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González-Paz L, Lossada C, Hurtado-León ML, Vera-Villalobos J, Paz JL, Marrero-Ponce Y, Martinez-Rios F, Alvarado Y. Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data. ACS OMEGA 2024; 9:8923-8939. [PMID: 38434903 PMCID: PMC10905729 DOI: 10.1021/acsomega.3c06968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/20/2024] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
Recent reports have suggested that the susceptibility of cells to SARS-CoV-2 infection can be influenced by various proteins that potentially act as receptors for the virus. To investigate this further, we conducted simulations of viral dynamics using different cellular systems (Vero E6, HeLa, HEK293, and CaLu3) in the presence and absence of drugs (anthelmintic, ARBs, anticoagulant, serine protease inhibitor, antimalarials, and NSAID) that have been shown to impact cellular recognition by the spike protein based on experimental data. Our simulations revealed that the susceptibility of the simulated cell systems to SARS-CoV-2 infection was similar across all tested systems. Notably, CaLu3 cells exhibited the highest susceptibility to SARS-CoV-2 infection, potentially due to the presence of receptors other than ACE2, which may account for a significant portion of the observed susceptibility. Throughout the study, all tested compounds showed thermodynamically favorable and stable binding to the spike protein. Among the tested compounds, the anticoagulant nafamostat demonstrated the most favorable characteristics in terms of thermodynamics, kinetics, theoretical antiviral activity, and potential safety (toxicity) in relation to SARS-CoV-2 spike protein-mediated infections in the tested cell lines. This study provides mathematical and bioinformatic models that can aid in the identification of optimal cell lines for compound evaluation and detection, particularly in studies focused on repurposed drugs and their mechanisms of action. It is important to note that these observations should be experimentally validated, and this research is expected to inspire future quantitative experiments.
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Affiliation(s)
- Lenin González-Paz
- Centro
de Biomedicina Molecular (CBM). Laboratorio de Biocomputación
(LB),Instituto Venezolano de Investigaciones
Científicas (IVIC),Maracaibo, Zulia 4001, República Bolivariana de Venezuela
| | - Carla Lossada
- Centro
de Biomedicina Molecular (CBM). Laboratorio de Biocomputación
(LB),Instituto Venezolano de Investigaciones
Científicas (IVIC),Maracaibo, Zulia 4001, República Bolivariana de Venezuela
| | - María Laura Hurtado-León
- Facultad
Experimental de Ciencias (FEC). Departamento de Biología. Laboratorio
de Genética y Biología Molecular (LGBM),Universidad del Zulia (LUZ),Maracaibo 4001, República Bolivariana de Venezuela
| | - Joan Vera-Villalobos
- Facultad
de Ciencias Naturales y Matemáticas, Departamento de Química
y Ciencias Ambientales, Laboratorio de Análisis Químico
Instrumental (LAQUINS), Escuela Superior
Politécnica del Litoral, Guayaquil EC090112, Ecuador
| | - José L. Paz
- Departamento
Académico de Química Inorgánica, Facultad de
Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos. Cercado de Lima, Lima 15081, Perú
| | - Yovani Marrero-Ponce
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias
de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades
Médicas; e Instituto de Simulación Computacional (ISC-USFQ),
Diego de Robles y vía Interoceánica, Universidad San Francisco de Quito (USFQ), Quito, Pichincha 170157, Ecuador
| | - Felix Martinez-Rios
- Universidad
Panamericana. Facultad de Ingeniería. Augusto Rodin 498, Ciudad de México 03920, México
| | - Ysaías.
J. Alvarado
- Centro
de Biomedicina Molecular (CBM). Laboratorio de Química Biofísica
Teórica y Experimental (LQBTE),Instituto
Venezolano de Investigaciones Científicas (IVIC),Maracaibo, Zulia 4001, República Bolivariana
de Venezuela
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Comparative study of SARS-CoV-2 infection in different cell types: Biophysical-computational approach to the role of potential receptors. Comput Biol Med 2022; 142:105245. [PMID: 35077937 PMCID: PMC8770263 DOI: 10.1016/j.compbiomed.2022.105245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/07/2022] [Accepted: 01/16/2022] [Indexed: 12/17/2022]
Abstract
Cellular susceptibility to SARS-CoV-2 infection in the respiratory tract has been associated with the ability of the virus to interact with potential receptors on the host membrane. We have modeled viral dynamics by simulating various cellular systems and artificial conditions, including macromolecular crowding, based on experimental and transcriptomic data to infer parameters associated with viral growth and predict cell susceptibility. We have accomplished this based on the type, number and level of expression of the angiotensin-converting enzyme 2 (ACE2), transmembrane serine 2 (TMPRSS2), basigin2 (CD147), FURIN protease, neuropilin 1 (NRP1) or other less studied candidate receptors such as heat shock protein A5 (HSPA5) and angiotensin II receptor type 2 (AGTR2). In parallel, we studied the effect of simulated artificial environments on the accessibility to said proposed receptors. In addition, viral kinetic behavior dependent on the degree of cellular susceptibility was predicted. The latter was observed to be more influenced by the type of proteins and expression level, than by the number of potential proteins associated with the SARS CoV-2 infection. We predict a greater theoretical propensity to susceptibility in cell lines such as NTERA-2, SCLC-21H, HepG2 and Vero6, and a lower theoretical propensity in lines such as CaLu3, RT4, HEK293, A549 and U-251MG. An important relationship was observed between expression levels, protein diffusivity, and thermodynamically favorable interactions between host proteins and the viral spike, suggesting potential sites of early infection other than the lungs. This research is expected to stimulate future quantitative experiments and promote systematic investigation of the effect of crowding presented here.
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Parra-Rojas C, Hernandez-Vargas EA. PDEparams: parameter fitting toolbox for partial differential equations in python. Bioinformatics 2020; 36:2618-2619. [PMID: 31851311 DOI: 10.1093/bioinformatics/btz938] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 09/12/2019] [Accepted: 12/16/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Partial differential equations (PDEs) is a well-established and powerful tool to simulate multi-cellular biological systems. However, available free tools for validation against data are on development. RESULTS The PDEparams module provides a flexible interface and readily accommodates different parameter analysis tools in PDE models such as computation of likelihood profiles, and parametric bootstrapping, along with direct visualization of the results. To our knowledge, it is the first open, freely available tool for parameter fitting of PDE models. AVAILABILITY AND IMPLEMENTATION PDEparams is distributed under the MIT license. The source code, usage instructions and examples are freely available on GitHub at github.com/systemsmedicine/PDE_params. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- César Parra-Rojas
- Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
| | - Esteban A Hernandez-Vargas
- Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany.,Instituto de Matemáticas, UNAM, Unidad Juriquilla, 76230 Queretaro, Mexico
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