assembly: a target-based approach. Lancet, 403(10443), 2551–2564. https://doi.org/10.1016/S0140-6736(24)01019-5
dc.relation.referencesMenéndez-Arias L. (2008). Mechanisms of resistance to nucleoside analogue inhibitors of HIV-1 reverse transcriptase. Virus Research, 134(1-2), 124–146. https://doi.org/10.1016/j.virusres.2007.12.015
dc.relation.referencesMera-Banguero, C., Orduz, S., Cardona, P., Orrego, A., Muñoz-Pérez, J., & Branch-Bedoya, J. W. (2024). AmpClass: an antimicrobial peptide predictor based on supervised machine learning. Anais da Academia Brasileira de Ciencias, 96(4), e20230756. https://doi.org/10.1590/0001-3765202420230756
dc.relation.referencesMesa, A., Orrego, A., Branch-Bedoya, J.W., Mera-Banguero, C., Orduz, S. (2025) Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells. Current Microbiology, 82(9):379. doi: 10.1007/s00284-025-04346-3. PMID: 40643674; PMCID: PMC12254070.
dc.relation.referencesMillies, B., Von Hammerstein, F., Gellert, A., Hammerschmidt, S., Barthels, F., Göppel, U., Immerheiser, M., Elgner, F., Jung, N., Basic, M., Kersten, C., Kiefer, W., Bodem, J., Hildt, E., Windbergs, M., Hellmich, U. A., & Schirmeister, T. (2019). Proline-based allosteric inhibitors of Zika and Dengue virus NS2B/NS3 proteases. Journal of Medicinal Chemistry, 62(24), 11359–11382. https://doi.org/10.1021/acs.jmedchem.9b01697
dc.relation.referencesMirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: making protein folding accessible to all. Nature Methods, 19(6), 679–682. https://doi.org/10.1038/s41592-022-01488-1
dc.relation.referencesMonsalve, D., Mesa, A., Mira, L. M., Mera, C., Orduz, S., & Branch-Bedoya, J. W. (2024). Antimicrobial peptides designed by computational analysis of proteomes. Antonie Van Leeuwenhoek, 117(1), 55. https://doi.org/10.1007/s10482-024-01946-0
dc.relation.referencesMooney, C., Haslam, N. J., Holton, T. A., Pollastri, G., & Shields, D. C. (2013). PeptideLocator: prediction of bioactive peptides in protein sequences. Bioinformatics (Oxford, England), 29(9), 1120–1126. https://doi.org/10.1093/bioinformatics/btt103
dc.relation.referencesMoravej, H., Moravej, Z., Yazdanparast, M., Heiat, M., Mirhosseini, A., Moosazadeh Moghaddam, M., & Mirnejad, R. (2018). Antimicrobial peptides: Features, action, and their resistance mechanisms in bacteria. Microbial Drug Resistance (Larchmont, N.Y.), 24(6), 747–767.
dc.relation.referencesMorena, F., Cencini, C., Calzoni, E., Martino, S., & Emiliani, C. (2024). A novel workflow for In Silico prediction of bioactive peptides: An exploration of Solanum lycopersicum by-products. Biomolecules, 14(8), 930. https://doi.org/10.3390/biom14080930
dc.relation.referencesMorens, D. M., Daszak, P., Markel, H., & Taubenberger, J. K. (2020). Pandemic COVID-19 joins history's pandemic legion. mBio, 11(3), e00812-20. https://doi.org/10.1128/mBio.00812-20
dc.relation.referencesMorris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785-2791. https://doi.org/10.1002/jcc.21256
dc.relation.referencesMorris, G. M., & Lim-Wilby, M. (2008). Molecular docking. Methods in Molecular Biology, 443, 365–382. https://doi.org/10.1007/978-1-59745-177-2_19.
dc.relation.referencesMuller, P. Y., & Milton, M. N. (2012). The determination and interpretation of the therapeutic index in drug development. Nature Reviews Drug Discovery, 11(10), 751-761. https://doi.org/10.1038/nrd3801
dc.relation.referencesMuñoz, A., & Read, N. D. (2012). Live-cell imaging and analysis shed light on the complexity and dynamics of antimicrobial peptide action. Frontiers in Immunology, 3, 248. https://doi.org/10.3389/fimmu.2012.00248
dc.relation.referencesMurray, C. J. L., Ikuta, K. S., Sharara, F., Swetschinski, L., Aguilar, G. R., Gray, A., ... & Hay, S. I. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet, 399(10325), 629-655. https://doi.org/10.1016/S0140-6736(21)02724-0
dc.relation.referencesNagarajan, D., Nagarajan, T., Roy, N., Kulkarni, O., Ravichandran, S., Mishra, M., Chakravortty, D., & Chandra, N. (2018). Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria. The Journal of Biological Chemistry, 293(10), 3492–3509. https://doi.org/10.1074/jbc.M117.805499
dc.relation.referencesNguyen, L. T., Haney, E. F., & Vogel, H. J. (2011). The expanding scope of antimicrobial peptide structures and their modes of action. Trends in Biotechnology, 29(9), 464–472. https://doi.org/10.1016/j.tibtech.2011.05.001
dc.relation.referencesNuti, R., Goud, N. S., Saraswati, A. P., Alvala, R., Alvala, M. (2017). Antimicrobial peptides: A promising therapeutic strategy in tackling antimicrobial resistance. Current Medicinal Chemistry, 24(38),4303–4314. doi:10.2174/0929867324666170815102441
dc.relation.referencesOmardien, S., Drijfhout, J. W., Vaz, F. M., Wenzel, M., Hamoen, L. W., Zaat, S. A. J., & Brul, S. (2018). Bactericidal activity of amphipathic cationic antimicrobial peptides involves altering the membrane fluidity when interacting with the phospholipid bilayer. Biochimica et Biophysica Acta. Biomembranes, 1860(11), 2404–2415. https://doi.org/10.1016/j.bbamem.2018.06.004
dc.relation.referencesOmer, A. A. M., Kumar, S., Selegård, R., Bengtsson, T., & Khalaf, H. (2025). Characterization of Novel Plantaricin-Derived Antiviral Peptides Against Flaviviruses. International journal of molecular sciences, 26(3), 1038. https://doi.org/10.3390/ijms26031038
dc.relation.referencesO’Neill, J. (2016). Tackling drug-resistant infections globally: Final report and recommendations. Review on antimicrobial resistance. London, UK: Wellcome Trust & HM Government. https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf
dc.relation.referencesOrrego, A. (2020). Sistema de inteligencia artificial para la predicción automática de péptidos bioactivos. (Tesis de pregrado, Universidad Nacional de Colombia, Sede Medellín).
dc.relation.referencesOrrego, A., Vélez, A., Cardona, P., Muñoz, J., Ceballo, A., Duque, G., Mera, C. A., Orduz, S., & Branch, J. W. (2019). PepMultiTools. https://ciencias.medellin.unal.edu.co/gruposdeinvestigacion/prospeccionydisenobiomoleculas
dc.relation.referencesOyardi, O., Savage, P. B., & Guzel, C. B. (2022). Effects of Ceragenins and Antimicrobial Peptides on the A549 Cell Line and an In Vitro Co-Culture Model of A549 Cells and Pseudomonas aeruginosa. Pathogens (Basel, Switzerland), 11(9), 1044. https://doi.org/10.3390/pathogens11091044
dc.relation.referencesPalakurthi, S. S., Shah, B., Kapre, S., Charbe, N., Immanuel, S., Pasham, S., Thalla, M., Jain, A., & Palakurthi, S. (2024). A comprehensive review of challenges and advances in exosome-based drug delivery systems. Nanoscale advances, 6(23), 5803–5826. https://doi.org/10.1039/d4na00501e
dc.relation.referencesPandit, G., Chowdhury, N., Abdul Mohid, S., Bidkar, A. P., Bhunia, A., & Chatterjee, S. (2021). Effect of secondary structure and side chain length of hydrophobic amino acid residues on the antimicrobial activity and toxicity of 14-residue-long de novo AMPs. ChemMedChem, 16(2), 355–367. https://doi.org/10.1002/cmdc.202000550
dc.relation.referencesPergande, M. R., & Cologna, S. M. (2017). Isoelectric point separations of peptides and proteins. Proteomes, 5(1), 4. https://doi.org/10.3390/proteomes5010004
dc.relation.referencesPhuong, H. B. T., Ngan, H. D., Huy, B. L., Dinh, H. V., & Xuan, H. L. (2024). The amphipathic design in helical antimicrobial peptides. ChemMedChem, 19(7), e202300480. https://doi.org/10.1002/cmdc.202300480
dc.relation.referencesPitart, C., Solé, M., Roca, I., Fàbrega, A., Vila, J., & Marco, F. (2011). First outbreak of a plasmid-mediated carbapenem-hydrolyzing OXA-48 beta-lactamase in Klebsiella pneumoniae in Spain. Antimicrobial Agents and Chemotherapy, 55(9), 4398–4401. https://doi.org/10.1128/AAC.00329-11
dc.relation.referencesPirtskhalava, M., Armstrong, A. A., Grigolava, M., Chubinidze, M., Alimbarashvili, E., Vishnepolsky, B., Gabrielian, A., Rosenthal, A., Hurt, D. E., & Tartakovsky, M. (2021). DBAASP v3: Database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Research, 49(D1), D288–D297. https://doi.org/10.1093/nar/gkaa991
dc.relation.referencesPowers, J. P., & Hancock, R. E. (2003). The relationship between peptide structure and antibacterial activity. Peptides, 24(11), 1681–1691. https://doi.org/10.1016/j.peptides.2003.08.023
dc.relation.referencesPreußke, N., Lipfert, M., Rothemund, S., Leippe, M., & Sönnichsen, F. D. (2021). Designed Trp-cage proteins with antimicrobial activity and enhanced stability. Biochemistry, 60(42), 3187–3199. https://doi.org/10.1021/acs.biochem.1c00567
dc.relation.referencesPreventing the next pandemic. (2021). Bulletin of the World Health Organization, 99(5), 326–327. https://doi.org/10.2471/BLT.21.020521
dc.relation.referencesQuigua-Orozco, R. M., Andrade, I. E. P., Oshiro, K. G. N., Rezende, S. B., Santos, A. D. O., Pereira, J. A. L., da Silva, V. G., Buccini, D. F., Porto, W. F., Macedo, M. L. R., Cardoso, M. H., & Franco, O. L. (2024). In silico optimization of analogs derived pro-adrenomedullin peptide to evaluate antimicrobial potential. Chemical Biology & Drug Design, 104(1), e14588. https://doi.org/10.1111/cbdd.14588
dc.relation.referencesRahmati, S., Zandi, F., Ahmadi, K., Adeli, A., Rastegarpanah, N., Amanlou, M., & Vaziri, B. (2024). Computational structure-based design of antiviral peptides as potential protein-protein interaction inhibitors of rabies virus phosphoprotein and human LC8. Heliyon, 11(1), e41520. https://doi.org/10.1016/j.heliyon.2024.e41520
dc.relation.referencesRakhmetullina, A., Zielenkiewicz, P., & Odolczyk, N. (2024). Peptide-Based Inhibitors of Protein-Protein Interactions (PPIs): A Case Study on the Interaction Between SARS-CoV-2 Spike Protein and Human Angiotensin-Converting Enzyme 2 (hACE2). Biomedicines, 12(10), 2361. https://doi.org/10.3390/biomedicines12102361
dc.relation.referencesRathore, A. S., Choudhury, S., Arora, A., Tijare, P., & Raghava, G. P. S. (2024). ToxinPred 3.0: An improved method for predicting the toxicity of peptides. Computers in Biology and Medicine, 179, 108926. https://doi.org/10.1016/j.compbiomed.2024.108926
dc.relation.referencesRice, L. B. (2008). Federal funding for the study of antimicrobial resistance in nosocomial pathogens: No ESKAPE. The Journal of Infectious Diseases, 197(8), 1079-1081. https://doi.org/10.1086/533452
dc.relation.referencesRispoli, G. (2017). Studying the mechanism of membrane permeabilization induced by antimicrobial peptides using patch-clamp techniques. Methods in Molecular Biology, 1548, 255–269. https://doi.org/10.1007/978-1-4939-6737-7_18
dc.relation.referencesSader, H. S., Castanheira, M., Mendes, R. E., Toleman, M., Walsh, T. R., & Jones, R. N. (2005). Dissemination and diversity of metallo-beta-lactamases in Latin America: report from the SENTRY antimicrobial surveillance program. International Journal of Antimicrobial Agents, 25(1), 57–61. https://doi.org/10.1016/j.ijantimicag.2004.08.013
dc.relation.referencesSahsuvar, S., Kocagoz, T., Gok, O., & Can, O. (2023). In vitro efficacy of different PEGylation designs on cathelicidin-like peptide with high antibacterial and antifungal activity. Scientific reports, 13(1), 11213. https://doi.org/10.1038/s41598-023-38449-3
dc.relation.referencesSalazar, J., Samhan-Arias, A. K., & Gutierrez-Merino, C. (2023). Hexa-Histidine, a peptide with versatile applications in the study of amyloid-β(1-42) molecular mechanisms of action. Molecules, 28(20), 7138. https://doi.org/10.3390/molecules28207138
dc.relation.referencesSalmaso, V., & Moro, S. (2018). Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: an overview. Frontiers in Pharmacology, 9, 923. https://doi.org/10.3389/fphar.2018.00923
dc.relation.referencesSánchez-Ortega, I., García-Almendárez, B. E., Santos-López, E. M., Amaro-Reyes, A., Barboza-Corona, J. E., & Regalado, C. (2014). Antimicrobial edible films and coatings for meat and meat products preservation. The Scientific World Journal, 2014, 248935. https://doi.org/10.1155/2014/248935
dc.relation.referencesSánchez, Y. A., Castillo, J. A., Ramírez, K. J., Orduz, S., & Camargo, D. O. (2020). Peptide derivatives of Dermaseptin S4 in fresh bovine semen for bacterial contamination control: Physicochemical and structural characterization, antibacterial potency, and effects on red blood and sperm cells. Reproduction in Domestic Animals, 55(8), 905–914. https://doi.org/10.1111/rda.13701
dc.relation.referencesSantos, C., Rodrigues, G. R., Lima, L. F., dos Reis, M. C. G., Cunha, N. B., Dias, S. C., & Franco, O. L. (2022). Advances and perspectives for antimicrobial peptide and combinatory therapies. Frontiers in Bioengineering and Biotechnology, 10, 1051456. https://doi.org/10.3389/fbioe.2022.1051456
dc.relation.referencesSantos-Júnior, C. D., Torres, M. D. T., Duan, Y., Rodríguez Del Río, Á., Schmidt, T. S. B., Chong, H., Fullam, A., Kuhn, M., Zhu, C., Houseman, A., Somborski, J., Vines, A., Zhao, X. M., Bork, P., Huerta-Cepas, J., de la Fuente-Nunez, C., & Coelho, L. P. (2024). Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell, 187(14), 3761–3778.e16. https://doi.org/10.1016/j.cell.2024.05.013
dc.relation.referencesSantajit, S., & Indrawattana, N. (2016). Mechanisms of antimicrobial resistance in ESKAPE pathogens. BioMed Research International, 2016, 2475067. https://doi.org/10.1155/2016/2475067
dc.relation.referencesSamson, M., Pizzorno, A., Abed, Y., & Boivin, G. (2013). Influenza virus resistance to neuraminidase inhibitors. Antiviral Research, 98(2), 174–185. https://doi.org/10.1016/j.antiviral.2013.03.014
dc.relation.referencesSamy, M. N., Mahmoud, B. K., Shady, N. H., Abdelmohsen, U. R., & Ross, S. A. (2022). Bioassay-guided fractionation with antimalarial and antimicrobial activities of paeonia officinalis. Molecules, 27(23), 8382. https://doi.org/10.3390/molecules27238382
dc.relation.referencesSapay, N., Guermeur, Y., & Deléage, G. (2006). Prediction of amphipathic in-plane membrane anchors in monotopic proteins using a SVM classifier. BMC Bioinformatics, 7, 255. https://doi.org/10.1186/1471-2105-7-255
dc.relation.referencesSarkar, T., Ghosh, S., Sundaravadivelu, P. K., Pandit, G., Debnath, S., Thummer, R. P., Satpati, P., & Chatterjee, S. (2024). Mechanism of protease resistance of D-Amino acid residue containing cationic antimicrobial heptapeptides. ACS Infectious Diseases, 10(2), 562–581. https://doi.org/10.1021/acsinfecdis.3c00491
dc.relation.referencesSarmah, A. K., Meyer, M. T., & Boxall, A. B. (2006). A global perspective on the use, sales, exposure pathways, occurrence, fate, and effects of veterinary antibiotics (VAs) in the environment. Chemosphere, 65(5), 725–759. https://doi.org/10.1016/j.chemosphere.2006.03.026
dc.relation.referencesSchaduangrat, N., Nantasenamat, C., Prachayasittikul, V., & Shoombuatong, W. (2019). Meta-iAVP: A sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation. International Journal of Molecular Sciences, 20(22), 5743. https://doi.org/10.3390/ijms20225743
dc.relation.referencesSchrödinger, LLC. (2015). The PyMOL Molecular Graphics System (Version 1.8). Schrödinger, LLC.
dc.relation.referencesSchwarze, M., Brakel, A., Hoffmann, R., & Krizsan, A. (2025). Peptides Corresponding to the Receptor-Binding Domain (RBD) of Several SARS-CoV-2 Variants Of Concern Prevent Recognition of the Human ACE2 Receptor and Consecutive Cell Infections. ChemMedChem, 20(7), e202400973. https://doi.org/10.1002/cmdc.202400973
dc.relation.referencesSharma, A., Singla, D., Rashid, M., & Raghava, G. P. (2014). Designing of peptides with desired half-life in intestine-like environment. BMC Bioinformatics, 15(1), 282. https://doi.org/10.1186/1471-2105-15-282
dc.relation.referencesSingh, N., & Li, W. (2020). Absolute binding free energy calculations for highly flexible protein mdm2 and its inhibitors. International Journal of Molecular Sciences, 21(13), 4765. https://doi.org/10.3390/ijms21134765
dc.relation.referencesSmanski, M. J., Zhou, H., Claesen, J., Shen, B., Fischbach, M. A., & Voigt, C. A. (2016). Synthetic biology to access and expand nature's chemical diversity. Nature Reviews Microbiology, 14(3), 135–149. https://doi.org/10.1038/nrmicro.2015.24
dc.relation.referencesSmith, J. R., Roberts, K. D., & Rybak, M. J. (2015). Dalbavancin: A novel lipoglycopeptide antibiotic with extended activity against gram-positive infections. Infectious Diseases and Therapy, 4(3), 245–258. https://doi.org/10.1007/s40121-015-0077-7
dc.relation.referencesSomma, S., Gastaldo, L., & Corti, A. (1984). Teicoplanin, a new antibiotic from Actinoplanes teichomyceticus nov. sp. Antimicrobial Agents and chemotherapy, 26(6), 917–923. https://doi.org/10.1128/AAC.26.6.917
dc.relation.referencesSosiangdi, S., Taemaitree, L., Tankrathok, A., Daduang, S., Boonlue, S., Klaynongsruang, S., & Jangpromma, N. (2023). Rational design and characterization of cell-selective antimicrobial peptides based on a bioactive peptide from Crocodylus siamensis hemoglobin. Scientific reports, 13(1), 16096. https://doi.org/10.1038/s41598-023-43274-9
dc.relation.referencesStarvaggi, J., Previti, S., Zappalà, M., & Ettari, R. (2024). The inhibition of NS2B/NS3 protease: a new therapeutic opportunity to treat Dengue and Zika virus infection. International Journal of Molecular Sciences, 25(8), 4376. https://doi.org/10.3390/ijms25084376
dc.relation.referencesSteiner, H., Hultmark, D., Engström, A., Bennich, H., & Boman, H. G. (1981). Sequence and specificity of two antibacterial proteins involved in insect immunity. Nature, 292(5820), 246–248. https://doi.org/10.1038/292246a0
dc.relation.referencesStrandberg, E., Tiltak, D., Ieronimo, M., Kanithasen, N., Wadhwani, P., Ulrich, A.S. (2007) Influence of C-terminal amidation on the antimicrobial and hemolytic activities of cationic α-helical peptides. Pure and Applied Chemistry. 79(4): 717-728. https://doi.org/10.1351/pac200779040717
dc.relation.referencesTai, W., He, L., Zhang, X., Pu, J., Voronin, D., Jiang, S., Zhou, Y., & Du, L. (2020). Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus: implication for development of RBD protein as a viral attachment inhibitor and vaccine. Cellular & Molecular Immunology, 17(6), 613–620. https://doi.org/10.1038/s41423-020-0400-4
dc.relation.referencesTamayo, S., Castañeda, C.A., & Orduz, S. (2018). Type-Peptide 1.0. Herramientas computacionales pedagógicas para el estudio, diseño y optimización de péptidos antimicrobianos basada en sus propiedades fisicoquímicas. Dirección Nacional de Derechos de Autor. Ministerio del Interior. Registro 13-70-274. Diciembre 5, 2018
dc.relation.referencesTang, Z., Ma, Q., Chen, X., Chen, T., Ying, Y., Xi, X., Wang, L., Ma, C., Shaw, C., & Zhou, M. (2021). Recent Advances and Challenges in Nanodelivery Systems for Antimicrobial Peptides (AMPs). Antibiotics (Basel, Switzerland), 10(8), 990. https://doi.org/10.3390/antibiotics10080990
dc.relation.referencesTaniguchi, M., Aida, R., Saito, K., Oya, R., Ochiai, A., Saitoh, E., & Tanaka, T. (2020). Identification of cationic peptides derived from low protein rice by-products and evaluation of their multifunctional activities. Journal of Bioscience and Bioengineering, 129(3), 307–314. https://doi.org/10.1016/j.jbiosc.2019.09.009
dc.relation.referencesTavares, L. S., Silva, C. S., de Souza, V. C., da Silva, V. L., Diniz, C. G., & Santos, M. O. (2013). Strategies and molecular tools to fight antimicrobial resistance: Resistome, transcriptome, and antimicrobial peptides. Frontiers in Microbiology, 4, 412. https://doi.org/10.3389/fmicb.2013.00412
dc.relation.referencesThakkar, R., Agarwal, D. K., Ranaweera, C. B., Ishiguro, S., Conda-Sheridan, M., Gaudreault, N. N., Richt, J. A., Tamura, M., & Comer, J. (2023). De novo design of a stapled peptide targeting SARS-CoV-2 spike protein receptor-binding domain. RSC Medicinal Chemistry, 14(9), 1722–1733. https://doi.org/10.1039/d3md00222e
dc.relation.referencesThakur, N., Qureshi, A., & Kumar, M. (2012). AVPpred: Collection and prediction of highly effective antiviral peptides. Nucleic Acids Research. 40, W199–W204. https://doi.org/10.1093/nar/gks450
dc.relation.referencesThe UniProt Consortium. (2023). UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Research, 51(D1), D523–D531. https://doi.org/10.1093/nar/gkad1090
dc.relation.referencesTimmons, P. B., & Hewage, C. M. (2020). HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Scientific Reports, 10(1), 10869. https://doi.org/10.1038/s41598-020-67701-3
dc.relation.referencesTimmons, P. B., & Hewage, C. M. (2021). ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Briefings in Bioinformatics, 22(6), bbab258. https://doi.org/10.1093/bib/bbab258
dc.relation.referencesTing, D., Beuerman, R. W., Dua, H. S., Lakshminarayanan, R., Mohammed, I. (2020). Strategies in translating the therapeutic potentials of host defense peptides. Frontiers in Immunology, 11, 983. https://doi.org/10.3389/fimmu.2020.00983.
dc.relation.referencesTorrent, M., Valle, J., Nogués, M. V., Boix, E., & Andreu, D. (2011). The generation of antimicrobial peptide activity: A trade-off between charge and aggregation?. Angewandte Chemie , 50(45), 10686–10689. https://doi.org/10.1002/anie.201103589
dc.relation.referencesTorres, M. D. T., Brooks, E. F., Cesaro, A., Sberro, H., Gill, M. O., Nicolaou, C., Bhatt, A. S., & de la Fuente-Nunez, C. (2024). Mining human microbiomes reveals an untapped source of peptide antibiotics. Cell, 187(19), 5453–5467.e15. https://doi.org/10.1016/j.cell.2024.07.027
dc.relation.referencesTorres, M., Cao, J., Franco, O. L., Lu, T. K., & de la Fuente-Nunez, C. (2021). Synthetic biology and computer-based frameworks for antimicrobial peptide discovery. ACS Nano, 15(2), 2143–2164. https://doi.org/10.1021/acsnano.0c09509
dc.relation.referencesTorres, M. D. T., Melo, M. C. R., Flowers, L., Crescenzi, O., Notomista, E., & de la Fuente-Nunez, C. (2022). Mining for encrypted peptide antibiotics in the human proteome. Nature biomedical engineering, 6(1), 67–75. https://doi.org/10.1038/s41551-021-00801-1
dc.relation.referencesTorres, M. D. T., Wan, F., & de la Fuente-Nunez, C. (2025). Deep learning reveals antibiotics in the archaeal proteome. Nature microbiology, 10(9), 2153–2167. https://doi.org/10.1038/s41564-025-02061-0
dc.relation.referencesTunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., Velankar, S., Kleywegt, G. J., Bateman, A., Evans, R., Pritzel, A., Figurnov, M., Ronneberger, O., Bates, R., Kohl, S. A. A., Potapenko, A., … Hassabis, D. (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596(7873), 590–596. https://doi.org/10.1038/s41586-021-03828-1
dc.relation.referencesVanzolini, T., Bruschi, M., Rinaldi, A. C., Magnani, M., & Fraternale, A. (2022). Multitalented synthetic antimicrobial peptides and their antibacterial, antifungal, and antiviral mechanisms. International Journal of Molecular Sciences, 23(1), 545. https://doi.org/10.3390/ijms23010545
dc.relation.referencesVan Boeckel, T. P., Glennon, E. E., Chen, D., Gilbert, M., Robinson, T. P., Grenfell, B. T., Levin, S. A., Bonhoeffer, S., & Laxminarayan, R. (2017). Reducing antimicrobial use in food animals. Science, 357(6358), 1350–1352. https://doi.org/10.1126/science.aao1495
dc.relation.referencesVan den Meersche, T., Rasschaert, G., Haesebrouck, F., Van Coillie, E., Herman, L., Van Weyenberg, S., Daeseleire, E., & Heyndrickx, M. (2019). Presence and fate of antibiotic residues, antibiotic resistance genes and zoonotic bacteria during biological swine manure treatment. Ecotoxicology and Environmental Safety, 175, 29–38. https://doi.org/10.1016/j.ecoenv.2019.01.127
dc.relation.referencesVélez, A., Mera, C., Orduz, S., Branch, J. W. (2021). Synthetic antimicrobial peptides generation using recurrent neural networks, DYNA, 88(216), 210-219. https://revistas.unal.edu.co/index.php/dyna/article/download/88799/78606/519512
dc.relation.referencesVelmurugan, D., Mythily, U., & Rao, K. (2014). Design and docking studies of peptide inhibitors as potential antiviral drugs for Dengue virus NS2B/NS3 protease. Protein and Peptide Letters, 21(8), 815–827. https://doi.org/10.2174/09298665113209990062
dc.relation.referencesVeltri, D., Kamath, U., & Shehu, A. (2018). Deep learning improves antimicrobial peptide recognition. Bioinformatics, 34(16), 2740–2747. https://doi.org/10.1093/bioinformatics/bty179
dc.relation.referencesVilas Boas, L. C. P., Campos, M. L., Berlanda, R. L. A., de Carvalho Neves, N., & Franco, O. L. (2019). Antiviral peptides as promising therapeutic drugs. Cellular and Molecular Life Sciences, 76(18), 3525–3542. https://doi.org/10.1007/s00018-019-03138-w
dc.relation.referencesVillalobos, A. P., Barrero, L. I., Rivera, S. M., Ovalle, M. V., & Valera, D. (2014). Vigilancia de infecciones asociadas a la atención en salud, resistencia bacteriana y consumo de antibióticos en hospitales de alta complejidad, Colombia, 2011. Biomédica, 34(Sup1), 67-80. https://doi.org/10.7705/biomedica.v34i0.1698
dc.relation.referencesVishnepolsky, B., Grigolava, M., Managadze, G., Gabrielian, A., Rosenthal, A., Hurt, D. E., Tartakovsky, M., & Pirtskhalava, M. (2022). Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction. Briefings in Bioinformatics, 23(4), bbac233. https://doi.org/10.1093/bib/bbac233
dc.relation.referencesWalsh, C. T., & Wencewicz, T. A. (2014). Prospects for new antibiotics: a molecule-centered perspective. The Journal of Antibiotics, 67(1), 7–22. https://doi.org/10.1038/ja.2013.49
dc.relation.referencesWaghu, F. H., Barai, R. S., Gurung, P., & Idicula-Thomas, S. (2016). CAMPR3: a database on sequences, structures, and signatures of antimicrobial peptides. Nucleic Acids Research, 44(D1), D1094–D1097. https://doi.org/10.1093/nar/gkv1051
dc.relation.referencesWang, G. (2017). Antimicrobial peptides: Discovery, design, and novel therapeutic strategies. Omaha: CABI.
dc.relation.referencesWang, G. (2020). The antimicrobial peptide database provides a platform for decoding the design principles of naturally occurring antimicrobial peptides. Protein Science, 29(1), 8–18. https://doi.org/10.1002/pro.3702
dc.relation.referencesWang, G., Li, X., & Wang, Z. (2016). APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Research, 44(D1), D1087–D1093. https://doi.org/10.1093/nar/gkv1278
dc.relation.referencesWang, G., Vaisman, I. I., & van Hoek, M. L. (2022). Machine Learning Prediction of Antimicrobial Peptides. Methods in molecular biology (Clifton, N.J.), 2405, 1–37. https://doi.org/10.1007/978-1-0716-1855-4_1
dc.relation.referencesWang, G., Watson, K. M., & Buckheit, R. W., Jr (2008). Anti-human immunodeficiency virus type 1 activities of antimicrobial peptides derived from human and bovine cathelicidins. Antimicrobial agents and chemotherapy, 52(9), 3438–3440. https://doi.org/10.1128/AAC.00452-08
dc.relation.referencesWang, T., Zou, C., Wen, N., Liu, X., Meng, Z., Feng, S., Zheng, Z., Meng, Q., & Wang, C. (2021). The effect of structural modification of antimicrobial peptides on their antimicrobial activity, hemolytic activity, and plasma stability. Journal of Peptide Science, 27(5), e3306. https://doi.org/10.1002/psc.3306
dc.relation.referencesWang, Y., Song, M., & Chang, W. (2024). Antimicrobial peptides and proteins against drug-resistant pathogens. Cell surface (Amsterdam, Netherlands), 12, 100135. https://doi.org/10.1016/j.tcsw.2024.100135
dc.relation.referencesWeng, G., Wang, E., Wang, Z., Liu, H., Zhu, F., Li, D., & Hou, T. (2019). HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Research, 47(W1), W322–W330. https://doi.org/10.1093/nar/gkz397
dc.relation.referencesWensing, A. M., Calvez, V., Ceccherini-Silberstein, F., Charpentier, C., Günthard, H. F., Paredes, R., Shafer, R. W., & Richman, D. D. (2019). 2019 update of the drug resistance mutations in HIV-1. Topics in Antiviral Medicine, 27(3), 111–121.
dc.relation.referencesWHO. (2021 de noviembre de 2021). World Health Organization. Obtenido de antimicrobial resistance: https://www.who.int/news-room/fact-sheets/detail/antimicrobial
dc.relation.referencesWHO. (2021). Global Antimicrobial resistance and use surveillance system (GLASS) report. World Health Organization. https://www.who.int/publications/i/item/9789240027336
dc.relation.referencesWiegand, I., Hilpert, K., Hancock, R. (2008). Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nature Protocols, 3: 163-175. https://doi.org/10.1038/nprot.2007.521
dc.relation.referencesWimley, W. C., & Hristova, K. (2011). Antimicrobial peptides: successes, challenges, and unanswered questions. The Journal of Membrane Biology, 239(1-2), 27–34. https://doi.org/10.1007/s00232-011-9343-0
dc.relation.referencesXia, X., Torres, M. D. T., & de la Fuente-Nunez, C. (2025). Proteasome-derived antimicrobial peptides discovered via deep learning. bioRxiv: the preprint server for biology, 2025.03.17.643752. https://doi.org/10.1101/2025.03.17.643752
dc.relation.referencesXu, G., Jin, J., Fu, Z., Wang, G., Lei, X., Xu, J., & Wang, J. (2025). Extracellular vesicle-based drug overview: research landscape, quality control and nonclinical evaluation strategies. Signal transduction and targeted therapy, 10(1), 255. https://doi.org/10.1038/s41392-025-02312-w
dc.relation.referencesXu, X., Yan, C., & Zou, X. (2018). MDockPeP: An ab-initio protein-peptide docking server. Journal of Computational Chemistry, 39(28), 2409–2413. https://doi.org/10.1002/jcc.25555
dc.relation.referencesYang, F., & Ma, Y. (2024). The application and prospects of antimicrobial peptides in antiviral therapy. Amino Acids, 56(1), 68. https://doi.org/10.1007/s00726-024-03427-0
dc.relation.referencesYang, J., & Zhang, Y. (2015). Protein structure and function prediction using I-TASSER. Current Protocols in Bioinformatics, 52, 5.8.1–5.8.15. https://doi.org/10.1002/0471250953.bi0508s52
dc.relation.referencesYang, Y., Zhang, J., Wu, S., Deng, Y., Wang, S., Xie, L., Li, X., & Yang, L. (2024). Exosome/antimicrobial peptide laden hydrogel wound dressings promote scarless wound healing through miR-21-5p-mediated multiple functions. Biomaterials, 308, 122558. https://doi.org/10.1016/j.biomaterials.2024.122558
dc.relation.referencesYao, K., Liu, J., Sun, R., Wang, Y., Jiang, Y., Wang, T., Chen, X., Ma, C., Chen, T., Shaw, C., Zhou, M., & Wang, L. (2025). Enhancing the selectivity and conditional sensitivity of an antimicrobial peptide through cleavage simulations and homoarginine incorporation to combat drug-resistant bacteria. Scientific reports, 15(1), 21798. https://doi.org/10.1038/s41598-025-06522-8
dc.relation.referencesYou, Y., Liu, H., Zhu, Y., & Zheng, H. (2023). Rational design of stapled antimicrobial peptides. Amino acids, 55(4), 421–442. https://doi.org/10.1007/s00726-023-03245-w
dc.relation.referencesZabidi, N. Z., Liew, H. L., Farouk, I. A., Puniyamurti, A., Yip, A. J. W., Wijesinghe, V. N., Low, Z. Y., Tang, J. W., Chow, V. T. K., & Lal, S. K. (2023). Evolution of SARS-CoV-2 variants: implications on immune escape, vaccination, therapeutic and diagnostic strategies. Viruses, 15(4), 944. https://doi.org/10.3390/v15040944
dc.relation.referencesZasloff, M. (1987). Magainins, a class of antimicrobial peptides from Xenopus skin: isolation, characterization of two active forms, and partial cDNA sequence of a precursor. Proceedings of the National Academy of Sciences of the United States of America, 84(15), 5449–5453. https://doi.org/10.1073/pnas.84.15.5449
dc.relation.referencesZasloff, M. (2002). Antimicrobial peptides of multicellular organisms. Nature, 415(6870), 389–395. https://doi.org/10.1038/415389a
dc.relation.referencesZelezetsky, I., & Tossi, A. (2006). Alpha-helical antimicrobial peptides--using a sequence template to guide structure-activity relationship studies. Biochimica et Biophysica Acta, 1758(9), 1436–1449. https://doi.org/10.1016/j.bbamem.2006.03.021
dc.relation.referencesZhang, B., Shi, W., Li, J., Liao, C., Li, M., Huang, W., Qian, H. (2017). Synthesis and biological evaluation of novel peptides as potential agents with antitumor and multidrug resistance- reversing activities. Chemical Biology & Drug Design. 90(5): 972-980. https://doi.org/10.1111/cbdd.13023
dc.relation.referencesZhang, Q. Y., Yan, Z. B., Meng, Y. M., Hong, X. Y., Shao, G., Ma, J. J., Cheng, X. R., Liu, J., Kang, J., & Fu, C. Y. (2021). Antimicrobial peptides: mechanism of action, activity, and clinical potential. Military Medical Research, 8(1), 48. https://doi.org/10.1186/s40779-021-00343-2
dc.relation.referencesZhou, P., Jin, B., Li, H., & Huang, S. Y. (2018). HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Research, 46(W1), W443–W450. https://doi.org/10.1093/nar/gky357
dc.relation.referencesZhou, P., & Huang, J. (2015). Computational Peptidology. Hatfield: Humana Press.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc500 - Ciencias naturales y matemáticas
dc.subject.ddc660 - Ingeniería química
dc.subject.lembDiversidad biológica
dc.subject.lembAntibioticos
dc.subject.lembPeptidos
dc.subject.lembPeptídeos antimicrobianos
dc.subject.proposalPéptidosspa
dc.subject.proposalAntimicrobianosspa
dc.subject.proposalAntiviralesspa
dc.subject.proposalAcoplamiento molecularspa
dc.subject.proposalBiodiversityeng
dc.subject.proposalPeptideseng
dc.subject.proposalAntiviralseng
dc.subject.proposalAntimicrobialseng
dc.subject.proposalMolecular dockingeng
dc.titleDiseño racional y validación in silico e in vitro de péptidos antimicrobianos sintéticos inspirados en proteomas de especies de la biodiversidad colombianaspa
dc.title.translatedRational design and in silico and in vitro validation of synthetic antimicrobial peptides inspired by the proteomes of species from Colombian biodiversityeng
dc.typeTrabajo de grado - Maestría
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dcterms.audience.professionaldevelopmentEstudiantes
dcterms.audience.professionaldevelopmentInvestigadores
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
1152222002.2025.pdf
Tamaño:
3.35 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ciencias - Biotecnología
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Maestría en Ciencias - Biotecnología