Resumen
Durante casi un año, la raza humana se ha visto sacudida por la aparición de un nuevo coronavirus conocido como SARS-CoV-2, el cual se originó en China y desde allí se ha dispersado por casi todas las regiones alrededor del globo. La incidencia y mortalidad de este peligroso virus, que se han vuelto a incrementar recientemente en países como Colombia, ha puesto en alarma los diferentes sistemas de salud pública y ha generado enormes dificultades y desafíos a nivel económico, social y político. Con el objetivo de encontrar rápidamente alternativas de tratamiento, de forma paralela a las iniciativas de prevención planteadas por la llegada de una vacuna efectiva, la biología computacional y las tecnologías asistidas por computadora han entrado en evidente auge, posicionándose vertiginosamente en la industria farmacéutica a nivel internacional. En este artículo se presentan los principales hallazgos de un estudio dentro de esta categoría enfocado en diseñar y descubrir nuevos agentes con capacidad inhibitoria contra el nuevo coronavirus, describiendo la estrategia metodológica utilizada y planteando las implicaciones, retos y perspectivas a futuro asociadas a encontrar una molécula capaz de exhibir un potencial terapéutico prometedor.
Citas
“OMS | Nuevo coronavirus - China,” WHO, 2020.
“Naming the coronavirus disease (COVID-19) and the virus that causes it.” .
“Alocución de apertura del Director General de la OMS en la rueda de prensa sobre la COVID-19 celebrada el 11 de marzo de 2020.” .
E.Callaway, H.Ledford, and S.Mallapaty, “Six months of coronavirus: the mysteries scientists are still racing to solve,” Nature, vol. 583, no. 7815, pp. 178–179, 2020.
E.Rodríguez Mega, “Latin American scientists join the coronavirus vaccine race: ‘No one’s coming to rescue us,’” Nature, vol. 582, no. 7813, pp. 470–471, 2020.
F.Prieto-Martínez and J.Medina-Franco, “Computer-aided drug design: when informatics, chemistry and art meets,” TIP. Rev. Espec. en ciencias químico-biológicas, vol. 21, no. 2, pp. 124–134, 2018.
Z.Jinet al., “Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors,” Nature, vol. 582, no. 7811, pp. 289–293, 2020.
S. R.Langdon, J.Blagg, and N.Brown, “Scaffold diversity in medicinal chemistry space,” in Scaffold Hopping in Medicinal Chemistry, 2013, pp. 39–60.
J.Gu, Y.Gui, L.Chen, G.Yuan, H.Lu, and X.Xu, “Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology,” vol. 8, no. 4, pp. 1–10, 2013.
B. A. P.Wilson, C. C.Thornburg, C. J.Henrich, T.Grkovic, and B. R.O’Keefe, “Creating and screening natural product libraries,” Nat. Prod. Rep., 2020.
S. R. 2019-3, “Maestro, Schrödinger, LLC, New York, NY, 2019.”
S. S.Ou-Yang, J. Y.Lu, X. Q.Kong, Z. J.Liang, C.Luo, and H.Jiang, “Computational drug discovery.,” Acta Pharmacol. Sin., vol. 33, no. 9, pp. 1131–40, 2012.
D. B.Kitchen, H.Decornez, J. R.Furr, and J.Bajorath, “Docking and scoring in virtual screening for drug discovery: methods and applications.,” Nat. Rev. Drug Discov., vol. 3, no. 11, pp. 935–49, Nov. 2004.
A. C.Anderson, R. H. O.Neil, T. S.Surti, and R. M.Stroud, “Approaches to solving the rigid receptor problem by identifying a minimal set of £ exible residues during ligand docking 1,” vol. 8, 2001.
J. H.Jensen, Molecular Modeling Basics, vol. 53, no. 9. 2013.
S.Genheden and U.Ryde, “The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities,” Expert Opin. Drug Discov., vol. 10, no. 5, pp. 449–461, 2015.
T. A.Binkowski, S.Naghibzadeh, and J.Liang, “CASTp: Computed Atlas of Surface Topography of proteins,” Nucleic Acids Res., vol. 31, no. 13, pp. 3352–3355, 2003.
J.Dundas, Z.Ouyang, J.Tseng, A.Binkowski, Y.Turpaz, and J.Liang, “CASTp: Computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues,” Nucleic Acids Res., vol. 34, no. WEB. SERV. ISS., pp. 116–118, 2006.
M.Karaman, “Pharmacophore Analyses of SARS-CoV-2 Active Main Protease Inhibitors Using Pharmacophore Query and Docking Study,” Jun. 2020.
M. J.Abrahamet al., “Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers,” SoftwareX, vol. 1–2, pp. 19–25, 2015.
D.Van Der Spoel, E.Lindahl, B.Hess, G.Groenhof, A. E.Mark, and H. J. C.Berendsen, “GROMACS: Fast, flexible, and free,” J. Comput. Chem., vol. 26, no. 16, pp. 1701–1718, 2005.
S.Saberi Fathi and J. A.Tuszynski, “A simple method for finding a protein’s ligand-binding pockets,” BMC Struct. Biol., vol. 14, no. 1, p. 18, 2014.
G.Wolber and T.Langer, “LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters,” J. Chem. Inf. Model., vol. 45, no. 1, pp. 160–169, 2005.
G.Wolber, “LigandScout Automated Structure-Based Pharmacophore Model Generation Pharmacophores from LigandScout,” Spring, vol. 2005, 2005.
M. P. A.Sanderset al., “From the protein’s perspective: the benefits and challenges of protein structure-based pharmacophore modeling,” Med. Chem. Commun., vol. 3, no. 1, pp. 28–38, 2012.
I.Wallach, “Pharmacophore inference and its application to computational drug discovery,” Drug Dev. Res., vol. 72, no. 1, pp. 17–25, 2011.