1
|
Fomina AD, Uvarova VI, Kozlovskaya LI, Palyulin VA, Osolodkin DI, Ishmukhametov AA. Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors. Mol Inform 2024:e202300279. [PMID: 38973780 DOI: 10.1002/minf.202300279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/21/2024] [Accepted: 03/03/2024] [Indexed: 07/09/2024]
Abstract
During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.
Collapse
Affiliation(s)
- Anastasia D Fomina
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Department of Chemistry, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Victoria I Uvarova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
| | - Liubov I Kozlovskaya
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, 119991, Moscow, Russia
| | - Vladimir A Palyulin
- Department of Chemistry, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Dmitry I Osolodkin
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, 119991, Moscow, Russia
| | - Aydar A Ishmukhametov
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, 119991, Moscow, Russia
| |
Collapse
|
2
|
Lee D, Jung HG, Park D, Bang J, Cheong DY, Jang JW, Kim Y, Lee S, Lee SW, Lee G, Kim YH, Hong JH, Hwang KS, Lee JH, Yoon DS. Bioengineered amyloid peptide for rapid screening of inhibitors against main protease of SARS-CoV-2. Nat Commun 2024; 15:2108. [PMID: 38453923 PMCID: PMC10920794 DOI: 10.1038/s41467-024-46296-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 02/22/2024] [Indexed: 03/09/2024] Open
Abstract
The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has evoked a worldwide pandemic. As the emergence of variants has hampered the neutralization capacity of currently available vaccines, developing effective antiviral therapeutics against SARS-CoV-2 and its variants becomes a significant challenge. The main protease (Mpro) of SARS-CoV-2 has received increased attention as an attractive pharmaceutical target because of its pivotal role in viral replication and proliferation. Here, we generated a de novo Mpro-inhibitor screening platform to evaluate the efficacies of Mpro inhibitors based on Mpro cleavage site-embedded amyloid peptide (MCAP)-coated gold nanoparticles (MCAP-AuNPs). We fabricated MCAPs comprising an amyloid-forming sequence and Mpro-cleavage sequence, mimicking in vivo viral replication process mediated by Mpro. By measuring the proteolytic activity of Mpro and the inhibitory efficacies of various drugs, we confirmed that the MCAP-AuNP-based platform was suitable for rapid screening potential of Mpro inhibitors. These results demonstrated that our MCAP-AuNP-based platform has great potential for discovering Mpro inhibitors and may accelerate the development of therapeutics against COVID-19.
Collapse
Affiliation(s)
- Dongtak Lee
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Center for Nanomedicine, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Hyo Gi Jung
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea
| | - Dongsung Park
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, Seoul, 02447, South Korea
| | - Junho Bang
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea
| | - Da Yeon Cheong
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, South Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, South Korea
| | - Jae Won Jang
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea
| | - Yonghwan Kim
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea
| | - Seungmin Lee
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Department of Electrical Engineering, Kwangwoon University, Seoul, 01897, South Korea
| | - Sang Won Lee
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, USA
| | - Gyudo Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, South Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, 30019, South Korea
| | - Yeon Ho Kim
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea
| | - Ji Hye Hong
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea
- Department of Electrical Engineering, Kwangwoon University, Seoul, 01897, South Korea
| | - Kyo Seon Hwang
- Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, Seoul, 02447, South Korea.
| | - Jeong Hoon Lee
- Department of Electrical Engineering, Kwangwoon University, Seoul, 01897, South Korea.
| | - Dae Sung Yoon
- School of Biomedical Engineering, Korea University, Seoul, 02841, South Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, 02841, South Korea.
- Astrion Inc, Seoul, 02841, South Korea.
| |
Collapse
|
3
|
Roy S, Bhattacharya S. An in silico approach to evaluate the bindings of natural flavonoids and RNA-DNA hybrids. J Biomol Struct Dyn 2023:1-8. [PMID: 37922129 DOI: 10.1080/07391102.2023.2275184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/20/2023] [Indexed: 11/05/2023]
Abstract
Flavonoids, low molecular weight polyphenolic compounds, are important natural products that belong to plant secondary metabolites. They have diverse biomedical applications such as antioxidative, anti-inflammatory, enzyme inhibitory, antimutagenic, anticarcinogenic, aromatase inhibitory effects, etc. Some of the flavonoids have been exported for bindings with certain DNA and tRNA structures both experimentally and computationally. RNA-DNA hybrid (RDH) falls into an important category of noncanonical nucleic acid structures that have many important biological functions. We have investigated the interaction of RDH structures with some of the dietary flavonoids with the aid of computational methods such as docking and molecular dynamics simulation. The presence of the - OH group on the ligand and the availability of a proper binding pocket in the macromolecule are the two main factors driving the binding preference. Thus, this computationally guided report explains the binding of the flavonoids with RDH structures to assist the researchers in designing noncanonical nucleic acid-targeted drug molecules.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Soma Roy
- School of Applied and Interdisciplinary Sciences, Indian Association for the Cultivation of Science, Kolkata, India
| | - Santanu Bhattacharya
- School of Applied and Interdisciplinary Sciences, Indian Association for the Cultivation of Science, Kolkata, India
- Department of Organic Chemistry, Indian Institute of Science, Bangalore, India
- Department of Chemistry, Indian Institute of Science, Education & Research, Tirupati, India
| |
Collapse
|
4
|
Sivangi KB, Amilpur S, Dasari CM. ReGen-DTI: A novel generative drug target interaction model for predicting potential drug candidates against SARS-COV2. Comput Biol Chem 2023; 106:107927. [PMID: 37499436 DOI: 10.1016/j.compbiolchem.2023.107927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023]
Abstract
Covid-19 has caused massive numbers of infections and fatalities globally. In response, there has been a large-scale experimental and computational research effort to study and develop drugs. Towards this, Deep learning techniques are used for the generation of potential novel drug candidates that are proven to be effective against exploring large molecular search spaces. Recent advances in reinforcement learning in conjunction with generative techniques has proven to be a promising field in the area of drug discovery. In this regard, we propose a generative drug discovery approach using reinforcement techniques for sampling novel molecules that bind to the main protease of SARS-COV2. The generative method reported significant validity scores for the generated novel molecules and captured the underlying features of the training molecules. Further, the model is fine-tuned on existing re-purposed molecules which are active towards specific target proteins based on similarity metrics. Upon fine tuning the model generated 92.71% valid, 93.55% unique, and 100% novel molecules. Unlike previous methods which are dependent on docking procedures, we proposed a deep learning based novel drug target interaction (DTI) model to find the binding affinity between candidate molecules and target protease sequence. Finally, the binding affinity of the generated molecules is predicted against the 3CLPro main protease by using the proposed DTI model. Most of the generated molecules have shown binding affinity scores <100 nM (lower the better), which are significantly better compared to the existing commercial drugs including Remdesevir.
Collapse
Affiliation(s)
- Kaushik Bhargav Sivangi
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India
| | - Santhosh Amilpur
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India
| | - Chandra Mohan Dasari
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India.
| |
Collapse
|
5
|
Ruatta SM, Prada Gori DN, Fló Díaz M, Lorenzelli F, Perelmuter K, Alberca LN, Bellera CL, Medeiros A, López GV, Ingold M, Porcal W, Dibello E, Ihnatenko I, Kunick C, Incerti M, Luzardo M, Colobbio M, Ramos JC, Manta E, Minini L, Lavaggi ML, Hernández P, Šarlauskas J, Huerta García CS, Castillo R, Hernández-Campos A, Ribaudo G, Zagotto G, Carlucci R, Medrán NS, Labadie GR, Martinez-Amezaga M, Delpiccolo CML, Mata EG, Scarone L, Posada L, Serra G, Calogeropoulou T, Prousis K, Detsi A, Cabrera M, Alvarez G, Aicardo A, Araújo V, Chavarría C, Mašič LP, Gantner ME, Llanos MA, Rodríguez S, Gavernet L, Park S, Heo J, Lee H, Paul Park KH, Bollati-Fogolín M, Pritsch O, Shum D, Talevi A, Comini MA. Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro. Front Pharmacol 2023; 14:1193282. [PMID: 37426813 PMCID: PMC10323144 DOI: 10.3389/fphar.2023.1193282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/31/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.
Collapse
Affiliation(s)
- Santiago M. Ruatta
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Montevideo, Uruguay
- Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Santa Fe, Argentina
| | - Denis N. Prada Gori
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
| | - Martín Fló Díaz
- Laboratory of Immunovirology, Institut Pasteur de Montevideo, Montevideo, Uruguay
- Departamento de Inmunobiología, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Franca Lorenzelli
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Montevideo, Uruguay
| | - Karen Perelmuter
- Cell Biology Unit, Institut Pasteur de Montevideo, Montevideo, Uruguay
| | - Lucas N. Alberca
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Carolina L. Bellera
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Andrea Medeiros
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Montevideo, Uruguay
- Departamento de Bioquímica, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Gloria V. López
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
- Vascular Biology and Drug Discovery Lab, Institut Pasteur de Montevideo, Montevideo, Uruguay
| | - Mariana Ingold
- Vascular Biology and Drug Discovery Lab, Institut Pasteur de Montevideo, Montevideo, Uruguay
| | - Williams Porcal
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
- Vascular Biology and Drug Discovery Lab, Institut Pasteur de Montevideo, Montevideo, Uruguay
| | - Estefanía Dibello
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Irina Ihnatenko
- PVZ—Center of Pharmaceutical Engineering, Institute of Medicinal and Pharmaceutical Chemistry, Technische Universität Braunschweig, Braunschweig, Germany
| | - Conrad Kunick
- PVZ—Center of Pharmaceutical Engineering, Institute of Medicinal and Pharmaceutical Chemistry, Technische Universität Braunschweig, Braunschweig, Germany
| | - Marcelo Incerti
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Martín Luzardo
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Maximiliano Colobbio
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
- Laboratorio de Química Fina, Facultad de Química, Instituto Polo Tecnológico de Pando, Universidad de la República, Montevideo, Uruguay
| | - Juan Carlos Ramos
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
- Laboratorio de Química Fina, Facultad de Química, Instituto Polo Tecnológico de Pando, Universidad de la República, Montevideo, Uruguay
| | - Eduardo Manta
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
- Laboratorio de Química Fina, Facultad de Química, Instituto Polo Tecnológico de Pando, Universidad de la República, Montevideo, Uruguay
| | - Lucía Minini
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - María Laura Lavaggi
- Laboratorio de Química Biológica Ambiental, Sede Rivera, Centro Universitario Regional Noreste, Universidad de la República, Montevideo, Uruguay
| | - Paola Hernández
- Departamento de Genética, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay
| | - Jonas Šarlauskas
- Life Sciences Centre, Department of Xenobiotic Biochemistry, Institute of Biochemistry, Vilnius University, Vilnius, Lithuania
| | | | - Rafael Castillo
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico
| | - Alicia Hernández-Campos
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico
| | - Giovanni Ribaudo
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Giuseppe Zagotto
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Renzo Carlucci
- Facultad de Ciencias Bioquímicas y Farmacéuticas, Instituto de Química Rosario (IQUIR) UNR, CONICET, Universidad Nacional de Rosario, Rosario, Argentina
| | - Noelia S. Medrán
- Facultad de Ciencias Bioquímicas y Farmacéuticas, Instituto de Química Rosario (IQUIR) UNR, CONICET, Universidad Nacional de Rosario, Rosario, Argentina
| | - Guillermo R. Labadie
- Facultad de Ciencias Bioquímicas y Farmacéuticas, Instituto de Química Rosario (IQUIR) UNR, CONICET, Universidad Nacional de Rosario, Rosario, Argentina
| | - Maitena Martinez-Amezaga
- Facultad de Ciencias Bioquímicas y Farmacéuticas, Instituto de Química Rosario (IQUIR) UNR, CONICET, Universidad Nacional de Rosario, Rosario, Argentina
| | - Carina M. L. Delpiccolo
- Facultad de Ciencias Bioquímicas y Farmacéuticas, Instituto de Química Rosario (IQUIR) UNR, CONICET, Universidad Nacional de Rosario, Rosario, Argentina
| | - Ernesto G. Mata
- Facultad de Ciencias Bioquímicas y Farmacéuticas, Instituto de Química Rosario (IQUIR) UNR, CONICET, Universidad Nacional de Rosario, Rosario, Argentina
| | - Laura Scarone
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Laura Posada
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | - Gloria Serra
- Departamento de Química Orgánica, Facultad de Química, Universidad de la República, Montevideo, Uruguay
| | | | - Kyriakos Prousis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Anastasia Detsi
- Laboratory of Organic Chemistry, School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - Mauricio Cabrera
- Laboratorio de Moléculas Bioactivas, Departamento de Ciencias Biológicas, CENUR Litoral Norte, Universidad de la República, Paysandú, Uruguay
| | - Guzmán Alvarez
- Laboratorio de Moléculas Bioactivas, Departamento de Ciencias Biológicas, CENUR Litoral Norte, Universidad de la República, Paysandú, Uruguay
| | - Adrián Aicardo
- Departamento de Bioquímica, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
- Centro de Investigaciones Biomédicas (CEINBIO), Universidad de la República, Montevideo, Uruguay
- Departamento de Nutrición Clínica, Escuela de Nutrición, Universidad de la República, Montevideo, Uruguay
| | - Verena Araújo
- Departamento de Bioquímica, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
- Centro de Investigaciones Biomédicas (CEINBIO), Universidad de la República, Montevideo, Uruguay
- Departamento de Alimentos, Escuela de Nutrición, Universidad de la República, Montevideo, Uruguay
| | - Cecilia Chavarría
- Departamento de Bioquímica, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
- Centro de Investigaciones Biomédicas (CEINBIO), Universidad de la República, Montevideo, Uruguay
| | | | - Melisa E. Gantner
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Manuel A. Llanos
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Santiago Rodríguez
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
| | - Luciana Gavernet
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Soonju Park
- Screening Discovery Platform, Institut Pasteur Korea, Seongnam, Republic of Korea
| | - Jinyeong Heo
- Screening Discovery Platform, Institut Pasteur Korea, Seongnam, Republic of Korea
| | - Honggun Lee
- Screening Discovery Platform, Institut Pasteur Korea, Seongnam, Republic of Korea
| | - Kyu-Ho Paul Park
- Screening Discovery Platform, Institut Pasteur Korea, Seongnam, Republic of Korea
| | | | - Otto Pritsch
- Laboratory of Immunovirology, Institut Pasteur de Montevideo, Montevideo, Uruguay
- Departamento de Inmunobiología, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - David Shum
- Screening Discovery Platform, Institut Pasteur Korea, Seongnam, Republic of Korea
| | - Alan Talevi
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Marcelo A. Comini
- Laboratory Redox Biology of Trypanosomes, Institut Pasteur de Montevideo, Montevideo, Uruguay
| |
Collapse
|
6
|
Ajmal A, Mahmood A, Hayat C, Hakami MA, Alotaibi BS, Umair M, Abdalla AN, Li P, He P, Wadood A, Hu J. Computer-assisted drug repurposing for thymidylate kinase drug target in monkeypox virus. Front Cell Infect Microbiol 2023; 13:1159389. [PMID: 37313340 PMCID: PMC10258308 DOI: 10.3389/fcimb.2023.1159389] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 05/10/2023] [Indexed: 06/15/2023] Open
Abstract
Introduction Monkeypox is a zoonotic disease caused by brick-shaped enveloped monkeypox (Mpox) virus that belongs to the family of ancient viruses known as Poxviridae. Subsequently, the viruses have been reported in various countries. The virus is transmitted by respiratory droplets, skin lesions, and infected body fluids. The infected patients experience fluid-filled blisters, maculopapular rash, myalgia, and fever. Due to the lack of effective drugs or vaccines, there is a need to identify the most potent and effective drugs to reduce the spread of monkeypox. The current study aimed to use computational methods to quickly identify potentially effective drugs against the Mpox virus. Methods In our study, the Mpox protein thymidylate kinase (A48R) was targeted because it is a unique drug target. We screened a library of 9000 FDA-approved compounds of the DrugBank database by using various in silico approaches, such as molecular docking and molecular dynamic (MD) simulation. Results Based on docking score and interaction analysis, compounds DB12380, DB13276, DB13276, DB11740, DB14675, DB11978, DB08526, DB06573, DB15796, DB08223, DB11736, DB16250, and DB16335 were predicted as the most potent. To examine the dynamic behavior and stability of the docked complexes, three compounds-DB16335, DB15796, and DB16250 -along with the Apo state were simulated for 300ns. The results revealed that compound DB16335 revealed the best docking score (-9.57 kcal/mol) against the Mpox protein thymidylate kinase. Discussion Additionally, during the 300 ns MD simulation period, thymidylate kinase DB16335 showed great stability. Further, in vitro and in vivo study is recommended for the final predicted compounds.
Collapse
Affiliation(s)
- Amar Ajmal
- Department of Biochemistry, Computational Medicinal Chemistry Laboratory, Abdul Wali Khan University, Mardan, Pakistan
| | - Arif Mahmood
- Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Chandni Hayat
- Department of Biochemistry, Computational Medicinal Chemistry Laboratory, Abdul Wali Khan University, Mardan, Pakistan
| | - Mohammed Ageeli Hakami
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al-Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Bader S. Alotaibi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al-Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Muhammad Umair
- Department of Life Sciences, School of Science, University of Management and Technology (UMT), Lahore, Pakistan
| | - Ashraf N. Abdalla
- Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Ping Li
- Institute of Biomedical Sciences, Shanxi University, Taiyuan, China
| | - Pei He
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Abdul Wadood
- Department of Biochemistry, Computational Medicinal Chemistry Laboratory, Abdul Wali Khan University, Mardan, Pakistan
| | - Junjian Hu
- Department of Central Laboratory, SSL Central Hospital of Dongguan City, Affiliated Dongguan Shilong People’s Hospital of Southern Medical University, Dongguan, China
| |
Collapse
|
7
|
Hirlekar BU, Nuthi A, Singh KD, Murty US, Dixit VA. An overview of compound properties, multiparameter optimization, and computational drug design methods for PARP-1 inhibitor drugs. Eur J Med Chem 2023; 252:115300. [PMID: 36989813 DOI: 10.1016/j.ejmech.2023.115300] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 03/29/2023]
Abstract
Breast cancer treatment with PARP-1 inhibitors remains challenging due to emerging toxicities, drug resistance, and unaffordable costs of treatment options. How do we invent strategies to design better anti-cancer drugs? A part of the answer is in optimized compound properties, desirability functions, and modern computational drug design methods that drive selectivity and toxicity and have not been reviewed for PARP-1 inhibitors. Nonetheless, comparisons of these compound properties for PARP-1 inhibitors are not available in the literature. In this review, we analyze the physchem, PKPD space to identify inherent desirability functions characteristic of approved drugs that can be valuable for the design of better candidates. Recent literature utilizing ligand, structure-based drug design strategies and matched molecular pair analysis (MMPA) for the discovery of novel PARP-1 inhibitors are also reviewed. Thus, this perspective provides valuable insights into the medchem and multiparameter optimization of PARP-1 inhibitors that might be useful to other medicinal chemists.
Collapse
|
8
|
Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int J Mol Sci 2023; 24:ijms24054401. [PMID: 36901832 PMCID: PMC10003049 DOI: 10.3390/ijms24054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.
Collapse
|
9
|
Eilts F, Bauer S, Fraser K, Dordick JS, Wolff MW, Linhardt RJ, Zhang F. The diverse role of heparan sulfate and other GAGs in SARS-CoV-2 infections and therapeutics. Carbohydr Polym 2023; 299:120167. [PMID: 36876764 PMCID: PMC9516881 DOI: 10.1016/j.carbpol.2022.120167] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 11/25/2022]
Abstract
In December 2019, the global coronavirus disease 2019 (COVID-19) pandemic began in Wuhan, China. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which infects host cells primarily through the angiotensin-converting enzyme 2 (ACE2) receptor. In addition to ACE2, several studies have shown the importance of heparan sulfate (HS) on the host cell surface as a co-receptor for SARS-CoV-2-binding. This insight has driven research into antiviral therapies, aimed at inhibiting the HS co-receptor-binding, e.g., by glycosaminoglycans (GAGs), a family of sulfated polysaccharides that includes HS. Several GAGs, such as heparin (a highly sulfated analog of HS), are used to treat various health indications, including COVID-19. This review is focused on current research on the involvement of HS in SARS-CoV-2 infection, implications of viral mutations, as well as the use of GAGs and other sulfated polysaccharides as antiviral agents.
Collapse
Affiliation(s)
- Friederike Eilts
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA; Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied Sciences Mittelhessen (THM), Giessen, Germany
| | - Sarah Bauer
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Keith Fraser
- Department of Biological Sciences, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Jonathan S Dordick
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Biological Sciences, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Michael W Wolff
- Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied Sciences Mittelhessen (THM), Giessen, Germany; Fraunhofer Institute for Molecular Biology and Applied Ecology (IME), Giessen, Germany
| | - Robert J Linhardt
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Biological Sciences, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.
| | - Fuming Zhang
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA.
| |
Collapse
|
10
|
Budipramana K, Sangande F. Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity. PHARMACIA 2022. [DOI: 10.3897/pharmacia.69.e89812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.
Collapse
|
11
|
Ahmed F, Gi Ho S, Samantasinghar A, Memon FH, Rahim CSA, Soomro AM, Pratibha, Sunildutt N, Kim KH, Choi KH. Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles. Comput Struct Biotechnol J 2022; 20:6097-6107. [DOI: 10.1016/j.csbj.2022.10.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/09/2022] [Accepted: 10/30/2022] [Indexed: 11/10/2022] Open
|
12
|
Manish M, Mishra S, Anand A, Subbarao N. Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics. Comput Biol Med 2022; 150:106125. [PMID: 36240593 PMCID: PMC9507791 DOI: 10.1016/j.compbiomed.2022.106125] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 09/10/2022] [Accepted: 09/18/2022] [Indexed: 12/04/2022]
Abstract
Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.
Collapse
Affiliation(s)
- Manish Manish
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
| | - Smriti Mishra
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
| | - Ayush Anand
- BP Koirala Institute of Health Sciences, Dharan, Nepal.
| | - Naidu Subbarao
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
| |
Collapse
|
13
|
Kumar S, Ayyannan SR. Identification of new small molecule monoamine oxidase-B inhibitors through pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation studies. J Biomol Struct Dyn 2022:1-22. [PMID: 35983603 DOI: 10.1080/07391102.2022.2112082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
The discovery of a safe and efficacious drug is a complex, time-consuming, and expensive process. Computational methodologies driven by cheminformatics tools play a central role in the high-throughput lead discovery and optimization process especially when the structure of the biological target is known. Monoamine oxidases are the membrane-bound FAD-containing enzymes and the isoform monoamine oxidase-B (MAO-B) is an attractive target for treating diseases like Alzheimer's disease, Parkinson's disease, glioma, etc. In the current study, we have used a pharmacophore-based virtual screening technique for the identification of new small molecule MAO-B inhibitors. Safinamide was used for building a pharmacophore model and the developed model was used to probe the ZINC database for potential hits. The obtained hits were filtered against drug-likeness and PAINS. Out of the hit's library, two compounds ZINC02181408, ZINC08853942 (most active), and ZINC53327382 (least active) were further subjected to molecular docking and dynamics simulation studies to assess their virtual binding affinities and stability of the resultant protein-ligand complex. The docking studies revealed that active ligands were well accommodated within the active site of MAO-B and interacted with both substrate and entrance cavity residues. MD simulation studies unveiled additional hydrogen bond interactions with the substrate cavity residues, Tyr398 and Tyr435 that are crucial for the catalytic role of MAO-B. Moreover, the predicted ADMET parameters suggest that the compounds ZINC08853942 and ZINC02181408 are suitable for CNS penetration. Thus, the attempted computational campaign yielded two potential MAO-B inhibitors that merit further experimental investigation.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Sandeep Kumar
- Pharmaceutical Chemistry Research Laboratory II, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, India
| | - Senthil Raja Ayyannan
- Pharmaceutical Chemistry Research Laboratory II, Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, India
| |
Collapse
|
14
|
Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
Collapse
Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
| |
Collapse
|
15
|
Deodato D, Asad N, Dore TM. Discovery of 2-Thiobenzimidazoles as Noncovalent Inhibitors of SARS-CoV-2 Main Protease. Bioorg Med Chem Lett 2022; 72:128867. [PMID: 35760254 PMCID: PMC9225965 DOI: 10.1016/j.bmcl.2022.128867] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/03/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022]
Abstract
The discovery of antiviral agents against SARS-CoV-2 is an important step toward ending the COVID-19 pandemic and to tackle future outbreaks. In this context, the main protease (Mpro) represents an ideal target for developing coronavirus antivirals, being conserved among different strains and essential for survival. In this work, using in silico tools, we created and validated a docking protocol able to predict binders to the catalytic site of Mpro. The following structure-based virtual screening of a subset of the ZINC library (over 4.3 million unique structures), led to the identification of a hit compound having a 2-thiobenzimidazole scaffold. The inhibitory activity was confirmed using a FRET-based proteolytic assay against recombinant Mpro. Structure-activity relationships were obtained with the synthesis of a small library of analogs, guided by the analysis of the docking pose. Our efforts led to the identification of a micromolar Mpro inhibitor (IC50 = 14.9 µM) with an original scaffold possessing ideal drug-like properties (predicted using the QikProp function) and representing a promising lead for the development of a novel class of coronavirus antivirals.
Collapse
Affiliation(s)
- Davide Deodato
- New York University Abu Dhabi, PO Box 129188, Abu Dhabi, United Arab Emirates
| | - Nadeem Asad
- New York University Abu Dhabi, PO Box 129188, Abu Dhabi, United Arab Emirates
| | - Timothy M Dore
- New York University Abu Dhabi, PO Box 129188, Abu Dhabi, United Arab Emirates; Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| |
Collapse
|
16
|
Design, Semisynthesis, and Estrogenic Activity of Lignan Derivatives from Natural Dibenzylbutyrolactones. Pharmaceuticals (Basel) 2022; 15:ph15050585. [PMID: 35631411 PMCID: PMC9145393 DOI: 10.3390/ph15050585] [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: 03/23/2022] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 12/25/2022] Open
Abstract
Based on molecular docking studies on the ERα, a series of lignan derivatives (3–16) were designed and semisynthesized from the natural dibenzylbutyrolactones bursehernin (1) and matairesinol dimethyl ether (2). To examine their estrogenic and antiestrogenic potencies, the effects of these compounds on estrogen receptor element (ERE)-driven reporter gene expression and viability in human ER+ breast cancer cells were evaluated. Lignan compounds induced ERE-driven reporter gene expression with very low potency as compared with the pure agonist E2. However, coincubation of 5 μM of lignan derivatives 1, 3, 4, 7, 8, 9, 11, 13, and 14 with increasing concentrations of E2 (from 0.01 pM to 1 nM) reduced both the potency and efficacy of pure agonists. The binding to the rhERα-LBD was validated by TR-FRET competitive binding assay and lignans bound to the rhERα with IC50 values from 0.16 μM (compound 14) to 6 μM (compound 4). Induced fit docking (IFD) and molecular dynamics (MD) simulations for compound 14 were carried out to further investigate the binding mode interactions. Finally, the in silico ADME predictions indicated that the most potent lignan derivatives exhibited good drug-likeness.
Collapse
|
17
|
Finding a chink in the armor: Update, limitations, and challenges toward successful antivirals against flaviviruses. PLoS Negl Trop Dis 2022; 16:e0010291. [PMID: 35482672 PMCID: PMC9049358 DOI: 10.1371/journal.pntd.0010291] [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] [Indexed: 12/15/2022] Open
Abstract
Flaviviruses have caused large epidemics and ongoing outbreaks for centuries. They are now distributed in every continent infecting up to millions of people annually and may emerge to cause future epidemics. Some of the viruses from this group cause severe illnesses ranging from hemorrhagic to neurological manifestations. Despite decades of research, there are currently no approved antiviral drugs against flaviviruses, urging for new strategies and antiviral targets. In recent years, integrated omics data-based drug repurposing paired with novel drug validation methodologies and appropriate animal models has substantially aided in the discovery of new antiviral medicines. Here, we aim to review the latest progress in the development of both new and repurposed (i) direct-acting antivirals; (ii) host-targeting antivirals; and (iii) multitarget antivirals against flaviviruses, which have been evaluated both in vitro and in vivo, with an emphasis on their targets and mechanisms. The search yielded 37 compounds that have been evaluated for their efficacy against flaviviruses in animal models; 20 of them are repurposed drugs, and the majority of them exhibit broad-spectrum antiviral activity. The review also highlighted the major limitations and challenges faced in the current in vitro and in vivo evaluations that hamper the development of successful antiviral drugs for flaviviruses. We provided an analysis of what can be learned from some of the approved antiviral drugs as well as drugs that failed clinical trials. Potent in vitro and in vivo antiviral efficacy alone does not warrant successful antiviral drugs; current gaps in studies need to be addressed to improve efficacy and safety in clinical trials.
Collapse
|
18
|
Piplani S, Singh P, Petrovsky N, Winkler DA. Computational Repurposing of Drugs and Natural Products Against SARS-CoV-2 Main Protease (Mpro) as Potential COVID-19 Therapies. Front Mol Biosci 2022; 9:781039. [PMID: 35359601 PMCID: PMC8964187 DOI: 10.3389/fmolb.2022.781039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/28/2022] [Indexed: 12/18/2022] Open
Abstract
We urgently need to identify drugs to treat patients suffering from COVID-19 infection. Drugs rarely act at single molecular targets. Off-target effects are responsible for undesirable side effects and beneficial synergy between targets for specific illnesses. They have provided blockbuster drugs, e.g., Viagra for erectile dysfunction and Minoxidil for male pattern baldness. Existing drugs, those in clinical trials, and approved natural products constitute a rich resource of therapeutic agents that can be quickly repurposed, as they have already been assessed for safety in man. A key question is how to screen such compounds rapidly and efficiently for activity against new pandemic pathogens such as SARS-CoV-2. Here, we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-CoV-2. We show that the shortlist of 84 candidates with the strongest predicted binding affinities is highly enriched (≥25%) in compounds experimentally validated in vivo or in vitro to have activity in SARS-CoV-2. The top candidates also include drugs and natural products not previously identified as having COVID-19 activity, thereby providing leads for experimental validation. This predictive in silico screening pipeline will be valuable for repurposing existing drugs and discovering new drug candidates against other medically important pathogens relevant to future pandemics.
Collapse
Affiliation(s)
- Sakshi Piplani
- College of Medicine and Public Health, Flinders University, Bedford, SA, Australia
- Vaxine Pty Ltd., Warradale, SA, Australia
| | - Puneet Singh
- College of Medicine and Public Health, Flinders University, Bedford, SA, Australia
- Vaxine Pty Ltd., Warradale, SA, Australia
| | - Nikolai Petrovsky
- College of Medicine and Public Health, Flinders University, Bedford, SA, Australia
- Vaxine Pty Ltd., Warradale, SA, Australia
- *Correspondence: Nikolai Petrovsky, ; David A. Winkler,
| | - David A. Winkler
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
- School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
- *Correspondence: Nikolai Petrovsky, ; David A. Winkler,
| |
Collapse
|
19
|
Gaudêncio SP, Pereira F. Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach. Mar Drugs 2022; 20:md20020129. [PMID: 35200658 PMCID: PMC8879326 DOI: 10.3390/md20020129] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/28/2022] [Accepted: 02/06/2022] [Indexed: 11/19/2022] Open
Abstract
Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure–activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar’s website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.
Collapse
Affiliation(s)
- Susana P. Gaudêncio
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal;
- UCIBIO—Applied Molecular Biosciences Unit, Department of Chemistry, Blue Biotechnology and Biomedicine Lab, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Florbela Pereira
- LAQV, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
- Correspondence:
| |
Collapse
|
20
|
DasGupta D, Chan WKB, Carlson HA. Computational Identification of Possible Allosteric Sites and Modulators of the SARS-CoV-2 Main Protease. J Chem Inf Model 2022; 62:618-626. [PMID: 35107014 DOI: 10.1021/acs.jcim.1c01223] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In this study, we target the main protease (Mpro) of the SARS-CoV-2 virus as it is a crucial enzyme for viral replication. Herein, we report three plausible allosteric sites on Mpro that can expand structure-based drug discovery efforts for new Mpro inhibitors. To find these sites, we used mixed-solvent molecular dynamics (MixMD) simulations, an efficient computational protocol that finds binding hotspots through mapping the surface of unbound proteins with 5% cosolvents in water. We have used normal mode analysis to support our claim of allosteric control for these sites. Further, we have performed virtual screening against the sites with 361 hits from Mpro screenings available through the National Center for Advancing Translational Sciences (NCATS). We have identified the NCATS inhibitors that bind to the remote sites better than the active site of Mpro, and we propose these molecules may be allosteric regulators of the system. After identifying our sites, new X-ray crystal structures were released that show fragment molecules in the sites we found, supporting the notion that these sites are accurate and druggable.
Collapse
Affiliation(s)
- Debarati DasGupta
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1065, United States
| | - Wallace K B Chan
- Department of Pharmacology, University of Michigan, Ann Arbor, Michigan 48109-5632, United States
| | - Heather A Carlson
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1065, United States
| |
Collapse
|
21
|
Bucinsky L, Bortňák D, Gall M, Matúška J, Milata V, Pitoňák M, Štekláč M, Végh D, Zajaček D. Machine learning prediction of 3CLpro SARS-CoV-2 docking scores. Comput Biol Chem 2022; 98:107656. [PMID: 35288359 PMCID: PMC8881816 DOI: 10.1016/j.compbiolchem.2022.107656] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/14/2022]
Abstract
Molecular docking results of two training sets containing 866 and 8,696 compounds were used to train three different machine learning (ML) approaches. Neural network approaches according to Keras and TensorFlow libraries and the gradient boosted decision trees approach of XGBoost were used with DScribe’s Smooth Overlap of Atomic Positions molecular descriptors. In addition, neural networks using the SchNetPack library and descriptors were used. The ML performance was tested on three different sets, including compounds for future organic synthesis. The final evaluation of the ML predicted docking scores was based on the ZINC in vivo set, from which 1,200 compounds were randomly selected with respect to their size. The results obtained showed a consistent ML prediction capability of docking scores, and even though compounds with more than 60 atoms were found slightly overestimated they remain valid for a subsequent evaluation of their drug repurposing suitability.
Collapse
|
22
|
Discovery of C-12 dithiocarbamate andrographolide analogues as inhibitors of SARS-CoV-2 main protease: In vitro and in silico studies. Comput Struct Biotechnol J 2022; 20:2784-2797. [PMID: 35677603 PMCID: PMC9167041 DOI: 10.1016/j.csbj.2022.05.053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/27/2022] [Accepted: 05/27/2022] [Indexed: 11/27/2022] Open
Abstract
Andrographolide analogues were found to inhibit SARS-CoV-2 main protease. The compounds 3k, 3l, 3m and 3t showed promising in vitro inhibitory activity. Most of the candidates could bind well to the SARS-CoV-2 main protease active site.
A global crisis of coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has impacted millions of people’s lives throughout the world. In parallel to vaccine development, identifying potential antiviral agents against SARS-CoV-2 has become an urgent need to combat COVID-19. One of the most attractive drug targets for discovering anti-SARS-CoV-2 agents is the main protease (Mpro), which plays a pivotal role in the viral life cycle. This study aimed to elucidate a series of twenty-one 12-dithiocarbamate-14-deoxyandrographolide analogues as SARS-CoV-2 Mpro inhibitors using in vitro and in silico studies. These compounds were initially screened for the inhibitory activity toward SARS-CoV-2 Mpro by in vitro enzyme-based assay. We found that compounds 3k, 3l, 3m and 3t showed promising inhibitory activity against SARS-CoV-2 Mpro with >50% inhibition at 10 μM. Afterward, the binding mode of each compound in the active site of SARS-CoV-2 Mpro was explored by molecular docking. The optimum docked complexes were then chosen and subjected to molecular dynamic (MD) simulations. The MD results suggested that all studied complexes were stable along the simulation time, and most of the compounds could fit well with the SARS-CoV-2 Mpro active site, particularly at S1, S2 and S4 subsites. The per-residue decomposition free energy calculations indicated that the hot-spot residues essential for ligand binding were T25, H41, C44, S46, M49, C145, H163, M165, E166, L167, D187, R188, Q189 and T190. Therefore, the obtained information from the combined experimental and computational techniques could lead to further optimization of more specific and potent andrographolide analogues toward SARS-CoV-2 Mpro.
Collapse
|
23
|
Zhao J, Ma Q, Zhang B, Guo P, Wang Z, Liu Y, Meng M, Liu A, Yang Z, Du G. Exploration of SARS-CoV-2 3CL pro Inhibitors by Virtual Screening Methods, FRET Detection, and CPE Assay. J Chem Inf Model 2021; 61:5763-5773. [PMID: 34797660 PMCID: PMC8631171 DOI: 10.1021/acs.jcim.1c01089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Indexed: 01/28/2023]
Abstract
COVID-19 caused by a novel coronavirus (SARS-CoV-2) has been spreading all over the world since the end of 2019, and no specific drug has been developed yet. 3C-like protease (3CLpro) acts as an important part of the replication of novel coronavirus and is a promising target for the development of anticoronavirus drugs. In this paper, eight machine learning models were constructed using naïve Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular fingerprints. The optimal models were selected according to the results of 5-fold cross verification, test set verification, and external test set verification. A total of 5766 natural compounds from the internal natural product database were predicted, among which 369 chemical components were predicted to be active compounds by the optimal models and the EstPGood values were more than 0.6, as predicted by the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were selected for further biological activity determination by the fluorescence resonance energy transfer (FRET) method and cytopathic effect (CPE) detection. The results indicated that (+)-shikonin, shikonin, scutellarein, and 5,3',4'-trihydroxyflavone showed certain activity in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory concentration (IC50) values ranging from 4.38 to 87.76 μM. In the CPE assay, 5,3',4'-trihydroxyflavone showed a certain antiviral effect with an IC50 value of 8.22 μM. The binding mechanism of 5,3',4'-trihydroxyflavone with SARS-CoV-2 3CLpro was further revealed through CDOCKER analysis. In this study, 3CLpro prediction models were constructed based on machine learning algorithms for the prediction of active compounds, and the activity of potential inhibitors was determined by the FRET method and CPE assay, which provide important information for further discovery and development of antinovel coronavirus drugs.
Collapse
Affiliation(s)
- Jun Zhao
- Beijing Key Lab of Drug Target Identification and Drug
Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences
& Peking Union Medical College, Beijing 100050,
China
| | - Qinhai Ma
- State Key Laboratory of Respiratory Disease, National
Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory
Health, The First Affiliated Hospital of Guangzhou Medical
University, Guangzhou, Guangdong 510000, China
| | - Baoyue Zhang
- Beijing Key Lab of Drug Target Identification and Drug
Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences
& Peking Union Medical College, Beijing 100050,
China
| | - Pengfei Guo
- Beijing Key Lab of Drug Target Identification and Drug
Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences
& Peking Union Medical College, Beijing 100050,
China
| | - Zhe Wang
- Beijing Key Lab of Drug Target Identification and Drug
Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences
& Peking Union Medical College, Beijing 100050,
China
| | - Yi Liu
- College of Chemical Engineering, Sichuan
University of Science & Engineering, 519 Huixing Road, Zigong, Sichuan
643000, China
| | - Minsi Meng
- College of Chemical Engineering, Sichuan
University of Science & Engineering, 519 Huixing Road, Zigong, Sichuan
643000, China
| | - Ailin Liu
- Beijing Key Lab of Drug Target Identification and Drug
Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences
& Peking Union Medical College, Beijing 100050,
China
| | - Zifeng Yang
- State Key Laboratory of Respiratory Disease, National
Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory
Health, The First Affiliated Hospital of Guangzhou Medical
University, Guangzhou, Guangdong 510000, China
| | - Guanhua Du
- Beijing Key Lab of Drug Target Identification and Drug
Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences
& Peking Union Medical College, Beijing 100050,
China
| |
Collapse
|
24
|
Macip G, Garcia-Segura P, Mestres-Truyol J, Saldivar-Espinoza B, Pujadas G, Garcia-Vallvé S. A Review of the Current Landscape of SARS-CoV-2 Main Protease Inhibitors: Have We Hit the Bullseye Yet? Int J Mol Sci 2021; 23:259. [PMID: 35008685 PMCID: PMC8745775 DOI: 10.3390/ijms23010259] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/24/2021] [Accepted: 12/25/2021] [Indexed: 01/01/2023] Open
Abstract
In this review, we collected 1765 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M-pro inhibitors from the bibliography and other sources, such as the COVID Moonshot project and the ChEMBL database. This set of inhibitors includes only those compounds whose inhibitory capacity, mainly expressed as the half-maximal inhibitory concentration (IC50) value, against M-pro from SARS-CoV-2 has been determined. Several covalent warheads are used to treat covalent and non-covalent inhibitors separately. Chemical space, the variation of the IC50 inhibitory activity when measured by different methods or laboratories, and the influence of 1,4-dithiothreitol (DTT) are discussed. When available, we have collected the values of inhibition of viral replication measured with a cellular antiviral assay and expressed as half maximal effective concentration (EC50) values, and their possible relationship to inhibitory potency against M-pro is analyzed. Finally, the most potent covalent and non-covalent inhibitors that simultaneously inhibit the SARS-CoV-2 M-pro and the virus replication in vitro are discussed.
Collapse
Affiliation(s)
| | | | | | | | - Gerard Pujadas
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain; (G.M.); (P.G.-S.); (J.M.-T.); (B.S.-E.)
| | - Santiago Garcia-Vallvé
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain; (G.M.); (P.G.-S.); (J.M.-T.); (B.S.-E.)
| |
Collapse
|
25
|
Al-Shuhaib MBS, Hashim HO, Al-Shuhaib JMB. Epicatechin is a promising novel inhibitor of SARS-CoV-2 entry by disrupting interactions between angiotensin-converting enzyme type 2 and the viral receptor binding domain: A computational/simulation study. Comput Biol Med 2021; 141:105155. [PMID: 34942397 PMCID: PMC8679518 DOI: 10.1016/j.compbiomed.2021.105155] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/15/2021] [Accepted: 12/15/2021] [Indexed: 01/02/2023]
Abstract
Angiotensin-converting enzyme 2 (ACE2) is the first target of SARS-CoV-2 and a key functional host receptor through which this virus hooks into and infects human cells. The necessity to block this receptor is one of the essential means to prevent the outbreak of COVID-19. This study was conducted to determine the most eligible natural compound to suppress ACE2 to counterfeit its interaction with the viral infection. To do this, the most known compounds of sixty-six Iraqi medicinal plants were generated and retrieved from PubChem database. After preparing a library for Iraqi medicinal plants, 3663 unique ligands’ conformers were docked to ACE2 using the GLIDE tool. Results found that twenty-three compounds exhibited the highest binding affinity with ACE2. The druglikeness and toxicity potentials of these compounds were evaluated using SwissADME and Protox servers respectively. Out of these virtually screened twenty-three compounds, epicatechin and kempferol were predicted to exert the highest druglikeness and lowest toxicity potentials. Extended Molecular dynamics (MD) simulations showed that ACE2-epicatechin complex exhibited a slightly higher binding stability than ACE2-kempferol complex. In addition to the well-known ACE2 inhibitors that were identified in previous studies, this study revealed for the first time that epicatechin from Hypericum perforatum provided a better static and dynamic inhibition for ACE2 with highly favourable pharmacokinetic properties than the other known ACE2 inhibiting compounds. This study entailed the ability of epicatechin to be used as a potent natural inhibitor that can be used to block or at least weaken the SARS-CoV-2 entry and its subsequent invasion. In vitro experiments are required to validate epicatechin effectiveness against the activity of the human ACE2 receptor.
Collapse
Affiliation(s)
- Mohammed Baqur S Al-Shuhaib
- Department of Animal Production, College of Agriculture, Al-Qasim Green University, Al-Qasim, 51013, Babil, Iraq.
| | - Hayder O Hashim
- Department of Clinical Laboratory Sciences, College of Pharmacy, University of Babylon, Babil, 51001, Iraq.
| | | |
Collapse
|
26
|
|
27
|
Lewis RA. Best practices for repurposing studies. J Comput Aided Mol Des 2021; 35:1189-1193. [PMID: 34766233 PMCID: PMC8585576 DOI: 10.1007/s10822-021-00430-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/30/2021] [Indexed: 12/13/2022]
Affiliation(s)
- Richard A Lewis
- Global Discovery Chemistry, Novartis Pharma AG, Basel, Switzerland.
| |
Collapse
|