Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis

Mycobacterium tuberculosis (Mtb), el agente causal de la tuberculosis (TB), se clasifica predominantemente como un patógeno del sistema respiratorio, aunque tiene la capacidad de afectar otros órganos y tejidos del cuerpo. Esta enfermedad representa un desafío significativo en países con recursos ec...

Full description

Autores:
Pestana Nobles, Roberto Carlos
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
spa
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/14584
Acceso en línea:
https://hdl.handle.net/20.500.12442/14584
Palabra clave:
Mycobacterium tuberculosis
Dinámica molecular
Docking molecular
CtpF
Inhibidores
Tuberculosis
Dinámica molecular gaussiana acelerada
ATPasa tipo P.
Mycobacterium tuberculosis
Molecular dyamics
Molecular docking
CtpF
Inhibitors
Tuberculosis
Gaussian accelerated molecular dynamics
ATPase type P.
Rights
restrictedAccess
License
http://purl.org/coar/access_right/c_16ec
id USIMONBOL2_edf8194bb2038108288e393a0459f7a2
oai_identifier_str oai:bonga.unisimon.edu.co:20.500.12442/14584
network_acronym_str USIMONBOL2
network_name_str Repositorio Digital USB
repository_id_str
dc.title.spa.fl_str_mv Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
title Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
spellingShingle Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
Mycobacterium tuberculosis
Dinámica molecular
Docking molecular
CtpF
Inhibidores
Tuberculosis
Dinámica molecular gaussiana acelerada
ATPasa tipo P.
Mycobacterium tuberculosis
Molecular dyamics
Molecular docking
CtpF
Inhibitors
Tuberculosis
Gaussian accelerated molecular dynamics
ATPase type P.
title_short Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
title_full Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
title_fullStr Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
title_full_unstemmed Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
title_sort Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosis
dc.creator.fl_str_mv Pestana Nobles, Roberto Carlos
dc.contributor.advisor.none.fl_str_mv Yosa Reyes, Juvenal
Leyva Rojas, Jorge Alonso
dc.contributor.author.none.fl_str_mv Pestana Nobles, Roberto Carlos
dc.subject.spa.fl_str_mv Mycobacterium tuberculosis
Dinámica molecular
Docking molecular
CtpF
Inhibidores
Tuberculosis
Dinámica molecular gaussiana acelerada
ATPasa tipo P.
topic Mycobacterium tuberculosis
Dinámica molecular
Docking molecular
CtpF
Inhibidores
Tuberculosis
Dinámica molecular gaussiana acelerada
ATPasa tipo P.
Mycobacterium tuberculosis
Molecular dyamics
Molecular docking
CtpF
Inhibitors
Tuberculosis
Gaussian accelerated molecular dynamics
ATPase type P.
dc.subject.eng.fl_str_mv Mycobacterium tuberculosis
Molecular dyamics
Molecular docking
CtpF
Inhibitors
Tuberculosis
Gaussian accelerated molecular dynamics
ATPase type P.
description Mycobacterium tuberculosis (Mtb), el agente causal de la tuberculosis (TB), se clasifica predominantemente como un patógeno del sistema respiratorio, aunque tiene la capacidad de afectar otros órganos y tejidos del cuerpo. Esta enfermedad representa un desafío significativo en países con recursos económicos limitados, donde aproximadamente 1,5 millones de individuos fallecen anualmente a causa de esta. El tratamiento actual para la TB sigue las directrices establecidas por la Organización Mundial de la Salud (OMS), consistiendo principalmente en un tratamiento usando fármacos de primera línea: isoniacida (INH), rifampicina (RIF), etambutol (EMB) y pirazinamida (PZA). Una problemática central en el manejo de la TB es la interrupción prematura del tratamiento anti-TB, factor que contribuye significativamente a la aparición y propagación de cepas de Mtb resistentes a múltiples medicamentos, conocidas como Tuberculosis multi-resistente (TB-MDR) y Tuberculosis extremadamente-resistente (TB-XDR). Ante la necesidad urgente de superar las limitaciones de los tratamientos actuales y prevenir el desarrollo de resistencias bacterianas, se hace imperativo buscar y validar nuevos blancos terapéuticos que ofrezcan mecanismos alternativos de acción. En este contexto, emergen como blancos prometedores las ATPasas tipo P de Mtb, unas proteínas de membrana que catalizan el transporte de iones contra gradientes de concentración utilizando la energía derivada de la hidrólisis del ATP. Estas proteínas juegan roles esenciales en los procesos de transporte celular y en la interacción entre el patógeno y su huésped, convirtiéndose en candidatos ideales para el desarrollo de nuevas estrategias terapéuticas debido a su ubicación accesible en la membrana celular, lo cual facilita el abordaje por parte de agentes farmacológicos sin enfrentar obstáculos significativos de permeabilidad. Por lo que en este estudio se seleccionó la proteína CtpF, una bomba de eflujo específica para iones de calcio, la cual se encuentra implicada en mecanismos de defensa y supervivencia de la Mtb dentro del macrófago. Con el fin de tener una representación más fiel al entorno químico de la proteína, esta fue modelada dentro de una membrana lipídica compuesta por 1-palmitoil-2-oleoil-fosfatidilcolina (POPC) usando el servidor CHARMMGUI. Con este sistema construido se procedió a realizar una dinámica molecular gaussiana acelerada (GaMD) por 1 microsegundo, con el fin de explorar los movimientos intrínsecos de la proteína. La modelación de la proteína CtpF dentro de un ambiente simulado permitió estudiar la dinámica de la proteína y como esta se relaciona con su mecanismo enzimático, además se lograron identificar 4 compuestos con potencia inhibitoria para la proteína CtpF. A través de docking molecular, se evaluaron en total 670.000 moléculas, de las cuales 4 resultaron ser posibles inhibidores para la proteína CtpF. Estos 4 posibles inhibidores fueron evaluados usando dinámica molecular y cálculos de energía de interacción a través de MMPBSA comparando sus resultados con un ligando de referencia Ácido ciclopiazónico (CPA), energía de unión: -31.8663, donde se lograron identificar los aminoácidos que juegan un papel clave en la interacción con los ligandos. Se identificaron los siguientes compuestos, ligando L_43303, nombre IUPAC: Morfolina urea 1,2,4,5-tetraoxano, código ChEMBL: CHEMBL259023, energía libre de unión de -23.2025 kcal/mol. Ligando L_59025, nombre IUPAC: 2,2'-espirobi[6,7,8,9-tetrahidro-3H-ciclopenta[a]naftaleno]-1,1'- diona, energía libre de unión: -24.1896 kcal/mol. Ligando L_4946, nombre IUPAC: 1-[4-[3-(1-quinolin-2-ilazetidin-3-il)pirazin-2-il]piperazin-1-il]etanona, energía libre de unión: -29.358 kcal/mol. Ligando L_113260, nombre IUPAC: 10-amino-12-(3- metilfenil)-1,3,13-triazapentaciclo[11,8,0,02,11,04,9,015,20]henicosa2,4(9),10,15,17,19-hexaeno-14,21-diona, código ChEMBL: CHEMBL4213928, energía libre de unión: -30.1867 kcal/mol. Siendo el ligando L_113260 como posible punto de partida para para el diseño de nuevos fármacos mediante técnicas de hitto-lead y docking molecular basado en fragmentos. Este enfoque de la modelación molecular promete abrir nuevas vías para el desarrollo de terapias más efectivas y rápidas contra la TB, abordando así uno de los problemas de salud pública más urgente a nivel global.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-05-06T22:20:34Z
dc.date.available.none.fl_str_mv 2024-05-06T22:20:34Z
dc.date.issued.spa.fl_str_mv 2024
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.spa.spa.fl_str_mv Tesis de doctorado
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/20.500.12442/14584
url https://hdl.handle.net/20.500.12442/14584
dc.language.iso.spa.fl_str_mv spa
language spa
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.format.mimetype.spa.fl_str_mv pdf
dc.publisher.spa.fl_str_mv Ediciones Universidad Simón Bolívar
Facultad de Ciencias Básicas y Biomédicas
institution Universidad Simón Bolívar
bitstream.url.fl_str_mv https://bonga.unisimon.edu.co/bitstreams/a09a6b0d-0109-46c2-ae28-2f0b4268fd3f/download
https://bonga.unisimon.edu.co/bitstreams/50a46bd7-d215-4e5a-b6cc-92e19475c667/download
https://bonga.unisimon.edu.co/bitstreams/29c7ab45-b110-4fa8-bf11-796343a8e26b/download
https://bonga.unisimon.edu.co/bitstreams/41074cab-ba44-495e-b2c1-0db81fd2caa2/download
https://bonga.unisimon.edu.co/bitstreams/f906e7b1-e6c3-415d-adea-0c36049d2cf8/download
https://bonga.unisimon.edu.co/bitstreams/63071435-5935-48b5-bf83-1c93c481b498/download
https://bonga.unisimon.edu.co/bitstreams/5b9ffe34-e008-42c9-9006-c507d34b907d/download
https://bonga.unisimon.edu.co/bitstreams/bdcd0fb7-c2a2-4e35-8861-92f15fc9a95d/download
https://bonga.unisimon.edu.co/bitstreams/5b1bd5bc-8373-452b-9ba2-2f4f7f367fc1/download
https://bonga.unisimon.edu.co/bitstreams/24ef2b49-4d36-4e7e-91a5-88d5eca48b11/download
https://bonga.unisimon.edu.co/bitstreams/e506da2d-2f69-4c76-9fa3-1facc38c5f21/download
bitstream.checksum.fl_str_mv bbbbdff4f345ff56d1ad58b1a71cddb4
6555d31d7f54d6bdace4f8d607777014
2a1661e5960a7bab4fd8dda692fb677c
579e9e830f3211bc8f0a47a873362ebd
59cb140b944511a3bc3b53581de6f96c
579e9e830f3211bc8f0a47a873362ebd
59cb140b944511a3bc3b53581de6f96c
4302212dc34ead2fee3c0f04caaee480
424b6094e5232b5002512952a830ad6c
4302212dc34ead2fee3c0f04caaee480
424b6094e5232b5002512952a830ad6c
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
MD5
MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Digital Universidad Simón Bolívar
repository.mail.fl_str_mv repositorio.digital@unisimon.edu.co
_version_ 1812100491332550656
spelling Yosa Reyes, JuvenalLeyva Rojas, Jorge AlonsoPestana Nobles, Roberto Carlosd65233c8-25c9-4d96-b919-3b66132e6426-12024-05-06T22:20:34Z2024-05-06T22:20:34Z2024https://hdl.handle.net/20.500.12442/14584Mycobacterium tuberculosis (Mtb), el agente causal de la tuberculosis (TB), se clasifica predominantemente como un patógeno del sistema respiratorio, aunque tiene la capacidad de afectar otros órganos y tejidos del cuerpo. Esta enfermedad representa un desafío significativo en países con recursos económicos limitados, donde aproximadamente 1,5 millones de individuos fallecen anualmente a causa de esta. El tratamiento actual para la TB sigue las directrices establecidas por la Organización Mundial de la Salud (OMS), consistiendo principalmente en un tratamiento usando fármacos de primera línea: isoniacida (INH), rifampicina (RIF), etambutol (EMB) y pirazinamida (PZA). Una problemática central en el manejo de la TB es la interrupción prematura del tratamiento anti-TB, factor que contribuye significativamente a la aparición y propagación de cepas de Mtb resistentes a múltiples medicamentos, conocidas como Tuberculosis multi-resistente (TB-MDR) y Tuberculosis extremadamente-resistente (TB-XDR). Ante la necesidad urgente de superar las limitaciones de los tratamientos actuales y prevenir el desarrollo de resistencias bacterianas, se hace imperativo buscar y validar nuevos blancos terapéuticos que ofrezcan mecanismos alternativos de acción. En este contexto, emergen como blancos prometedores las ATPasas tipo P de Mtb, unas proteínas de membrana que catalizan el transporte de iones contra gradientes de concentración utilizando la energía derivada de la hidrólisis del ATP. Estas proteínas juegan roles esenciales en los procesos de transporte celular y en la interacción entre el patógeno y su huésped, convirtiéndose en candidatos ideales para el desarrollo de nuevas estrategias terapéuticas debido a su ubicación accesible en la membrana celular, lo cual facilita el abordaje por parte de agentes farmacológicos sin enfrentar obstáculos significativos de permeabilidad. Por lo que en este estudio se seleccionó la proteína CtpF, una bomba de eflujo específica para iones de calcio, la cual se encuentra implicada en mecanismos de defensa y supervivencia de la Mtb dentro del macrófago. Con el fin de tener una representación más fiel al entorno químico de la proteína, esta fue modelada dentro de una membrana lipídica compuesta por 1-palmitoil-2-oleoil-fosfatidilcolina (POPC) usando el servidor CHARMMGUI. Con este sistema construido se procedió a realizar una dinámica molecular gaussiana acelerada (GaMD) por 1 microsegundo, con el fin de explorar los movimientos intrínsecos de la proteína. La modelación de la proteína CtpF dentro de un ambiente simulado permitió estudiar la dinámica de la proteína y como esta se relaciona con su mecanismo enzimático, además se lograron identificar 4 compuestos con potencia inhibitoria para la proteína CtpF. A través de docking molecular, se evaluaron en total 670.000 moléculas, de las cuales 4 resultaron ser posibles inhibidores para la proteína CtpF. Estos 4 posibles inhibidores fueron evaluados usando dinámica molecular y cálculos de energía de interacción a través de MMPBSA comparando sus resultados con un ligando de referencia Ácido ciclopiazónico (CPA), energía de unión: -31.8663, donde se lograron identificar los aminoácidos que juegan un papel clave en la interacción con los ligandos. Se identificaron los siguientes compuestos, ligando L_43303, nombre IUPAC: Morfolina urea 1,2,4,5-tetraoxano, código ChEMBL: CHEMBL259023, energía libre de unión de -23.2025 kcal/mol. Ligando L_59025, nombre IUPAC: 2,2'-espirobi[6,7,8,9-tetrahidro-3H-ciclopenta[a]naftaleno]-1,1'- diona, energía libre de unión: -24.1896 kcal/mol. Ligando L_4946, nombre IUPAC: 1-[4-[3-(1-quinolin-2-ilazetidin-3-il)pirazin-2-il]piperazin-1-il]etanona, energía libre de unión: -29.358 kcal/mol. Ligando L_113260, nombre IUPAC: 10-amino-12-(3- metilfenil)-1,3,13-triazapentaciclo[11,8,0,02,11,04,9,015,20]henicosa2,4(9),10,15,17,19-hexaeno-14,21-diona, código ChEMBL: CHEMBL4213928, energía libre de unión: -30.1867 kcal/mol. Siendo el ligando L_113260 como posible punto de partida para para el diseño de nuevos fármacos mediante técnicas de hitto-lead y docking molecular basado en fragmentos. Este enfoque de la modelación molecular promete abrir nuevas vías para el desarrollo de terapias más efectivas y rápidas contra la TB, abordando así uno de los problemas de salud pública más urgente a nivel global.Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is primarily classified as a pathogen of the respiratory system, yet it possesses the capacity to affect other organs and body tissues. This disease presents a significant challenge in countries with limited economic resources, where approximately 1.5 million individuals die annually from it. The current TB treatment adheres to the guidelines established by the World Health Organization (WHO), mainly involving a regimen utilizing first-line drugs: isoniazid (INH), rifampicin (RIF), ethambutol (EMB), and pyrazinamide (PZA). A critical issue in TB management is the premature discontinuation of anti-TB treatment, a factor that significantly contributes to the emergence and spread of multi-drug resistant Mtb strains, known as Multi-Drug Resistant Tuberculosis (MDR-TB) and Extensively Drug-Resistant Tuberculosis (XDR-TB). Faced with the urgent need to surpass the limitations of current treatments and prevent the development of bacterial resistances, it is imperative to identify and validate new therapeutic targets that offer alternative mechanisms of action. In this regard, Mtb P-type ATPases, membrane proteins that catalyze ion transport against concentration gradients using energy from ATP hydrolysis, emerge as promising targets. These proteins play crucial roles in cellular transport processes and the pathogen-host interaction, making them ideal candidates for the development of new therapeutic strategies due to their accessible location on the cell membrane, which facilitates the approach by pharmacological agents without significant permeability obstacles. In light of the foregoing, this study selected the CtpF protein, a specific efflux pump for calcium ions involved in Mtb's defense and survival mechanisms inside the macrophage. To closely mimic the protein's chemical environment, it was modeled within a lipid membrane composed of 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) using the CHARMMGUI server. With this constructed system, accelerated Gaussian molecular dynamics (GaMD) were conducted for 1 microsecond to explore the protein's intrinsic movements. Modeling the CtpF protein in a simulated environment allowed for the study of its dynamics and relation to its enzymatic mechanism, also enabling the identification of 4 compounds with inhibitory potential for the CtpF protein. Through molecular docking, a total of 670,000 molecules were evaluated, resulting in 4 as potential CtpF protein inhibitors. These 4 potential inhibitors were further assessed using molecular dynamics and interaction energy calculations through MMPBSA, comparing their results with a reference ligand, Cyclopiazonic Acid (CPA), with a binding energy of - 31.8663 kcal/mol, identifying key amino acids involved in the interaction with the ligands. The following compounds were identified: ligand L_43303, IUPAC name Morpholine urea 1,2,4,5-tetraoxane, ChEMBL code: CHEMBL259023, with a free binding energy of -23.2025 kcal/mol. Ligand L_59025, IUPAC name: 2,2'- spirobi[6,7,8,9-tetrahydro-3H-cyclopenta[a]naphthalene]-1,1'-dione, with a free binding energy of -24.1896 kcal/mol. Ligand L_4946, IUPAC name: 1-[4-[3-(1- quinolin-2-ylazetidin-3-yl)pyrazin-2-yl]piperazin-1-yl]ethanone, with a free binding energy of -29.358 kcal/mol. Ligand L_113260, IUPAC name: 10-amino-12-(3- methylphenyl)-1,3,13-triazapentacyclo[11,8,0,02,11,04,9,015,20]henicosa2,4(9),10,15,17,19-hexaene-14,21-dione, ChEMBL code: CHEMBL4213928, with a free binding energy of -30.1867 kcal/mol. Ligand L_113260 was identified as a potential starting point for new drug design using hit-to-lead techniques and fragment-based molecular docking. This molecular modeling approach promises to open new pathways for the development of more effective and rapid therapies against TB, thereby addressing one of the most urgent global public health issuespdfspaEdiciones Universidad Simón BolívarFacultad de Ciencias Básicas y BiomédicasMycobacterium tuberculosisDinámica molecularDocking molecularCtpFInhibidoresTuberculosisDinámica molecular gaussiana aceleradaATPasa tipo P.Mycobacterium tuberculosisMolecular dyamicsMolecular dockingCtpFInhibitorsTuberculosisGaussian accelerated molecular dynamicsATPase type P.Mecanismo de la ATPasa tipo P CtpF de Mycobacterium tuberculosis como diana para nuevos antibióticos antituberculosisinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecinfo:eu-repo/semantics/doctoralThesisTesis de doctoradohttp://purl.org/coar/resource_type/c_db06Geneva WHO. Global tuberculosis report 2022. Glob. Tuberc. Rep, 2022, 2022Geneva WHO. Global tuberculosis report 2023. Glob. Tuberc. Rep, 2023, 2023.Madhukar Pai, Marcel A. Behr, David Dowdy, Keertan Dheda, Maziar Divangahi, Catharina C. Boehme, Ann Ginsberg, Soumya Swaminathan, Melvin Spigelman, Haileyesus Getahun, Dick Menzies, and Mario Raviglione. Tuberculosis. Nature Reviews Disease Primers, 2(l):16076, 2016. doi: 10.1038/nrdp.2016.76.Turner Brown, Matthieu Chavent, and Wonpil Irn. Molecular Modeling and Simulation of the Mycobacterial Cell Envelope: From Individual Components to Cell Envelope Assemblies. The Journal of Physical Chemistry B, 2023. ISSN 1520-6106. doi: 10. 1021/acs.jpcb.3c06136.R. Tassoni, Anneloes J. Blok, N. Pannu, and M. Ubbink. New conformations of acylation adducts of inhibitors of beta-lactamase from mycobacterium tuberculosis. Biochemistry, 58:997-1009, 2019. doi: 10.2210/PDB6H2A/PDB.Ministerio de Salud. Abecé tuberculosis. https://www.minsalud.gov.co/sites/ rid/Lists/BibliotecaDigital/RIDE/VS/PP/ET/abece-tuberculosis-msps.pdf.K. Seung, S. Keshavjee, and M. Rich. Multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis. Coid Spring Harbor perspectives in medicine, 5 9:a017863, 2015. doi: 10.1101/cshperspect.a017863.A. Iacobino, L. Fattorini, and F. Giannoni. Drug-resistant tuberculosis 2020: Where we stand. Applied Sciences, 2020. doi: 10.3390/appl0062153.L. Rodrigues, C. Villellas, R. Bailo, M. Viveiros, and J. A. Aínsa. Role of the mmr efflux pump in drug resistance in mycobacterium tuberculosis. Antimicrobial Agents and Chemotherapy, 57:751-757, 2013. doi: 10.1128/aac.01482-12.E. Kapp, S. F. Malan, J. Joubert, and S. L. Sampson. Small molecule efflux pump inhibitors in mycobacterium tuberculosis: a ratioual drug design perspective. MiniReviews in Medicinal Chemistry, 18, 2017. doi: 10.2174/1389557517666170510105506.H. Botella, P. Peyron, F. Levillain, R. Poincloux, Y. Poquet, I. Brandli, C. Wang, L. Tailleux, S. Tilleul, G. M. Charriére, S. J. Waddell, M. Foti, G. Lugo-Villarino, Q. Gao, I. Maridonneau-Parini, P. D. Butcher, P. R. Castagnoli, B. Gicquel, C. d. Chastellier, and O. Neyrolles. Mycobacterial pl-type atpases mediate resistance to zinc poisoning in human macrophages. Cell Host &Amp; Microbe, 10:248-259, 2011. doi: 10.1016/j.chom.2011.08.006.F. Wolschendorf, D. F. Ackart, T. B. Shrestha, L. Hascall-Dove, S. T. Nolan, G. Lamichhane, Y. Wang, S. H. Bossmann, R. J. Basaraba, and M. Niederweis. Copper resistance is essential for virulence of mycobacterium tuberculosis. Proceedings of the National Academy of Sciences, 108:1621-1626, 2011. doi: 10.1073/pnas.1009261108.Milena Maya-Hoyos, Cristian Rosales, Lorena Novoa-Aponte, Elianna Castillo, and Carlos Y. Soto. The P-type ATPase CtpF is a plasma membrane transporter mediating calcium efflux in Mycobacterium tuberculosis cells. Helíyon, 5(ll):e02852, 2019. ISSN 2405-8440. doi: 10.1016/j.heliyon.2019.e02852.P. Santos, F. López-Vallejo, D. Ramírez, J. Caballero, D. M. Espinosa, R. HernándezPando, and C. Y. Soto. Identification of mycobacterium tuberculosis ctpf as a target for designing new antituberculous compounds. Bioorganic &Amp; Medicinal Chemistry, 28:115256, 2020. doi: 10.1016/j.bmc.2019.115256.L. Novoa-Aponte, A. León-Torres, M. Patiño-Ruiz, J. Cuesta-Bernal, L. Salazar, D. Landsman, L. Mariño-Ramírez, and C. Soto. In silico identification and characterization of the ion transport specificity for p-type atpases in the mycobacterium tuberculosis complex. BMC Structural Biology, 12, 2012. doi: 10.1186/1472-6807-12-25.T. Padilla-Benavides, J. E. Long, D. Raimunda, C. M. Sassetti, and J. M. Argüello. A novel plb-type mn2+-transporting atpase is required for secreted protein metallation in mycobacteria. Journal of Biological Chemistry, 288:11334-11347, 2013. doi: 10. 1074/jbc.mll2.448175.David Mendez, Anna Gaulton, A Patricia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F Mosquera, Prudence Mutowo, Michal Nowot- ka, María Gordillo-Marañón, Fiona Hunter, Laura Junco, Grace Mugumbate, Milagros Rodriguez-Lopez, Francis Atkinson, Nicolás Bosc, Chris J Radoux, Aldo SeguraCabrera, Anne Hersey, and Andrew R Leach. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Research, 47(Dl):D930-D940, 11 2018. ISSN 0305-1048. doi: 10.1093/nar/gkyl075.John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchuluun, Benja mín R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, John Mayfield, and Ro- ger A. Sayle. Zinc20—a free ultralarge-scale chemical database for ligand disco- very. Journal of Chemical Information and Modeling, 60(12):6065 6073, 2020. doi: 10.1021/acs.jcim.0c00675A. Vedani and M. Smiesko. in silico toxicology in drug discovery — concepts based on three-dimensional models. Alternatives to Laboratory Animáis, 37:477-496, 2009. doi: 10.1177/026119290903700506.T. Hartung and S. Hoffmann. Food for thought ... on in silico methods in toxicology. ALTEX, 26 3:155-66, 2009. doi: 10.14573/ALTEX.2009.3.155.Claire Jean-Quartier, Fleur Jeanquartier, I. Jurisica, and Andreas Holzinger. In silico cáncer research towards 3r. BMC Cáncer, 18, 2018. doi: 10.1186/sl2885-018-4302-0S. Ekins, J. Mestres, and B. Testa. In silico pharmacology for drug discovery: applications to targets and beyond. British Journal of Pharmacology, 152:21 - 37, 2007. doi: 10.1038/sj.bjp.0707306Luca Pinzi and G. Rastelli. Molecular docking: Shifting paradigms in drug discovery. International Journal of Molecular Sciences, 20, 2019. doi: 10.3390/ijms20184331.D. Shaw, P. Maragakis, K. Lindorff-Larsen, S. Piaña, R. Dror, M. Eastwood, J. A. Bank, J. Jumper, J. Salmón, Y. Shan, and W. Wriggers. Atomic-level characterization of the structural dynamics of proteins. Science, 330:341 - 346, 2010. doi: 10.1126/ science. 1187409.J. L. Klepeis, K. Lindorff-Larsen, R. Dror, and D. Shaw. Long-timescale molecular dynamics simulations of protein structure and function. Current opinión in structural biology, 19 2:120-7, 2009. doi: 10.1016/j.sbi.2009.03.004.B. J. Neves, R. Braga, C. Melo-Filho, J. T. Moreira-Filho, E. Muratov, and C. Andrade. Qsar-based virtual screening: Advances and applications in drug discovery. Frontiers in Pharmacology, 9, 2018. doi: 10.3389/fphar.2018.01275Iñaki Comas, Mireia Cosedla, Tao Luo, Sonia Borrell, Kathryn E. Holt, Midori KatoMaeda, Julián Parkhill, Bijaya Malla, Stefan Berg, Guy Thwaites, Dorothy YeboahManu, Graham Bothamley, Jian Mei, Lanhai Wei, Stephen Bentley, Simón R. Harris, Stefan Niemann, Roland Diel, Abraham Aseffa, Qian Gao, Douglas Young, and Sebastien Gagneux. Out-of-africa migration and neolithic coexpansion of mycobacterium tuberculosis with modern humans. Nature Genetics, 45(10):1176-1182, Oct 2013. ISSN 1546-1718. doi: 10.1038/ng.2744. URL https://doi.org/10.1038/ng.2744.I. Barberis, N. Bragazzi, L. Galluzzo, and M. Martini. The history of tuberculosis: frorn the first historical records to the isolation of koch’s bacillus. Journal of Preventive Medicine and Hygiene, 58:E9 - E12, 2017. doi: 10.15167/2421-4248/JPMH2017.58.1. 728J. Cataño and J. Robledo. Tuberculous lymphadenitis and parotitis. Microbiology spectrum, 4 6, 2016. doi: 10.1128/microbiolspec.TNMI7-0008-2016.J. Prat and S. M. F. M. d. Souza. Prehistoric tuberculosis in america: adding comments to a literature review. Memorias Do Instituto Oswaldo Cruz, 98:151-159, 2003. doi: 10.1590/s0074-02762003000900023.Madhukar Pai, Marcel A. Behr, David Dowdy, Keertan Dheda, Maziar Divangahi, Catharina C. Boehme, Ann Ginsberg, Soumya Swaminathan, Melvin Spigelman, Haileyesus Getahun, Dick Menzies, and Mario Raviglione. Tuberculosis. Nature Reviews Disease Primers, 2(l):16076, 2016. doi: 10.1038/nrdp.2016.76M. Campo and L. Kawamura. What is tuberculosis (tb)? American journal of respiratory and critical care medicine, 195 4:P7-P8, 2017. doi: 10.1164/rccm.l954P7.Alexis Griffith and C. Trabue. You have gut tb kidding me. Journal of Community Hospital Internal Medicine Perspectives, 13:30 - 32, 2023. doi: 10.55729/2000-9666. 1147.C. Barry, H. Boshoff, V. Dartois, T. Dick, S. Ehrt, J. Flynn, D. Schnappinger, R. Wilkinson, and D. Young. The spectrum of latent tuberculosis: rethinking the bio- logy and intervention strategies. Nature Reviews Microbiology, 7:845-855, 2009. doi: 10.1038/nrmicro2236.J. Chan and J. Flynn. The immunological aspeets of lateney in tuberculosis. Clinical immunology, 110 1:2-12, 2004. doi: 10.1016/S1521-6616(03)00210-9.Adam Cohén, V. Mathiasen, T. Schón, and C. Wejse. The global prevalence of latent tuberculosis: a systematic review and meta-analysis. European Respiratory Journal, 54, 2019. doi: 10.1183/13993003.00655-2019.D. Russell. Who puts the tubercle in tuberculosis? Nature Reviews Microbiology, 5: 39-47, 2007. doi: 10.1038/nrmicrol538.Manlu Zhu and Xiongfeng Dai. On the intrinsic constraint of bacterial growth rate: M. tuberculosis’s view of the protein translation capacity. Critical Reviews in Microbiology, 44:455 -464, 2018. doi: 10.1080/1040841X.2018.1425672.Steven A. Porcelli and William R. JacobsJr. Exacting Edward Jenner’s revenge: The quest for a new tuberculosis vaccine. Science Translational Medicine, ll(490):eaax4219, 2019. ISSN 1946-6234. doi: 10.1126/scitranslmed.aax4219.Edward C. Jones-López, Olive Namugga, Francis Mumbowa, Martín Ssebidandi, Olive Mbabazi, Stephanie Moine, Gerald Mboowa, Matthew P. Fox, Nancy Reilly, Irene Ayakaka, Soyeon Kinr, Alphonse Okwera, Moses Joloba, and Kevin P. Fennelly. Cough ae- rosols of mycobacterium tuberculosis predict new infection. a household contact study. American Journal of Respiratory and Critical Care Medicine, 187(9): 1007— 1015, 2013. doi: 10.1164/rccm.201208-14220CK. Fennelly, E. Jones-López, I. Ayakaka, Soyeon Kim, Harriet Menyha, B. Kirenga, Christopher Muchwa, M. Joloba, S. Dryden-Peterson, Nancy Reilly, A. Okwera, A. Elliott, Peter G. Smith, R. Mugerwa, K. Eisenach, and J. Ellner. Variability of in- fectious aerosols produced during coughing by patients with pulmonary tuberculosis. American journal of respiratory and critical care medicine, 186 5:450-7, 2012. doi: 10.1164/rccm.201203-0444OC.S. Martín and E. Sabina. Malnutrition and associated disorders in tuberculosis and its therapy. Journal of Dietary Supplements, 16:602 - 610, 2018. doi: 10.1080/19390211. 2018.1472165.B. Ayelign, Markos Negash, Meaza Genetu, Tadelo Wondmagegn, and Tewodros Shibabaw. Immunological impacts of diabetes on the susceptibility of mycobacterium tuberculosis. Journal of Immunology Research, 2019, 2019. doi: 10.1155/2019/6196532.L. Bell and M. Noursadeghi. Pathogenesis of hiv-1 and mycobacterium tuberculosis co- infection. Nature Reviews Microbiology, 16:80-90, 2017. doi: 10.1038/nrnricro.2017.128.S. Doherty, A. V. Van Voorhees, M. Lebwohl, N. Forman, Melodie S. Young, S. Hsu, and National Psoriasis Foundation. National psoriasis foundation consensus statement on screening for latent tuberculosis infection in patients with psoriasis treated with systemic and biologic agents. Journal of the American Academy of Dermatology, 59 2: 209-17, 2008. doi: 10.1016/j.jaad.2008.03.023T. Garnier, K. Eiglmeier, J. Camus, N. Medina, H. Mansoor, M. Pryor, S. Duthoy, S. Grondin, C. Lacroix, Christel Monsempe, S. Simón, B. Harris, R. Atkin, J. Doggett, R. Mayes, L. Keating, P. Wheeler, J. Parkhill, B. Barrell, S. Colé, S. Gordon, and R. G. Hewinson. The complete genome sequence of mycobacterium bovis. Proceedings of the National Academy of Sciences of the United States of América, 100:7877- 7882, 2003. doi: 10.1073/pnas. 1130426100.Alicia Aranaz, Debby Cousins, Ana Mateos, and Lucas Domínguez. Elevation of mycobacterium tuberculosis subsp. caprae aranaz et al. 1999 to species rank as mycobacterium caprae comb. nov., sp. nov. Internatíonal Journal of Systema- tic and Evolutionary Microbiology, 53(6): 1785-1789, 2003. ISSN 1466-5034. doi: https://doi.org/10.1099/ijs.0.02532-0. URL https : //www.microbiologyresearch. org/contení/journal/ij sem/10.1099/ij s.0.02532-0.J. v. Ingen, Z. Rahim, A. Mulder, M. J. Boeree, R. Siméone, R. Brosch, and D. v. Soolingen. Characterization ofmycobacterium orygisasm. tuberculosiscomplex subspecies. Emerging Infectious Díseases, 18:653-655, 2012. doi: 10.3201/eidl804.110888.B. D. de Jong, M. Antonio, and S. Gagneux. Mycobacterium africanum—review of an important cause of human tuberculosis in west africa. PLoS Neglected Tropical Díseases, 4, 2010. doi: 10.1371/journal.pntd.0000744.D. Cousins, R. Bastida, A. Cataldi, V. Quse, S. Redrobe, S. Dow, P. Duignan, A. Murray, C. Dupont, N. Ahmed, D. Collins, W. Butler, D. Dawson, D. Rodríguez, J. Loureiro, M. Romano, A. Alito, M. Zumárraga, and A. Bernardelli. Tuberculosis in seáis caused by a novel member of the mycobacterium tuberculosis complex: Mycobacterium pinnipedii sp. nov. Internatíonal journal of systematic and evolutionary microbiology, 53 Pt 5:1305-14, 2003. doi: 10.1099/IJS.0.02401-0.N. Smith, T. Crawshaw, J. Parry, and R. Birtles. Mycobacterium microti: More diverse than previously thought. Journal of Clinical Microbioloqy, 47:2551 - 2559, 2009. doi: 10.1128/JCM.00638-09M. Fabre, Y. Hauck, C. Soler, J. Koeck, J. van Ingen, D. van Soolingen, G. Vergnaud, and C. Pourcel. Molecular characteristics of ” mycobacterium canettii”the smooth mycobacterium tuberculosis bacilli. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious díseases, 10 8:1165-73, 2010. doi: 10.1016/j.meegid.2010.07.016D. Bespiatykh, J. Bespyatykh, I. Mokrousov, and E. Shitikov. A comprehensive map of mycobacterium tuberculosis complex regions of difference. mSphere, 6, 2021. doi: 10.1128/msphere.00535-21.R. Brosch, S. V. Gordon, M. Marmiesse, P. Brodin, C. Buchrieser, K. Eiglmeier, T. Garnier, C. Gutiérrez, R. G. Hewinson, K. Kremer, L. M. Parsons, A. S. Pym, S. Samper, D. v. Soolingen, and S. T. Colé. A new evolutionary scenario for the mycobacterium tuberculosis complex. Proceedings of the National Academy of Sciences, 99:3684- 3689, 2002. doi: 10.1073/pnas.052548299.S. Stanley, A. Barczak, M. Silvis, Samantha S Luo, Kimberly M Sogi, Martha S. Vokes, M. Bray, Anne E Carpenter, C. B. Moore, N. Siddiqi, E. Rubín, and D. Hung. Identification of host-targeted small molecules that restrict intracellular mycobacterium tuberculosis growtli. PLoS Pathogens, 10, 2014. doi: 10.1371/journal.ppat. 1003946.Dennis Wong, H. Bach, Jim Sun, Z. Hmama, and Y. Av-Gay. Mycobacterium tubercu losis protein tyrosine phosphatase (ptpa) exeludes host vacuolar-hj—atpase to inhibit phagosome acidification. Proceedings of the National Academy of Sciences, 108:19371 - 19376, 2011. doi: 10.1073/pnas.ll09201108.J. Kiuchen and K. Ravichandran. Phagosome maturation: going through the acid test. Nature Reviews Molecular Cell Biology, 9:781-795, 2008. doi: 10.1038/nrm2515.S. Al-Shehri. Reactive oxygen and nitrogen species and innate immune response. Biochimie, 2020. doi: 10.1016/j.biochi.2020.11.022O. Neyrolles, F. Wolschendorf, A. Mitra, and M. Niederweis. Mycobacteria, metáis, and the macrophage. Immunological Reviews, 264:249-263, 2015. doi: 10.1111/imr.12265.Stefan Ehlers and Ulrich Schaible. The granuloma in tuberculosis: Dynamics of a host-pathogen collusion. Frontiers in Immunology, 3, 2013. ISSN 1664-3224. doi: 10. 3389/fimmu.2012.00411. URL https : //www. frontiersin.org/articles/10.3389/ fimmu.2012.00411.Antonio J. Pagán and Balita Ramakrishnan. The formation and function of gra nulomas. Annual Review of Immunology, 36(l):639-665, 2018. doi: 10.1146/ annurevimmunol-032712-100022. PMID: 29400999.A. Litvinov and B. Ariel, [historical reference: giant multinuclear cells in tubercular granuloma]. Problemy tuberkuleza i boleznei legkikh, 11:59-61, 2005.I. Rosenkrands, R. Slayden, Janne Crawford, C. Aagaard, C. Barry, and P. Andersen. Hypoxic response of mycobacterium tuberculosis studied by metabolic labeling and proteome analysis of cellular and extracellular proteins. Journal of Bacteriology, 184: 3485 - 3491, 2002. doi: 10.1128/JB.184.13.3485-3491.2002.P. Cardona and J. Ruiz-Manzano. On the nature of mycobacterium tuberculosis-latent bacilli. European Respiratory Journal, 24:1044 - 1051, 2004. doi: 10.1183/09031936. 04.00072604.C. J. Queval, O. R. Song, J. P. Carralot, J. Saliou, A. Bongiovanni, G. Deloison, N. Deboosére, S. Jouny, R. Iantomasi, V. Delorme, A. Debrie, S. J. Park, J. Costa-Gouveia, S. Tomavo, R. Brosch, A. Yoshimura, E. Yeramian, and P. Brodin. Mycobacterium tuberculosis controls phagosomal acidification by targeting cish-mediated signaling. Cell Reports, 20:3188-3198, 2017. doi: 10.1016/j.celrep.2017.08.101.Weijie Zhai, Fengjuan Wu, Yiyuan Zhang, Yurong Fu, and Zhijun Liu. The Immune Escape Mechanisms of Mycobacterium Tuberculosis. International Journal of Molecular Sciences, 20(2):340, 2019. doi: 10.3390/ijms20020340.D. Machado, I. Couto, J. Perdigáo, L. Rodrigues, I. Portugal, P. V. Baptista, B. Veigas, L. Amaral, and M. Viveiros. Contribution of efflux to the emergence of isoniazid and multidrug resistance in mycobacterium tuberculosis. PLoS ONE, 7:e34538, 2012. doi: 10.1371 /journal.pone.0034538.. B. Abomoelak, S. Marcus, Sarah K. Ward, P. Karakousis, H. Steinberg, and A. Talaat. Characterization of a novel heat shock protein (hsp22.5) involved in the pathogenesis of mycobacterium tuberculosis. Journal of Bacteriology, 193:3497 - 3505, 2011. doi: 10.1128/JB.01536-10.Yangli, ZhangCaiqin, Zhaoyong, ZhaoNingning, WuPengpeng, Zhanghai, and ShiChanghong. Effects of mycobacterium tuberculosis rnutant strain hspl6.3 gene on rnurine raw 264.7 macrophage autophagy. DNA and Cell Bioloqy, 37:7-14, 2018. doi: 10.1089/DNA.2016.3599.Nooruddin Khan, Kaiser Alarn, S. Mande, V. Valluri, S. E. Hasnain, and S. Mukhopadhyay. Mycobacterium tuberculosis heat shock protein 60 modulates immune response to ppd by manipulating the surface expression of tlr2 on macrophages. Cellular Microbiology, 10, 2008. doi: 10.1111/j.l462-5822.2008.01161.x.Zulfiqar A. Malik, Gerene M. Denning, and David J. Kusner. Inhibition of Ca2+ Signaling by Mycobacterium tuberculosisls Associated with Reduced Phagosome-Lysosome Fusión and Increased Survival within Human Macrophages. Journal of Experimental Medicine, 191(2):287-302, 01 2000. ISSN 0022-1007. doi: 10.1084/jem.l91.2.287. URL https://doi.org/10.1084/jem.191.2.287.Zulfiqar A. Malik, Christopher R. Thompson, Samad Hashimi, Brandon Porter, Shankar S. Iyer, and David J. Kusner. Cutting Edge: Mycobacterium tuberculosis Blocks Ca2+ Signaling and Phagosome Maturation in Human Macrophages Via Specific Inhibition of Sphingosine Kinasel. The Journal of Immunology, 170 (6) :2811-2815, 03 2003. ISSN 0022-1767. doi: 10.4049/jimmunol.l70.6.2811. URL https://doi.org/10.4049/j immunol.170.6.2811.Elsie Lee and R. Holzman. Evolution and current use of the tuberculin test. Clinical infectious diseases : an official publication ofthe Infectious Diseases Society of América, 34 3:365-70, 2002. doi: 10.1086/338149.M. Sester, G. Sotgiu, C. Lange, C. Giehl, E. Girardi, G. Migliori, A. Bossink, K. Dheda, R. Diel, J. Domínguez, M. Lipman, J. Nemeth, P. Ravn, S. Winkler, E. Huitric, A. Sandgren, and D. Manissero. Inferieron-; release assays for the diagnosis of active tuberculosis: a systematic review and meta-analysis. European Respiratory Journal, 37:100 - 111, 2010. doi: 10.1183/09031936.00114810.V. Alien, M. Nicol, and L. A. Tow. Sputum processing prior to mycobacterium tu berculosis detection by culture or nucleic acid amplification testing: a narrative review. Research and Reviews: Journal of Microbiology and Biotechnology, 5:120-130, 2015.Jiaru Yang, Xinlin Han, Aihua Liu, X. Bai, Cui ping Xu, Fukai Bao, Shi Feng, Lvyan Tao, Mingbiao Ma, and Yun Peng. Use of digital droplet per to detect mycobacterium tuberculosis dna in whole blood-derived dna samples from patients with pulmonary and extrapulmonary tuberculosis. Frontiers in Cellular and Infection Mícrobiology, 7, 2017. doi: 10.3389/fcimb.2017.00369.Andrej Trauner, Sonia Borrell, Klaus Reither, and Sebastien Gagneux. Evolution of drug resistance in tuberculosis: Recent progress and implications for diagnosis and therapy. Drugs, 74(10):1063-1072, Jul 2014. ISSN 1179-1950. doi: 10.1007/s40265-014-0248-y. URL https: //doi. org/10.1007/s40265-014-0248-y.Monique Combrink, D. T. Loots, and I. du Preez. Metabolomics describes previously unknown toxicity mechanisms of isoniazid and rifampicin. Toxicology letters, 2020. doi: 10.1016/j.toxlet.2020.01.018M. Ramakrishnan, Y. Nisha, J Ezhil, and K. Krishnamoorthy. Adverse drug reaction monitoring of antitubercular drugs during intensive phase at tertiary care medical college hospital: A prospective study. National Journal of Physiology, Pharmacy and Pharmacology, 10:0-0, 2020. doi: 10.5455/njppp.2020.10.07179202017072020.Center for Disease Control and Prevention. The costly burden of drug-resistant tb disease in the u.s. https://www.cdc.gov/nchhstp/newsroom/fact-sheets/tb/ costlyburden-drug-resistant.html, 2021. Accedido 10-01-2024.D. Franco, G. Calabrese, S. Guglielmino, and S. Conoci. Metal-based nanoparticles: Antibacterial mechanisms and biomedical application. Microorganisms, 10, 2022. doi: 10.3390/microorganismsl0091778.S. Gelperina, K. Kisich, M. Iseman, and L. Heifets. The potential advantages of nanoparticle drug delivery systems in chemotherapy of tuberculosis. Ameri- can journal of respiratory and critical care medicine, 172 12:1487-90, 2005. doi: 10.1164/RCCM.200504-613PP.J. Costa-Gouveia, J. Aínsa, P. Brodin, and Ainhoa Lucía. How can nanoparticles contribute to antituberculosis therapy? Drug discovery today, 22 3:600-607, 2017. doi: 10.1016/j.drudis.2017.01.011.J. Trousil, Z. Syrová, N. K. Dal, D. Rak, R. Konefal, E. Pavlova, J. Matéjková, D. Crnarko, P. Kubícková, O. Pavlis, Tomás Urbánek, M. Sedlák, Federico Fenaroli, I. Raska, P. Stépánek, and M. Hruby. Rifampicin nanoformulation enhances treatrnent of tuberculosis in zebrafish. Biomacromolecules, 20 4:1798-1815, 2019. doi: 10.1021/acs.biomac.9b00214.P. Prabhu, Trinette Fernandes, Pramila Chaubey, P. Kaur, S. Narayanan, Ramya Vk, and S. Sawarkar. Mannose-conjugated chitosan nanoparticles for delivery of rifampicin to osteoarticular tuberculosis. Drug Delivery and Translational Research, 11:1509 - 1519, 2021. doi: 10.1007/sl3346-021-01003-7.C. R. Horsburgh, C. E. Barry, and C. Lange. Treatment of tuberculosis. New England Journal of Medicine, 373:2149-2160, 2015. doi: 10.1056/nejmral413919.P. E. Almeida da Silva and J. Palomino. Molecular basis and mechanisms of drug resistance in mycobacterium tuberculosis: classical and new drugs. The Journal of antimicrobial chemotherapy, 66 7:1417-30, 2011. doi: 10.1093/jac/dkrl73.A. Bahuguna and D. Rawat. An overview of new antitubercular drugs, drug candidates, and their targets. Medicinal Research Reviews, 40:263 - 292, 2020. doi: 10.1002/med. 21602.C. S. Rule, M. Patrick, and M. Sandkvist. Measuring in vitro atpase activity for enzymatic characterization. Journal of Visualized Experiments, 2016. doi: 10.3791/ 54305.C. Prodromou, B. Panaretou, S. Chollan, G. Siligardi, R. C. O’Brien, J. E. Ladbury, S. M. Roe, P. W. Piper, and L. H. Pearl. The atpase cycle of hsp90 drives a molecular clamp’ via transient dimerization of the n-terminal domains. The EMBO Journal, 19: 4383-4392, 2000. doi: 10.1093/emboj/19.16.4383.L. V. Zingman, D. M. Hodgson, P. Bast, G. C. Kane, C. Perez-Terzic, R. J. Gumina, D. Pucar, M. Bienengraeber, P. P. Dzeja, T. Miki, S. Seino, A. E. Alekseev, and A. Terzic. Kir6.2 is required for adaptation to stress. Proceedings of the Nat.ional Academy of Sciences, 99:13278-13283, 2002. doi: 10.1073/pnas.212315199.P. L. Pedersen. Transport atpases into the year 2008: a brief overview related to types, structures, functions and roles in health and disease. Journal of Bioenergetics and Biomembranes, 39:349-355, 2007. doi: 10.1007/sl0863-007-9123-9.H. Apell. Reviews of Physiology, Biochemistry and Pharmacology. Springer Charn, 2003.M. Dyla, M. Kjaergaard, H. Poulsen, and P. Nissen. Structure and mechanism of p-type atpase ion pumps. Annual Review of Biochemistry, 89:583-603, 2020. doi: 10.1146/annur ev-bio chem-010611-112801T. Vasanthakumar and J. Rubinstein. Structure and roles of v-type atpases. Trends in biochemical sciences, 2020. doi: 10.1016/j.tibs.2019.12.007.P. Hanson and S. Whiteheart. Aaa+ proteins: have engine, will work. Nature Reviews Molecular Cell Biology, 6:519-529, 2005. doi: 10.1038/nrml684.W. Kühlbrandt. Structure and mechanisms of f-type atp synthases. Annual review of biochemistry, 88:515-549, 2019. doi: 10.1146/annurev-biochem-013118-110903.V. Zubareva, A. S. Lapashina, T. Shugaeva, A. V. Litvin, and B. Feniouk. Rotary ion-translocating atpases/atp synthases: Diversity, similarities, and differences. Biochemistry (Moscow), 85:1613-1630, 2020. doi: 10.1134/S0006297920120135.M. Bublitz, J. P. Morth, and P. Nissen. P-type atpases at a glance. Journal of Cell Science, 124:2515-2519, 2011. doi: 10.1242/jcs.088716.I. Voskoboinik, J. Camakaris, and J. Mercer. Understanding the mechanism and function of copper p-type atpases. Advances in protein chemistry, 60:123-50, 2002. doi: 10.1016/S0065-3233(02)60053-l.H. L. Y. Chan, V. Babayan, E. Blyumin, C. Gandhi, K. Hak, D. Harake, K. K. Kumar, P. Lee, T. T. Li, H. Y. Liu, T. C. T. Lo, C. J. Meyer, S. Stanford, K. S. Zamora, and M. H. Saier. The p-type atpase superfamily. Microbíal Physiology, 19:5-104, 2010. doi: 10.1159/000319588.L. Novoa-Aponte and C. Y. S. Ospina. mycobacterium tuberculosisp-type atpases: possible targets for drug or vaccine development. BioMed Research Internatíonal, 2014:1-9, 2014. doi: 10.1155/2014/296986.W. Kiihlbrandt. Biology, structure and mechanism of p-type atpases. Nature Reviews Molecular Cell Biology, 5:282-295, 2004. doi: 10.1038/nrml354G. Hu, W. J. Rice, S. Dróse, K. Altendorf, and D. L. Stokes. Three-dimensional structure of the kdpfabc complex of escherichia coli by electrón tomography of two- dimensional crystals. Journal of Structural Bioloqy, 161:411-418, 2008. doi: 10.1016/ j.jsb.2007.09.006.M. Periasamy, S. Maurya, S. Sahoo, Sushant Singh, F. C. Reis, and N. Bal. Role of serca pump in rnuscle thermogenesis and metabolism. Comprehensive Physiology, 7 3: 879-890, 2017. doi: 10.1002/cphy.cl60030.E. Strehler. Plasma membrane calcium atpases: From generic ca(2+) sump pumps to versatile systems for fine-tuning cellular ca(2.). Biochemical and bíophysical research communications, 460 1:26-33, 2015. doi: 10.1016/j.bbrc.2015.01.121.A. Askari. The sodium pump and digitalis drugs: Dogmas and fallacies. Pharmacology Research & Perspectives, 7, 2019. doi: 10.1002/prp2.505.M. Faraco, Y. Li, S. Li, C. Spelt, G. P. D. Sansebastiano, L. Reale, F. Ferranti, W. Verweij, R. Koes, and F. Quattrocchio. A tonoplast p3b-atpase mediates fusión of two types of vacuoles in petal cells. Cell Reports, 19:2413-2422, 2017. doi: 10.1016/j.celrep.2017.05.076.J. Andersen, Amia L. Vestergaard, Stine A. Mikkelsen, L. Mogensen, Madhavan Chalat, and R. Molday. P4-atpases as phospholipid flippases—structure, function, and enigmas. Frontiers in Physiology, 7, 2016. doi: 10.3389/fphys.2016.00275.K. Ekberg, B. P. Pedersen, D. M. Sprensen, A. K. Nielsen, B. Veierskov, P. Nissen, M. G. Palmgren, and M. J. Buch-Pedersen. Structural identification of catión binding pockets in the plasma membrane proton pump. Proceedings of the National Academy of Sciences, 107:21400-21405, 2010. doi: 10.1073/pnas.l010416107.D. P. de la Hera, G. Corradi, H. P. Adamo, and Felicitas de Tezanos Pinto. Parkin- son’s disease-associated human p5b-atpase atpl3a2 increases spermidine uptake. The Biochemical journal, 450 1:47-53, 2013. doi: 10.1042/BJ20120739.Xuejun C. Zhang and Hongwei Zhang. P-type atpases use a domain-association me- chanism to couple atp hydrolysis to conformational change. Biophysics Reports, pages 1-9, 2019. doi: 10.1007/S41048-019-0087-1.P. Nissen. Snapshots of p-type atpases - fronr crystal structures to single-molecule studies. Biophysical Journal, 110, 2016. doi: 10.1016/J.BPJ.2015.11.1969.D. Martin. Structure-function relationships in the na+,k+-pump. Seminars in neph- rology, 25 5:282-91, 2005. doi: 10.1016/J.SEMNEPHROL.2005.03.003.Lorena Novoa-Aponte and Carlos Yesid Soto Ospina. Mycobacterium tuberculosis p-type atpases: Possible targets for drug or vaccine development. BioMed Research. Intemational, 2014, 2014. doi: 10.1155/2014/296986.S. K. Ward, B. Abomoelak, E. A. Hoye, H. Steinberg, and A. M. Talaat. Ctpv: a putative copper exporter required for full virulence of mycobacterium tuberculosis. Molecular Microbiology, 77:1096-1110, 2010. doi: 10.1111/j. 1365-2958.2010.07273.x.Andrés León-Torres, Lorena Novoa-Aponte, and C. Soto. Ctpa, a putative mycobac terium tuberculosis p-type atpase, is stimulated by copper (i) in the mycobacterial plasma membrane. BioMetals, 28:713 - 724, 2015. doi: 10.1007/sl0534-015-9860-x.O. Shey-Njila, A. Hikal, T. Gupta, K. Sakamoto, H. Yahyaoui Azanri, W. Watford, F. Quinn, and R. Karls. Ctpb facilitates mycobacterium tuberculosis growth in copper- limited niches. Intemational Journal of Molecular Sciences, 23, 2022. doi: 10.3390/ ijms23105713.Daniel Raimunda, Jarukit E. Long, T. Padilla-Benavides, C. Sassetti, and J. Argüello. Differential roles for the co2+/ni2+ transporting atpases, ctpd and ctpj, in mycobacte-rium tuberculosis virulence. Molecular Microbiology, 91, 2014. doi: 10.1111/mmi.12454.D. Raimunda, J. E. Long, T. Padilla-Benavides, C. M. Sassetti, and J. M. Argüello. Differential roles for the co2+/ni2-|- transporting atpases, ctpd and ctpj, in myco-bacterium tuberculosis virulence. Molecular Microbiology, 91:185-197, 2013. doi: 10.1111/mmi. 12454.Liu Chen, Xiaohui Li, Piao Xu, and Zheng-Guo He. A novel zinc exporter ctpg enhances resistance to zinc toxicity and survival in mycobacterium bovis. Microbiology Spectrum, 10, 2022. doi: 10.1128/spectrunr.01456-21.Paola A. Pulido, Lorena Novoa-Aponte, Nicolás Villamil, and C. Soto. The dosr dor- rnancy regulator of mycobacterium tuberculosis stimulates the na+/k+ and ca2+ at- pase activities in plasma membrane vesicles of mycobacteria. Current Microbiology, 69:604 - 610, 2014. doi: 10.1007/s00284-014-0632-6.Milena Maya-Hoyos, D. Mata-Espinosa, M. López-Torres, Blanca Tovar-Vázquez, J. Barrios-Payán, J. León-Contreras, M. Ocampo, R. Hernández-Pando, and C. Soto. The ctpf gene encoding a calcium p-type atpase of the plasma membrane contributes to full virulence of mycobacterium tuberculosis. International Journal of Molecular Sciences, 23, 2022. doi: 10.3390/ijms23116015J. Jumper, Richard Evans, A. Pritzel, Tim Green, Michael Figurnov, O. Ronneber- ger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Amia Potapenko, Alex Bridgland, Clemens Meyer, Simón A A Kohl, Andy Ballard, A. Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, J. Adler, T. Back, Stig Petersen, D. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, S. Bodenstein, David Silver, Oriol Vinyals, A. Sénior, K. Kavuk- cuoglu, Pushmeet Kohli, and D. Hassabis. Highly accurate protein structure prediction with alphafold. Nature, 596:583 - 589, 2021. doi: 10.1038/s41586-021-03819-2.M. Dorn, Mario Inostroza-Ponta, L. Buriol, and H. Verli. A knowledge-based genetic algorithm to predict three-dimensional structures of polypeptides. 2013 IEEE Con- gress on Evolutionary Computation, pages 1233-1240, 2013. doi: 10.1109/CEC.2013. 6557706.Sung-Joon Park and M. Yamamura. Real-Coded Genetic Algorithm to Reveal Biological Significant Sites of Remotely Homologous Proteins. Springer, 2003. doi: 10.1007/ 3-540-45110-2_45.B. Mark, S. McKenna, and Mazdak Khajehpour. Protein structural analysis. Com prehensivo Biotechnology, 1:139-153, 2011. doi: 10.1016/B978-0-08-088504-9.00015-5.F. Jiang. Experimental and theoretical methods for the analysis of the spatial structure of protein. Physics, 1, 2007F. Castellani, B. Rossunr, A. Diehl, M. Schubert, Kristina Rehbein, and H. Oschkinat. Structure of a protein determined by solid-state magic-angle-spinning nmr spectros- copy. Nature, 420:98-102, 2002. doi: 10.1038/nature01070.Helen M. Berman, John Westbrook, Zukang Feng, Gary Gilliland, T. N. Bliat, Helge Weissig, Ilya N. Shindyalov, and Philip E. Bourne. The Protein Data Bank. Nucleic Acids Research, 28(l):235-242, 01 2000. ISSN 0305-1048. doi: 10.1093/nar/28.1.235. URL https: //doi . org/10.1093/nar/28.1.235.The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research, 51(D1):D523-D531, 11 2022. ISSN 0305-1048. doi: 10.1093/ nar/gkacl052. URL https://doi.org/10.1093/nar/gkacl052.E. W. Sayers, E. Bolton, J. R. Brister, K. Canese, J. Chan, D. C. Corneau, R. Connor, K. Funk, C. Kelly, S. Kim, T. Madej, A. Marchler-Bauer, C. J. Lanczycki, S. Lathrop, Z. Lu, F. Thibaud-Nissen, T. Murphy, L. Phan, Y. Skripchenko, T. Tse, J. Wang, R. Williams, B. W. Trawick, K. D. Pruitt, and S. T. Sherry. Database resources of the national center for biotechnology information. Nucleic Acids Research, 50:D20-D26, 2021. doi: 10.1093/nar/gkablll2D. J. Mead, S. Lunagómez, and D. Gatherer. Visualization of protein sequence space with force-directed graphs, and their application to the choice of target-template pairs for homology modelling. Journal of Molecular Graphics and Modelling, 92:180—191, 2019. doi: 10.1016/j.jmgm.2019.07.014D. Baker and A. Sali. Protein structure prediction and structural genomics. Science, 294:93-96, 2001. doi: 10.1126/science.l065659.B. Webb and A. Sali. Comparative protein structure modeling using modeller. Current Protocols in Bioinformatics, 54, 2016. doi: 10.1002/cpbi.3.A. Waterhouse, M. Bertoni, S. Bienert, G. Studer, G. Tauriello, R. Gumienny, F. Heer, T. d. Beer, C. Rempfer, L. Bordoli, R. Lepore, and T. Schwede. Swiss-model: homology modelling of protein structures and complexes. Nucleic Acids Research, 46:W296- W303, 2018. doi: 10.1093/nar/gky427.C. Zhou and P. Lu. de novo design of membrane transport proteins. Proteins: Structure, Function, and Bioinformatics, 90:1800-1806, 2022. doi: 10.1002/prot.26336.Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger. Colabfold: making protein folding accessible to all. Nature Methods, 19(6):679-682, Jun 2022. ISSN 1548-7105. doi: 10.1038/s41592-022-01488-l. URL https://doi.org/10.1038/s41592-022-01488-1.Carter J. Wilson, W. Choy, and M. Karttunen. Alphafold2: A role for disordered protein prediction? bioRxiv, 2021. doi: 10.1101/2021.09.27.461910.J. Jurnper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tun- yasuvunakool, R. Bates, A. Zídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reirnan, E. Clancy, M. Zieliñski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Sénior, K. Kavukcuoglu, P. Kohli, and D. Hassabis. Highly accurate protein structure prediction witli alphafold. Nature, 596:583-589, 2021. doi: 10.1038/s41586-021-03819-2.Xiaogen Zhou, Wei Zheng, Yang Li, Robín Pearce, Chengxin Zhang, Eric W. Bell. Guijun Zhang, and Yang Zhang. I-tasser-mtd: a deep-learning-based platform for multi-domain protein structure and function prediction. Nature Protocols, 17(10): 2326-2353, Oct 2022. ISSN 1750-2799. doi: 10.1038/s41596-022-00728-0. URL https: //doi.org/10.1038/s41596-022-00728-0.Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchin- nikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovan- ni, José H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christopli Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Chris- topher García, Nick V. Grishin, Paúl D. Adams, Randy J. Read, and David Baker. Accurate prediction of protein structures and interactions using a three-track neu- ral network. Science, 373(6557):871-876, 2021. doi: 10.1126/science.abj8754. URL https://www.science.org/doi/abs/10.1126/science.abj8754.Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, N. Smetanin, R. Verkuil, O. Ka- beli, Y. Shmueli, A. Costa, M. Fazel-Zar andi, T. Sercu, S. Candido, and A. Rives. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379:1123-1130, 2023. doi: 10.1126/science.ade2574.Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, Jianzhu Ma, and Jian Peng. High-resolution de novo structure prediction frorn primary sequence. bioRxiv, 2022. doi: 10.1101/2022. 07.21.500999. URL https://www.biorxiv.org/content/early/2022/07/22/2022. 07.21.500999Jin Li, Ailing Fu, and Le Zhang. An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking. Interdisciplinary Sciences: Computational Life Sciences, ll(2):320-328, 2019. ISSN 1913-2751. doi: 10.1007/ sl2539-019-00327-w.Leonardo L. G. Ferreira, Ricardo N Dos Santos, G. Oliva, and A. Andricopulo. Mole cular docking and structure-based drug design strategies. Molecules, 20:13384 - 13421, 2015. doi: 10.3390/molecules200713384.G. Mendez-Callejas, M. Piñeros-Avila, J. Yosa-Reyes, R. Pestaña-Nobles, R. Torrene- gra, M. F. Camargo-Ubate, A. E. Bello-Castro, and C. A. Celis. A novel tri-hydroxy- methylated chalcone isolated from chromolaena tacotana with anti-cancer potential targeting pro-survival proteins. International Journal of Molecular Sciences, 24:15185, 2023. doi: 10.3390/ijms242015185.B. Arjmand, Shayesteh Kokabi Hamidpour, Sepideh Alavi-Moghadam, Hanieh Yavari, Ainaz Shahbazbadr, M. Tavirani, K. Gilany, and B. Larijani. Molecular docking as a therapeutic approach for targeting cáncer stern cell metabolic processes. Frontiers in Pharmacology, 13, 2022. doi: 10.3389/fphar.2022.768556Charles Gnanaraj, M. Sekar, S. Fuloria, S. S. Swain, S. Gan, K. Chidambaram, Nur Najihah Izzati Mat Rani, T. Balan, Sarah Stephenie, Pei Teng Lum, S. Jeyabalan, M. Begum, V. Chandramohan, L. Thangavelu, V. Subramaniyan, and N. Fuloria. In silico molecular docking analysis of karanjin against alzheimer’s and parkinson’s disea- ses as a potential natural lead molecule for new drug design, development and therapy. Molecules, 27, 2022. doi: 10.3390/molecules27092834.M. Haroun, C. Tratrat, Aggeliki Kolokotroni, A. Petrou, A. Geronikaki, Marija Iva- nov, M. Kostic, M. Sokovic, A. Carazo, P. Mladénka, N. Sreeharsha, K. Venugo- pala, Anroop B Nair, and Heba S. Elsewedy. 5-benzyliden-2-(5-methylthiazol-2- ylimino)thiazolidin-4-ones as antimicrobial agents. design, synthesis, biological eva- luation and molecular docking studies. Antibiotics, 10, 2021. doi: 10.3390/ antibioticsl0030309.R. Pestaña-Nobles, J. A. Leyva-Rojas, and J. Yosa. Searching hit potential antimicro- bials in natural compounds space against biofilm formation. Molecules, 25:5334, 2020. doi: 10.3390/molecules25225334.R. Pestaña-Nobles, Y. C. A. Díaz, E. G. M. Sierra, J. Yosa, N. J. Galán-Freyle, L. X. Sepulveda-Montaño, D. G. Kuroda, and L. C. Pacheco-Londoño. Docking and mo-lecular dynamic of microalgae compounds as potential inhibitors of beta-lactamase. International Journal of Molecular Sciences, 23:1630, 2022. doi: 10.3390/ijms23031630.Israel T. Desta, Kathryn A. Porter, B. Xia, D. Kozakov, and S. Vajda. Performance and its limits in rigid body protein-protein docking. Structure, 2020. doi: 10.2139/ ssrn.3537797.Tunde Aderinwale, Charles W Christoffer, Daipayan Sarkar, Ernán Alnabati, and D. Kihara. Computational structure modeling for diverse categories of niacro- molecular interactions. Current. opinión in structural biology, 64:1-8, 2020. doi: 10.1016/j.sbi.2020.05.017.G. Klebe. Virtual Screening: An Alternative or Complement. to High Throughput Scree- ning? Springer Dordrecht, Rauischholzhausen, Germany, 2022.Chao Shen, Jun-Jie Ding, Zhe Wang, Dongsheng Cao, Xiao-Qin Ding, and Tingjun Hou. From machine learning to deep learning: Advances in scoring functions for pro- tein-ligand docking. Wiley Interdisciplinary Reviews: Computational Molecular Scien- ce, 10, 2019. doi: 10.1002/wcms.l429.J. Erickson, M. Jalaie, D. Robertson, Richard A. Lewis, and M. Vieth. Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. Journal of medicinal chemistry, 47 1:45-55, 2004. doi: 10.1021/JM030209Y.C. Barillari, Justine Taylor, R. Viner, and J. Essex. Classification of water molecules in protein binding sites. Journal of the American Chemical Society, 129 9:2577-87, 2007. doi: 10.1021/JA066980Q.Yipin Lu, Renxiao Wang, Chao-Yie Yang, and Shaomeng Wang. Analysis of ligand- bound water molecules in high-resolution crystal structures of protein-ligand com- plexes. Journal of chemical information and modeling, 47 2:668-75, 2007. doi: 10.1021/ci6003527.C. Hetényi and D. van der Spoel. Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Letters, 580, 2006. doi: 10.1016/j.febslet.2006. 01.074.W. Tian, C. Chen, L. Xue, J. Zhao, and J. Liang. Castp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research, 46W363-W367, 2018. doi: 10.1093/nar/gky473.Joel Graef, Christiane Ehrt, and Matthias Rarey. Binding site detection remastered: Enabling fast, robust, and reliable binding site detection and descriptor calculation with dogsite3. Journal of Chemical Information and Modeling, 63(10) :3128-3137, 2023. doi: 10.1021/acs.jcim.3c00336.S. A. Hollingsworth and R. Dror. Molecular dynamics simulation for all. Neuron, 99: 1129-1143, 2018. doi: 10.1016/j.neuron.2018.08.011.M. De Vivo, Matteo Masetti, G. Bottegoni, and A. Cavalli. Role of molecular dynamics and related methods in drug discovery. Journal of medicinal chemistry, 59 9:4035-61, 2016. doi: 10.1021/acs.jmedchem.5b01684.Sharon M. Loverde. Molecular simulation of the transport of drugs across model membranes. The journal of physical chemistry letters, 5 10:1659-65, 2014. doi: 10. 1021/jz500321d.S. A. Hollingsworth and R. O. Dror. Molecular dynamics simulation for all. Neuron, 99:1129-1143, 2018. doi: 10.1016/j.neuron.2018.08.011C. Tian, K. Kasavajhala, K. Belfon, L. Raguette, H. Huang, A. N. Migues, J. Bickel. Y. Wang, J. Pincay, Q. Wu, and C. Simmerling. Ffl9sb: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. Journal of Chemical Theory and Computation, 16:528-552, 2019. doi: 10.1021/acs.jctc. 9b00591.J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman, and D. A. Case. Development and testing of a general amber forcé field. Journal of Computational Chemistry, 25: 1157-1174, 2004. doi: 10.1002/jcc.20035.W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. Impey, and M. L. Klein. Compa- rison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79:926-935, 1983. doi: 10.1063/1.445869.S. Izadi, R. Anandakrishnan, and A. V. Onufriev. Building water models: a different approach. The Journal of Physical Chemistry Letters, 5:3863-3871, 2014. doi: 10.1021/ jz501780a.C. J. Dickson, R. C. Walker, and I. R. Gould. Lipid21: complex lipid membrane simulations with amber. Journal of Chemical Theory and Computation, 18:1726-1736, 2022. doi: 10.1021/acs.jctc.lc01217M. Abraham, T. J. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hess, and E. Lindahl. Gromacs: high performance molecular simulations through multi-level parallelism frorn laptops to supercomputers. SoftwareX, 1-2:19-25, 2015. doi: 10.1016/j.softx.2015.06. 001.D. v. d. Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark, and H. J. C. Berendsen. Gromacs: fast, flexible, and free. Journal of Computational Chemistry, 26:1701-1718, 2005. doi: 10.1002/jcc.20291.D.A. Case, H.M. Aktulga, K. Belfon, I.Y. Ben-Shalom, J.T. Berryman, S.R. Bro- zell, D.S. Cerutti, T.E. Cheatham, III, G.A. Cisneros, V.W.D. Cruzeiro, T.A. Darden, N. Forouzesh, G. Giamba§u, T. Giese, M.K. Gilson, H. Gohlke, A.W. Goetz, J. Ha- rris, S. Izadi, S.A. Izmailov, K. Kasavajhala, M.C. Kaymak, E. King, A. Kovalenko, T. Kurtzman, T.S. Lee, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, M. Machado, V. Man, M. Manathunga, K.M. Merz, Y. Miao, O. Mikhailovskii, G. Monard, H. Nguyen, K.A. O’Hearn, A. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, D.R. Roe, A. Roit- berg, C. Sagui, S. Schott-Verdugo, A. Shajan, J. Shen, C.L. Simmerling, N.R. Skryn- nikov, J. Smith, J. Swails, R.C. Walker, J. Wang, J. Wang, H. Wei, X. Wu, Y. Wu, Y. Xiong, Y. Xue, D.M. York, S. Zhao, Q. Zhu, , and P.A. Kollman. AMBER 2022. University of California, San Francisco, CA, 2022James C. Phillips, David J. Hardy, Julio D. C. Maia, John E. Stone, Joño V. Ribeiro, Rafael C. Bernardi, Ronak Buch, Giacomo Fiorin, Jéróme Hénin, Wei Jiang, Ryan McGreevy, Marcelo C. R. Meló, Brian K. Radak, Robert D. Skeel, Abhishek Singharoy, Yi Wang, Benoít Roux, Aleksei Aksinrentiev, Zaida Luthey-Schulten, Laxmikant V. Kalé, Klaus Schulten, Christophe Chipot, and Emad Tajkhorshid. Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of Chemical Physics, 153(4):044130, 07 2020. ISSN 0021-9606. doi: 10.1063/5.0014475. URL https: //doi.org/10.1063/5.0014475.P. Eastman, J. M. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L. Wang, A. C. Simmonett, M. P. Harrigan, C. Stern, R. Wiewiora, B. R. Brooks, and V. S. Pande. Openmm 7: rapid development of higli performance algorithms for molecular dynamics. PLOS Computational Biology, 13:el005659, 2017. doi: 10.1371/ journal.pcbi. 1005659.G. Bhabha, J. Biel, and J. S. Fraser. Keep on moving: discovering and perturbing the conformational dynamics of enzymes. Accounts of Chemical Research, 48:423-430, 2014. doi: 10.1021/ar5003158.Y. Xu and M. Havenith. Perspective: watching low-frequency vibrations of water in biomolecular recognition by thz spectroscopy. The Journal of Chemical Physics, 143, 2015. doi: 10.1063/1.4934504.A. Srivastava, T. Nagai, A. Srivastava, O. Miyashita, and F. Tama. Role of compu tational methods in going beyond x-ray crystallography to explore protein structu- re and dynamics. International Journal of Molecular Sciences, 19:3401, 2018. doi: 10.3390/ijnrsl9113401.M. A. González. Forcé fields and molecular dynamics simulations. Ecole Thémati- que De La Société Franyaise De La Neutronique, 12:169-200, 2011. doi: 10.1051/sfn/ 201112009.J. D. Durrant and J. A. McCammon. Molecular dynamics simulations and drug dis- covery. BMC Biology, 9, 2011. doi: 10.1186/1741-7007-9-71.Y. I. Yang, Qiang Shao, Jun Zhang, Lijiang Yang, and Y. Gao. Enhanced sampling in molecular dynamics. The Journal of chemical physics, 151 7:070902, 2019. doi: 10.1063/1.5109531.Pu Liu, Byungchan Kinr, R. Friesner, and B. Berne. Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proceedings of the National Academy of Sciences of the United States of América, 102 39:13749-54, 2005. doi: 10.1073/PNAS.0506346102.J. Kástner. Umbrella sampling. Wiley Interdisciplinary Reviews: Computational Mo lecular Science, 1, 2011. doi: 10.1002/wcms.66.A. v. d. Vaart and M. Karplus. Simulation of conformational transitions by the res- tricted perturbation-targeted molecular dynamics method. The Journal of Chemical Physics, 122, 2005. doi: 10.1063/1.1861885.Y. Miao, V. A. Feher, and J. A. McCammon. Gaussian accelerated molecular dynamics: unconstrained enhanced sampling and free energy calculation. Journal of Chemical Theory and Computation, 11:3584-3595, 2015. doi: 10.1021/acs.jctc.5b00436.D. Hamelberg, J. Marcus, and J. A. McCammon. Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. The Journal of Chemical Physics, 120:11919-11929, 2004. doi: 10.1063/1.1755656.Yui Tik Pang, Y. Miao, Yi Wang, and J. Mccammon. Gaussian accelerated molecular dynamics in namd. Journal of Chemical Theory and Computation, 13:9 - 19, 2016. doi: 10.1021/acs.jctc.6b00931.Bill R. Miller, T. D. McGee, J. Swails, Nadine Homeyer, H. Gohlke, and A. Roitberg. Mmpbsa.py: An efficient program for end-state free energy calculations. Journal of chemical theory and computation, 8 9:3314-21, 2012. doi: 10.1021/ct300418h.R. Kumari, R. Kumar, and A. Lynn. g _mmpbsa - a gromacs tool for high-throughput nnn-pbsa calculations. Journal of chemical information and modeling, 54 7:1951-62, 2014. doi: 10.1021/ci500020m.Ido Y. Ben-Shalom, Stefania Pfeiffer-Marek, Karl-Heinz Baringhaus, and Holger Gohl ke. Efficient approximation of ligand rotational and translational entropy changes upon binding for use in nnn-pbsa calculations. Journal of Chemical Information and Modeling, 57(2): 170-189, 2017. doi: 10.1021/acs.jcinr.6b00373Sanruel Genheden and Ulf Ryde. Comparison of the efficiency of the lie and mm/gbsa methods to calcúlate ligand-binding energies. Journal of Chemical Theory and Compu-tation, 7(ll):3768-3778, Nov 2011. ISSN 1549-9618. doi: 10.1021/ct200163c.Tingjun Hou, Junmei Wang, Youyong Li, and Wei Wang. Assessing the performance of the nnn/pbsa and mm/gbsa methods. 1. the accuracy of binding free energy cal-culations based on molecular dynamics simulations. Journal of chemical information and modeling, 51(1):69—82, Jan 2011. ISSN 1549-960X. doi: 10.1021/cil00275a.Ministerio de Salud. Resolución 8430 de 1993. https://www.minsalud.gov.co/ sites/rid/Lists/BibliotecaDigital/RIDE/DE/DIJ/RES0LUCI0N-8430-DE-1993. PDF. 1993. Accedido 10-01-2024.M. M. Lopera. Connnented review of the colombian legislation regarding the ethics of health research. Biomédica, 37:577, 2017. doi: 10.7705/biomedica.v37i4.3333.Andrea Mauri. alvaDesc: A Tool to Calcúlate and Analyze Molecular Descriptors and Fingerprints, pages 801-820. Springer US, New York, NY, 2020. ISBN 978-1-0716- 0150-1. doi: 10.1007/978-1-0716-0150-1_32.Andrea Mauri and Matteo Bertola. Alvascience: A new software suite for the qsar workflow applied to the blood-brain barrier permeability. International Journal of Molecular Sciences, 23(21), 2022. ISSN 1422-0067. doi: 10.3390/ijms232112882.Noel M. O’Boyle, Michael Banck, Craig A. James, Chris Morley, Tim Vandermeersch, and Geoffrey R. Hutchison. Open babel: An open chemical toolbox. Journal of Che- minformatics, 3(1):33, Oct 2011. ISSN 1758-2946. doi: 10.1186/1758-2946-3-33.Yuejiang Yu, Chun Cai, Jiayue Wang, Zonghua Bo, Zhengdan Zhu, and Hang Zheng. Uni-dock: Gpu-accelerated docking enables ultralarge virtual screening. Journal of Chemical Theory and Computation, 19(ll):3336-3345, 2023. doi: 10.1021/acs.jctc. 2c01145.Shidi Tang, Ruiqi Chen, Mengru Lin, Qingde Lin, Yanxiang Zhu, Ji Ding, Haifeng Hu, Ming Ling, and Jiansheng Wu. Accelerating autodock vina with gpus. Molecules, 27(9), 2022. ISSN 1420-3049. doi: 10.3390/molecules27093041. URL https://www. mdpi.com/1420-3049/27/9/3041.Jerome Eberhardt, Diogo Santos-Martins, Andreas F. Tillack, and Stefano Forli. Autodock vina 1.2.0: New docking methods, expanded forcé field, and python bin- dings. Journal of Chemical Information and Modeling, 61(8):3891-3898, 2021. doi: 10.1021/acs.jcim. lc00203.Oleg Trott and Arthur J. Olson. Autodock vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2):455-461, 2010. doi: https://doi.org/10. 1002/jcc.21334. URL https : //onlinelibrary. wiley. com/doi/abs/10.1002/j cc. 21334.Cresset Group. Fiare 7.2.0. https://www.cresset-group.com/software/flare/, 2022. Accedido 14-02-2020.I. A. Guedes, A. Barreto, D. Marinho, E. Krempser, M. A. Kuenemann, O. Spéran- dio, L. E. Dardenne, and M. A. Miteva. New machine learning and physics-based scoring functions for drug discovery. Scientific Reports, 11, 2021. doi: 10.1038/ s41598-021-82410-1.K. B. Santos, I. A. Guedes, A. L. M. Karl, and L. E. Dardenne. Highly flexible ligand docking: benchmarking of the dockthor program on the leads-pep protein-peptide data set. Journal of Chemical Information and Modeling, 60:667-683, 2020. doi: 10.1021/ acs.jcim. 9b00905.C. S. d. Magalháes, D. M. Almeida, H. J. C. Barbosa, and L. E. Dardenne. A dynamic niching genetic algorithm strategy for docking higlily flexible ligands. Information Sciences, 289:206-224, 2014. doi: 10.1016/j.ins.2014.08.002I. A. Guedes, L. S. C. Costa, K. B. Santos, A. L. M. Karl, G. K. Rocha, I. M. Teixeira, M. Galheigo, V. Medeiros, E. Krempser, F. L. Custodio, H. J. C. Barbosa, M. F. Nicolás, and L. E. Dardenne. Drug design and repurposing with dockthor-vs web server focusing on sars-cov-2 therapeutic targets and their non-synonym variants. Scientific Reports, 11, 2021. doi: 10.1038/s41598-021-84700-0.Karen Palacio-Rodríguez, Isaías Lans, Claudio N. Cavasotto, and Pilar Cossio. Ex- ponential consensus ranking improves the outcome in docking and receptor ensemble docking. Scientific Reports, 9(1):5142, 2019. doi: 10.1038/s41598-019-41594-3.Sunhwan Jo, Taehoon Kim, Vidyashankara G. Iyer, and Wonpil Im. Charmm-gui: A web-based graphical user interface for charmm. Journal of Computational Chemistry, 29(11): 1859-1865, 2008. doi: https://doi.org/10.1002/jcc.20945Mikhail A. Lomize, Irina D. Pogozheva, Hyeon Joo, Henry I. Mosberg, and Andrei L. Lomize. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Research, 40(Dl):D370-D376, 09 2011. ISSN 0305-1048. doi: 10.1093/nar/gkr703.Yinglong Miao, Victoria A. Feher, and J. Andrew McCammon. Gaussian accelerated molecular dynamics: Unconstrained enhanced sampling and free energy calculation. Journal of Chemical Theory and Computation, ll(8):3584-3595, 2015. doi: 10.1021/ acs.jctc.5b00436.Hung N. Do, Jinan Wang, Apurba Bhattarai, and Yinglong Miao. Glow: A workflow integrating gaussian-accelerated molecular dynamics and deep learning for free energy profiling. Journal of Chemical Theory and Computation, 18(3): 1423-1436, 2022. doi: 10.1021/acs.jctc.lc01055B. R. Miller, T. D. McGee, J. M. Swails, N. Homeyer, H. Gohlke, and A. E. Roitberg. MMPBSA.py: An efficient program for end-state free energy calculations. Journal of Chemical Theory and Computation, 8(9):3314-3321, 2012. ISSN 15499626. doi: 10.1021/ct300418h.D. A. Case, H. M. Aktulga, K. Belfon, D. S. Cerutti, G. A. Cisneros, V. W. D. Cruzei ro, N. Forouzesh, T. J. Giese, A. W. Gótz, H. Gohlke, S. Izadi, K. Kasavajhala, M. C. Kaymak, E. King, T. Kurtzman, T. Lee, P. Li, J. Liu, T. Luchko, R. Luo, M. Ma- nathunga, M. R. Machado, H. M. Nguyen, K. A. O’Hearn, A. V. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, A. Risheh, S. Schott-Verdugo, A. Shajan, J. Swails, J. Wang, H. Wei, X. Wu, Y. Wu, S. Zhang, S. Zhao, Q. Zhu, T. E. Cheatham, D. R. Roe, A. Roitberg, C. Simmerling, D. M. York, M. C. Nagan, and K. M. Merz. Arn- bertools. Journal of Chemical Information and Modeling, 63:6183-6191, 2023. doi: 10.1021/acs.jcim.3c01153.L. Xiao, J. Diao, D. Greene, J. Wang, and R. Luo. A continuum poisson-boltzmann model for membrane channel proteins. Journal of Chemical Theory and Computation, 13:3398-3412, 2017. doi: 10.1021/acs.jctc.7b00382.Ramon Guixá-González, Ismael Rodríguez-Espigares, Juan Manuel Ramírez-Anguita, Pau Carrió-Gaspar, Héctor Martinez-Seara, Toni Giorgino, and Jana Selent. MEMB- PLUGIN: studying membrane complexity in VMD. Bioinformatics, 30(10): 1478-1480, 01 2014. ISSN 1367-4803. doi: 10.1093/bioinformatics/btu037William Humphrey, Andrew Dalke, and Klaus Schulten. VMD - Visual Molecular Dynamics. Journal of Molecular Graphics, 14:33-38, 1996. 222. D. R. Roe and T. E. Cheatham. Ptraj and cpptraj: software for processing and analysis of molecular dynamics trajectory data. Journal of Chemical Theory and Computation, 9:3084-3095, 2013. doi: 10.1021/ct400341p.B. J. Grant, A. P. Rodrigues, K. M. ElSawy, J. A. McCammon, and L. S. D. Caves. Bio3d: an r package for the comparative analysis of protein structures. Bioinformatics, 22:2695-2696, 2006. doi: 10.1093/bioinformatics/btl461.B. J. Grant, L. Skjaerven, and X. Yao. The bio3d packages for structural bioinformatics. Protein Science, 30:20-30, 2020. doi: 10.1002/pro.3923.Cédric Bouysset and Sébastien Fiorucci. Prolif: a library to encode molecular in- teractions as fingerprints. Journal of Cheminformatics, 13(1):72, Sep 2021. ISSN 1758-2946. doi: 10.1186/sl3321-021-00548-6. URL https://doi.org/10.1186/ S13321-021-00548-6.Elaine C. Meng, Thornas D. Goddard, Eric F. Pettersen, Greg S. Couch, Zach J. Pearson, John H. Morris, and Thornas E. Ferrin. Ucsf chimerax: Tools for structure building and analysis. Protein Science, 32(ll):e4792, 2023. doi: https://doi.org/10. 1002/pro.4792.S. Raza, K. E. Ranaghan, M. W. v. d. Karnp, C. Woods, A. J. Mulholland, and S. S. Azam. Visualizing protein-ligand binding with chemical energy-wise decomposition (chewd): application to ligand binding in the kallikrein-8 si site. Journal of Computer- Aided Molecular Design, 33:461-475, 2019. doi: 10.1007/sl0822-019-00200-4.E. F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt, E. C. Meng, and T. E. Ferrin. Ucsf chimera—a visualization systern for exploratory research and analysis. Journal of Computational Chemistry, 25:1605-1612, 2004. doi: 10.1002/ jcc.20084.K. Moncoq, C. A. Trieber, and H. S. Young. The molecular basis for cyclopiazonic acid inhibition of the sarcoplasmic reticulum calcium purnp. Journal of Biologícal Chemistry, 282:9748-9757, 2007. doi: 10.1074/jbc.m611653200.L. Jalili-Baleh, H. Nadri, A. Moradi, S. N. A. Bukhari, M. Shakibaie, M. Jafari, M. Golshani, F. H. Moghadam, L. Firoozpour, A. Asadipour, S. Emami, M. Khoobi, and A. Foroumadi. New racemic annulated pyrazolo[l,2-b]phthalazines as tacrine-like ache inhibitors with potential use in alzheimer’s disease. European Journal of Medicinal Chemistry, 139:280-289, 2017. doi: 10.1016/j.ejmech.2017.07.072.National Center for Biotechnology Information. Pubchem compound summary for cid 145964448. https://pubchem.ncbi.nlm.nih.gov/compound/145964448, 2024. Accedido 7-02-2024.G. L. Ellis, R. K. Amewu, S. Sabbani, P. A. Stocks, A. E. Shone, D. Stanford, P. Gib- bons, J. Davies, L. Vivas, S. C. Charnaud, E. Bongard, C. R. Hall, K. Rimmer, S. Lo- zanom, M. Jesús, D. Gargallo, S. A. Ward, and P. M. O’Neill. Two-step synthesis of achiral dispiro-l,2,4,5-tetraoxanes with outstanding antimalarial activity, low toxicity, and high-stability profiles. Journal of Medicinal Chemistru, 51:2170-2177, 2008. doi: 10.1021/jm701435h.National Center for Biotechnology Information. Pubchem compound summary for cid 24827780, morpholine urea 1,2,4,5-tetraoxane. https://pubchem.ncbi.nlm.nih. gov/compound/24827780, 2024. Accedido 7-02-2024.National Center for Biotechnology Information. Pubchem compound summary for cid 382777, 2,2’-spirobi[2h-benz[e]indene]-l,r(3’h,3h)-dione, 6,6’,7,7’,8,8’,9,9’-octahydro- . https://pubchem.ncbi.nlm.nih.gov/compound/58546922, 2024. Accedido 7-02- 2024.A. Zagórska. Phosphodiesterase 10 (pdelO) inhibitors: an updated patent review (2014- present). Expert Opinión on Therapeutic Patents, 30:147-157, 2019. doi: 10.1080/ 13543776.2020.1709444.National Center for Biotechnology Information. Pubchem bioassay record for aid 1800922, source: Bindingdb. https://pubchem.ncbi.nlm.nih.gov/bioassay/ 1800922., 2024. Accedido 7-02-2024.P. Coll. Fármacos con actividad frente a mycobacterium tuberculosis. Enfermedades Infecciosas Y Microbiología Clínica, 27:474-480, 2009. doi: 10.1016/j.eimc.2009.06.010.Castiblanco A. César and Polo L. Claudia. Tuberculosis en Colombia: análisis de la situación epidemiológica, año 2006. infectio, 12(3), 2008.Sistema Nacional de Vigilancia en Salud Pública. Reportes enfermedades transmicibles. https://portalsivigila.ins.gov.co/Paginas/datos.aspx?cod=93, 2022. Accedido 09- 02-2024.Gobernación del Atlántico. Plan de acción 2023 - atlántico líder. https://www.atlántico.gov.co/índex.php/plan-de-accion-64894/ 23082-plan-de-accion2024-atlantico-para-la-gente, 2017. Accedido 8- 02-2024.Sede BarranquillaDoctorado en Genética y Biología MolecularORIGINALPDF.pdfPDF.pdfapplication/pdf18235975https://bonga.unisimon.edu.co/bitstreams/a09a6b0d-0109-46c2-ae28-2f0b4268fd3f/downloadbbbbdff4f345ff56d1ad58b1a71cddb4MD51PDF_Resumen.pdfPDF_Resumen.pdfapplication/pdf507735https://bonga.unisimon.edu.co/bitstreams/50a46bd7-d215-4e5a-b6cc-92e19475c667/download6555d31d7f54d6bdace4f8d607777014MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83000https://bonga.unisimon.edu.co/bitstreams/29c7ab45-b110-4fa8-bf11-796343a8e26b/download2a1661e5960a7bab4fd8dda692fb677cMD53TEXTPDF.txtPDF.txtExtracted texttext/plain101496https://bonga.unisimon.edu.co/bitstreams/41074cab-ba44-495e-b2c1-0db81fd2caa2/download579e9e830f3211bc8f0a47a873362ebdMD54PDF_RESUMEN.txtPDF_RESUMEN.txtExtracted texttext/plain73843https://bonga.unisimon.edu.co/bitstreams/f906e7b1-e6c3-415d-adea-0c36049d2cf8/download59cb140b944511a3bc3b53581de6f96cMD56PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain101496https://bonga.unisimon.edu.co/bitstreams/63071435-5935-48b5-bf83-1c93c481b498/download579e9e830f3211bc8f0a47a873362ebdMD58PDF_Resumen.pdf.txtPDF_Resumen.pdf.txtExtracted texttext/plain73843https://bonga.unisimon.edu.co/bitstreams/5b9ffe34-e008-42c9-9006-c507d34b907d/download59cb140b944511a3bc3b53581de6f96cMD510THUMBNAILPDF.jpgPDF.jpgGenerated Thumbnailimage/jpeg4246https://bonga.unisimon.edu.co/bitstreams/bdcd0fb7-c2a2-4e35-8861-92f15fc9a95d/download4302212dc34ead2fee3c0f04caaee480MD55PDF_RESUMEN.jpgPDF_RESUMEN.jpgGenerated Thumbnailimage/jpeg5452https://bonga.unisimon.edu.co/bitstreams/5b1bd5bc-8373-452b-9ba2-2f4f7f367fc1/download424b6094e5232b5002512952a830ad6cMD57PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg4246https://bonga.unisimon.edu.co/bitstreams/24ef2b49-4d36-4e7e-91a5-88d5eca48b11/download4302212dc34ead2fee3c0f04caaee480MD59PDF_Resumen.pdf.jpgPDF_Resumen.pdf.jpgGenerated Thumbnailimage/jpeg5452https://bonga.unisimon.edu.co/bitstreams/e506da2d-2f69-4c76-9fa3-1facc38c5f21/download424b6094e5232b5002512952a830ad6cMD51120.500.12442/14584oai:bonga.unisimon.edu.co:20.500.12442/145842024-08-14 21:53:09.298embargo2027-05-06https://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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