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Borges-Araújo L, Pereira GP, Valério M, Souza PCT. Assessing the Martini 3 protein model: A review of its path and potential. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2024; 1872:141014. [PMID: 38670324 DOI: 10.1016/j.bbapap.2024.141014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/13/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024]
Abstract
Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field - Martini 3 - a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are.
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Affiliation(s)
- Luís Borges-Araújo
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France
| | - Gilberto P Pereira
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France
| | - Mariana Valério
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France
| | - Paulo C T Souza
- Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France; Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, 69364 Lyon, France.
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2
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da Rocha JAP, da Costa RA, da Costa ADSS, da Rocha ECM, Gomes AJB, Machado AK, Fagan SB, Brasil DDSB, Lima e Lima AH. Harnessing Brazilian biodiversity database: identification of flavonoids as potential inhibitors of SARS-CoV-2 main protease using computational approaches and all-atom molecular dynamics simulation. Front Chem 2024; 12:1336001. [PMID: 38456183 PMCID: PMC10917896 DOI: 10.3389/fchem.2024.1336001] [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: 11/09/2023] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the etiological agent responsible for the global outbreak of COVID-19 (Coronavirus Disease 2019). The main protease of SARS-CoV-2, Mpro, is a key enzyme that plays a vital role in mediating viral replication and transcription. In this study, a comprehensive computational approach was employed to investigate the binding affinity, selectivity, and stability of natural product candidates as potential new antivirals acting on the viral polyprotein processing mediated by SARS-CoV-2 Mpro. A library of 288 flavonoids extracted from Brazilian biodiversity was screened to select potential Mpro inhibitors. An initial filter based on Lipinski's rule of five was applied, and 204 compounds that did not violate any of the Lipinski rules were selected. The compounds were then docked into the active site of Mpro using the GOLD program, and the poses were subsequently re-scored using MM-GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy calculations performed by AmberTools23. The top five flavonoids with the best MM-GBSA binding free energy values were selected for analysis of their interactions with the active site residues of the protein. Next, we conducted a toxicity and drug-likeness analysis, and non-toxic compounds were subjected to molecular dynamics simulation and free energy calculation using the MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. It was observed that the five selected flavonoids had lower MM-GBSA binding free energy with Mpro than the co-crystal ligand. Furthermore, these compounds also formed hydrogen bonds with two important residues, Cys145 and Glu166, in the active site of Mpro. Two compounds that passed the drug-likeness filter showed stable conformations during the molecular dynamics simulations. Among these, NuBBE_867 exhibited the best MM-PBSA binding free energy value compared to the crystallographic inhibitor. Therefore, this study suggests that NuBBE_867 could be a potential inhibitor against the main protease of SARS-CoV-2 and may be further examined to confirm our results.
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Affiliation(s)
- João Augusto Pereira da Rocha
- Laboratory of Modeling and Computational Chemistry, Federal Institute of Education, Science and Technology of Paraná (IFPA) Campus Bragança, Bragança, Brazil
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
- Laboratory of Biosolutions and Bioplastics of the Amazon, Graduate Program in Science and Environment, Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Belém, Brazil
- Graduate Program in Chemistry, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
| | - Renato Araújo da Costa
- Laboratory of Biosolutions and Bioplastics of the Amazon, Graduate Program in Science and Environment, Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Belém, Brazil
- Laboratory of Molecular Biology, Evolution and Microbiology, Federal Institute of Education Science and Technology of Paraná (IFPA) Campus Abaetetuba, Abaetetuba, Brazil
| | - Andreia do Socorro Silva da Costa
- Laboratory of Biosolutions and Bioplastics of the Amazon, Graduate Program in Science and Environment, Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Belém, Brazil
| | - Elaine Cristina Medeiros da Rocha
- Laboratory of Modeling and Computational Chemistry, Federal Institute of Education, Science and Technology of Paraná (IFPA) Campus Bragança, Bragança, Brazil
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
- Laboratory of Biosolutions and Bioplastics of the Amazon, Graduate Program in Science and Environment, Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Belém, Brazil
- Graduate Program in Chemistry, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
| | - Anderson José Bahia Gomes
- Laboratory of Molecular Biology, Evolution and Microbiology, Federal Institute of Education Science and Technology of Paraná (IFPA) Campus Abaetetuba, Abaetetuba, Brazil
| | | | | | - Davi do Socorro Barros Brasil
- Laboratory of Biosolutions and Bioplastics of the Amazon, Graduate Program in Science and Environment, Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Belém, Brazil
- Graduate Program in Chemistry, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
| | - Anderson Henrique Lima e Lima
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
- Graduate Program in Chemistry, Institute of Exact and Natural Sciences, Federal University of Pará, Belém, Brazil
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3
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Nicoli A, Weber V, Bon C, Steuer A, Gustincich S, Gainetdinov RR, Lang R, Espinoza S, Di Pizio A. Structure-Based Discovery of Mouse Trace Amine-Associated Receptor 5 Antagonists. J Chem Inf Model 2023; 63:6667-6680. [PMID: 37847527 PMCID: PMC10647090 DOI: 10.1021/acs.jcim.3c00755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Indexed: 10/18/2023]
Abstract
Trace amine-associated receptors (TAARs) were discovered in 2001 as new members of class A G protein-coupled receptors (GPCRs). With the only exception of TAAR1, TAAR members (TAAR2-9, also known as noncanonical olfactory receptors) were originally described exclusively in the olfactory epithelium and believed to mediate the innate perception of volatile amines. However, most noncanonical olfactory receptors are still orphan receptors. Given its recently discovered nonolfactory expression and therapeutic potential, TAAR5 has been the focus of deorphanization campaigns that led to the discovery of a few druglike antagonists. Here, we report four novel TAAR5 antagonists identified through high-throughput screening, which, along with the four ligands published in the literature, constituted our starting point to design a computational strategy for the identification of TAAR5 ligands. We developed a structure-based virtual screening protocol that allowed us to identify three new TAAR5 antagonists with a hit rate of 10%. Despite lacking an experimental structure, we accurately modeled the TAAR5 binding site by integrating comparative sequence- and structure-based analyses of serotonin receptors with homology modeling and side-chain optimization. In summary, we have identified seven new TAAR5 antagonists that could serve as lead candidates for the development of new treatments for depression, anxiety, and neurodegenerative diseases.
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Affiliation(s)
- Alessandro Nicoli
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Chemoinformatics
and Protein Modelling, Department of Molecular Life Sciences, School
of Life Sciences, Technical University of
Munich, 85354 Freising, Germany
| | - Verena Weber
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Institute
for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine
(INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany
- Faculty
of Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062 Germany
| | - Carlotta Bon
- Istituto
Italiano di Tecnologia, 16163 Genova, Italy
| | - Alexandra Steuer
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Chemoinformatics
and Protein Modelling, Department of Molecular Life Sciences, School
of Life Sciences, Technical University of
Munich, 85354 Freising, Germany
| | | | - Raul R. Gainetdinov
- Institute
of Translational Biomedicine and Saint Petersburg University Hospital,
Saint Petersburg State University, Saint Petersburg 199034, Russia
| | - Roman Lang
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
| | - Stefano Espinoza
- Istituto
Italiano di Tecnologia, 16163 Genova, Italy
- Dipartimento
di Scienze della Salute, Università
del Piemonte Orientale, 28100 Novara, Italy
| | - Antonella Di Pizio
- Leibniz
Institute for Food Systems Biology at the Technical University of
Munich, 85354 Freising, Germany
- Chemoinformatics
and Protein Modelling, Department of Molecular Life Sciences, School
of Life Sciences, Technical University of
Munich, 85354 Freising, Germany
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Malla BA, Ali A, Maqbool I, Dar NA, Ahmad SB, Alsaffar RM, Rehman MU. Insights into molecular docking and dynamics to reveal therapeutic potential of natural compounds against P53 protein. J Biomol Struct Dyn 2023; 41:8762-8781. [PMID: 36281711 DOI: 10.1080/07391102.2022.2137241] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 10/11/2022] [Indexed: 10/31/2022]
Abstract
P53 is eminent tumour suppressor protein that plays a prominent role in cell cycle arrest, DNA repair, senescence, differentiation and initiation of apoptosis. P53 is an attractive drug target and the high toxicity of some cancer chemotherapy drugs increase the demand for new anti-cancer drugs from natural products. In this current scenario, identification of promising anticancer compounds from natural sources by repurposing approach is still relevant for the early prevention and effective management of cancer. In present study, we docked natural compounds like podophyllotoxin, quercetin and rutin along standard drugs (MG-132 and Bay 61-3606) against p53 protein. ADME/T analysis predicted toxicity of phytochemicals and drugs. In silico docking analysis of podophyllotoxin, quercetin and rutin gave HDOCK docking scores of -187.87, -148. 97 and -143.85, whereas control drugs MG-132 and Bay 61-3606 showed docking scores of -159.59 and -140.71 against p53 respectively. AutoDock analysis of rutin and MG-132 showed highest binding affinity scores of -7.3 and -6.8 kcal/mol against p53. Molecular dynamic simulation for p53 protein displayed stable conformation and convergence. In this study, P53-rutin complex showed free binding energy score of 11.84 kcal/mol and P53-MG-132 complex reported free energy score of 16.3 kcal/mol. Protein contacts atlas gives non-covalent contacts framework by exploring interfaces of individual subunits and protein-ligand interactions. STRING tool predicts physical and functional interactions between proteins. The results of this study revealed that rutin and MG-132 could be promising inhibitors against targeted p53 protein and this could prove detrimental for molecular therapeutics and drug-designing strategies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Bashir Ahmad Malla
- Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Aarif Ali
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Irfan Maqbool
- Department of Clinical Biochemistry, SKIMS Soura, Srinagar, J&K, India
| | - Nazir Ahmad Dar
- Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, J&K, India
| | - Sheikh Bilal Ahmad
- Division of Veterinary Biochemistry, SKUAST-K, Shuhama Alusteng, J&K, India
| | - Rana M Alsaffar
- Department Of Pharmacology & Toxicology, College Of Pharmacy Girls Section, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Muneeb U Rehman
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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5
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Rama GR, Saraiva Macedo Timmers LF, Volken de Souza CF. In Silico Strategies to Predict Anti-aging Features of Whey Peptides. Mol Biotechnol 2023:10.1007/s12033-023-00887-9. [PMID: 37737930 DOI: 10.1007/s12033-023-00887-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023]
Abstract
We have analysed the in silico potential of bioactive peptides from cheese whey, the most relevant by-product from the dairy industry, to bind into the active site of collagenase and elastase. The peptides generated from the hydrolysis of bovine β-lactoglobulin with three proteases (trypsin, chymotrypsin, and subtilisin) were docked onto collagenase and elastase by molecular docking. The interaction models were ranked according to their free binding energy using molecular dynamics simulations, which showed that most complexes presented favourable interactions. Interactions with elastase had significantly lower binding energies than those with collagenase. Regarding the interaction site, it was found that four bioactive peptides were positioned in collagenase's active site, while six were found in elastase's active site. Among these, the most we have found one promising collagen-binding peptide produced by chymotrypsin and two for elastase, produced by subtilisin and chymotrypsin. These in silico results can be used as a tool for designing further experiments aiming at testing the in vitro potential of the peptides found in this work.
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Affiliation(s)
- Gabriela Rabaioli Rama
- Graduate Program in Biotechnology, University of Vale do Taquari-Univates, Av. Avelino Tallini, 171, Lajeado, RS, 95914-014, Brazil
| | | | - Claucia Fernanda Volken de Souza
- Graduate Program in Biotechnology, University of Vale do Taquari-Univates, Av. Avelino Tallini, 171, Lajeado, RS, 95914-014, Brazil.
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Shi Y, Zhang X, Yang Y, Cai T, Peng C, Wu L, Zhou L, Han J, Ma M, Zhu W, Xu Z. D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual screening. Comput Biol Med 2023; 164:107283. [PMID: 37536095 DOI: 10.1016/j.compbiomed.2023.107283] [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: 06/08/2023] [Revised: 07/15/2023] [Accepted: 07/28/2023] [Indexed: 08/05/2023]
Abstract
Resource- and time-consuming biological experiments are unavoidable in traditional drug discovery, which have directly driven the evolution of various computational algorithms and tools for drug-target interaction (DTI) prediction. For improving the prediction reliability, a comprehensive platform is highly expected as some previously reported webservers are small in scale, single-method, or even out of service. In this study, we integrated the multiple-conformation based docking, 2D/3D ligand similarity search and deep learning approaches to construct a comprehensive webserver, namely D3CARP, for target prediction and virtual screening. Specifically, 9352 conformations with positive control of 1970 targets were used for molecular docking, and approximately 2 million target-ligand pairs were used for 2D/3D ligand similarity search and deep learning. Besides, the positive compounds were added as references, and related diseases of therapeutic targets were annotated for further disease-based DTI study. The accuracies of the molecular docking and deep learning approaches were 0.44 and 0.89, respectively. And the average accuracy of five ligand similarity searches was 0.94. The strengths of D3CARP encompass the support for multiple computational methods, ensemble docking, utilization of positive controls as references, cross-validation of predicted outcomes, diverse disease types, and broad applicability in drug discovery. The D3CARP is freely accessible at https://www.d3pharma.com/D3CARP/index.php.
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Affiliation(s)
- Yulong Shi
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinben Zhang
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yanqing Yang
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingting Cai
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Cheng Peng
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leyun Wu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liping Zhou
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaxin Han
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Minfei Ma
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiliang Zhu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhijian Xu
- State Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China.
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7
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Ali A, Mir GJ, Ayaz A, Maqbool I, Ahmad SB, Mushtaq S, Khan A, Mir TM, Rehman MU. In silico analysis and molecular docking studies of natural compounds of Withania somnifera against bovine NLRP9. J Mol Model 2023; 29:171. [PMID: 37155030 PMCID: PMC10165590 DOI: 10.1007/s00894-023-05570-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 04/21/2023] [Indexed: 05/10/2023]
Abstract
CONTEXT NLRP9 is a member of nucleotide-binding domain leucine-rich repeat-containing receptors and is found to be associated with many inflammatory diseases. In the current scenario, the identification of promising anti-inflammatory compounds from natural sources by repurposing approach is still relevant for the early prevention and effective management of the disease. METHODS In the present study, we docked bioactives of Ashwagandha (Withanoside IV, Withanoside V, Withanolide A, Withanolide B, and Sitoindoside IX) and two control drugs against bovine NLRP9 protein. ADME/T analysis was used to determine the physiochemical properties of compounds and standard drugs. Molecular modeling was used to evaluate the correctness and quality of protein structures. In silico docking analysis revealed Withanolide B had the highest binding affinity score of -10.5 kcal/mol, whereas, among control drugs, doxycycline hydrochloride was most effective (-10.3 kcal/mol). The results of this study revealed that bioactives of Withania somnifera could be promising inhibitors against bovine NLRP9. In the present study, molecular simulation was used to measure protein conformational changes over time. The Rg value was found to be 34.77A°. RMSD and B-factor were also estimated to provide insights into the flexibility and mobile regions of protein structure. A functional protein network interaction was constructed from information collected from non-curative sources as protein-protein interactions (PPI) that play an important role in determining the function of the target protein and the ability of the drug molecule. Thus, in the present situation, it is important to identify bioactives with the potential to combat inflammatory diseases and provide strength and immunity to the host. However, there is still a need to study in vitro and in vivo to further support these findings.
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Affiliation(s)
- Aarif Ali
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Hazratbal, Srinagar, 190006, J&K, India
| | - Gh Jeelani Mir
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Hazratbal, Srinagar, 190006, J&K, India
| | - Aadil Ayaz
- Department of Microbiology, SKIMS Medical College Bemina, Srinagar, 190018, J&K, India
| | - Illiyas Maqbool
- Department of Microbiology, Government Medical College, Baramulla, 193101, J&K, India
| | - Sheikh Bilal Ahmad
- Division of Veterinary Biochemistry, Faculty of Veterinary Sciences & Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir (SKUAST-K), Shuhama, Srinagar, 190006, J&K, India
| | - Saima Mushtaq
- Veterinary Microbiology Department, Indian Veterinary Research Institute (IVRI), Bareilly, Uttar Pradesh, 243122, India
| | - Altaf Khan
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi Arabia
| | - Tahir Maqbool Mir
- National Centre for Natural Products Research, University of Mississippi, Oxford, MS, 38677, USA
| | - Muneeb U Rehman
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia.
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8
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:molecules28093906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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9
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Clyde A, Liu X, Brettin T, Yoo H, Partin A, Babuji Y, Blaiszik B, Mohd-Yusof J, Merzky A, Turilli M, Jha S, Ramanathan A, Stevens R. AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection. Sci Rep 2023; 13:2105. [PMID: 36747041 PMCID: PMC9901402 DOI: 10.1038/s41598-023-28785-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.
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Affiliation(s)
- Austin Clyde
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA.
- Department of Computer Science, University of Chicago, Chicago, 60637, USA.
| | - Xuefeng Liu
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Thomas Brettin
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
| | - Hyunseung Yoo
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Alexander Partin
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Yadu Babuji
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Ben Blaiszik
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
- University of Chicago, Globus, Chicago, 60637, USA
| | - Jamaludin Mohd-Yusof
- Los Alamos National Laboratory, Computer, Computational, and Statistical Sciences, Los Alamos, 87545, USA
| | - Andre Merzky
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Matteo Turilli
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Shantenu Jha
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Arvind Ramanathan
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Rick Stevens
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
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10
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Grasso G, Di Gregorio A, Mavkov B, Piga D, Labate GFD, Danani A, Deriu MA. Fragmented blind docking: a novel protein-ligand binding prediction protocol. J Biomol Struct Dyn 2022; 40:13472-13481. [PMID: 34641761 DOI: 10.1080/07391102.2021.1988709] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Arianna Di Gregorio
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland.,PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy
| | - Bojan Mavkov
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Dario Piga
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | | | - Andrea Danani
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Marco A Deriu
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy
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11
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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12
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Ochoa R, Cossio P, Fox T. Protocol for iterative optimization of modified peptides bound to protein targets. J Comput Aided Mol Des 2022; 36:825-835. [PMID: 36258137 PMCID: PMC9640467 DOI: 10.1007/s10822-022-00482-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/03/2022] [Indexed: 12/02/2022]
Abstract
Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, 050010, Colombia. .,Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach/Riss, Germany.
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, 050010, Colombia.,Center for Computational Mathematics, Flatiron Institute, New York, 10010, USA.,Center for Computational Biology, Flatiron Institute, New York, 10010, USA
| | - Thomas Fox
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach/Riss, Germany
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13
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Sorokina M, Belapure J, Tüting C, Paschke R, Papasotiriou I, Rodrigues JP, Kastritis PL. An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation. J Mol Biol 2022; 434:167637. [PMID: 35595165 PMCID: PMC9112565 DOI: 10.1016/j.jmb.2022.167637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/28/2022] [Accepted: 05/06/2022] [Indexed: 12/16/2022]
Abstract
After two years since the outbreak, the COVID-19 pandemic remains a global public health emergency. SARS-CoV-2 variants with substitutions on the spike (S) protein emerge increasing the risk of immune evasion and cross-species transmission. Here, we analyzed the evolution of the S protein as recorded in 276,712 samples collected before the start of vaccination efforts. Our analysis shows that most variants destabilize the S protein trimer, increase its conformational heterogeneity and improve the odds of the recognition by the host cell receptor. Most frequent substitutions promote overall hydrophobicity by replacing charged amino acids, reducing stabilizing local interactions in the unbound S protein trimer. Moreover, our results identify "forbidden" regions that rarely show any sequence variation, and which are related to conformational changes occurring upon fusion. These results are significant for understanding the structure and function of SARS-CoV-2 related proteins which is a critical step in vaccine development and epidemiological surveillance.
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Affiliation(s)
- Marija Sorokina
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, 06120 Halle/Saale, Germany,RGCC International GmbH, Baarerstrasse 95, Zug 6300, Switzerland,BioSolutions GmbH, Weinbergweg 22, 06120 Halle/Saale, Germany
| | - Jaydeep Belapure
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3a, 06120 Halle/Saale, Germany
| | - Christian Tüting
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3a, 06120 Halle/Saale, Germany
| | - Reinhard Paschke
- BioSolutions GmbH, Weinbergweg 22, 06120 Halle/Saale, Germany,Biozentrum, Martin Luther University Halle-Wittenberg, Weinbergweg 22, 06120 Halle/Saale, Germany
| | | | | | - Panagiotis L. Kastritis
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, 06120 Halle/Saale, Germany,Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3a, 06120 Halle/Saale, Germany,Biozentrum, Martin Luther University Halle-Wittenberg, Weinbergweg 22, 06120 Halle/Saale, Germany,Corresponding author at: Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Str. 3, 06120 Halle/Saale, Germany
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14
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Computational Investigations on the Natural Small Molecule as an Inhibitor of Programmed Death Ligand 1 for Cancer Immunotherapy. Life (Basel) 2022; 12:life12050659. [PMID: 35629327 PMCID: PMC9145275 DOI: 10.3390/life12050659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/22/2022] [Accepted: 04/27/2022] [Indexed: 11/24/2022] Open
Abstract
Several therapeutic monoclonal antibodies approved by the FDA are available against the PD-1/PD-L1 (programmed death 1/programmed death ligand 1) immune checkpoint axis, which has been an unprecedented success in cancer treatment. However, existing therapeutics against PD-L1, including small molecule inhibitors, have certain drawbacks such as high cost and drug resistance that challenge the currently available anti-PD-L1 therapy. Therefore, this study presents the screening of 32,552 compounds from the Natural Product Atlas database against PD-L1, including three steps of structure-based virtual screening followed by binding free energy to refine the ideal conformation of potent PD-L1 inhibitors. Subsequently, five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, were collected based on the ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and binding free energy (>−60 kcal/mol) for further computational investigation in comparison to co-crystallized ligand, i.e., JQT inhibitor. Based on interaction mapping, explicit 100 ns molecular dynamics simulation, and end-point binding free energy calculations, the selected natural compounds were marked for substantial stability with PD-L1 via intermolecular interactions (hydrogen and hydrophobic) with essential residues in comparison to the JQT inhibitor. Collectively, the calculated results advocate the selected natural compounds as the putative potent inhibitors of PD-L1 and, therefore, can be considered for further development of PD-L1 immune checkpoint inhibitors in cancer immunotherapy.
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15
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Pavan M, Bassani D, Bolcato G, Bissaro M, Sturles M, Moro S. Computational strategies to identify new drug candidates against neuroinflammation. Curr Med Chem 2022; 29:4756-4775. [PMID: 35135446 DOI: 10.2174/0929867329666220208095122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022]
Abstract
The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.
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Affiliation(s)
- Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Mattia Sturles
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
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16
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Llorach-Pares L, Nonell-Canals A, Avila C, Sanchez-Martinez M. Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases. Mar Drugs 2022; 20:53. [PMID: 35049908 PMCID: PMC8781171 DOI: 10.3390/md20010053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 11/30/2022] Open
Abstract
Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet's biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.
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Affiliation(s)
- Laura Llorach-Pares
- Mind the Byte S.L., 08028 Barcelona, Catalonia, Spain; (L.L.-P.); (A.N.-C.)
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), University of Barcelona, 08028 Barcelona, Catalonia, Spain;
| | | | - Conxita Avila
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), University of Barcelona, 08028 Barcelona, Catalonia, Spain;
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17
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Antifungal Activity of N-(4-Halobenzyl)amides against Candida spp. and Molecular Modeling Studies. Int J Mol Sci 2021; 23:ijms23010419. [PMID: 35008845 PMCID: PMC8745543 DOI: 10.3390/ijms23010419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 12/28/2022] Open
Abstract
Fungal infections remain a high-incidence worldwide health problem that is aggravated by limited therapeutic options and the emergence of drug-resistant strains. Cinnamic and benzoic acid amides have previously shown bioactivity against different species belonging to the Candida genus. Here, 20 cinnamic and benzoic acid amides were synthesized and tested for inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019. Five compounds inhibited the Candida strains tested, with compound 16 (MIC = 7.8 µg/mL) producing stronger antifungal activity than fluconazole (MIC = 16 µg/mL) against C. krusei ATCC 14243. It was also tested against eight Candida strains, including five clinical strains resistant to fluconazole, and showed an inhibitory effect against all strains tested (MIC = 85.3–341.3 µg/mL). The MIC value against C. krusei ATCC 6258 was 85.3 mcg/mL, while against C. krusei ATCC 14243, it was 10.9 times smaller. This strain had greater sensitivity to the antifungal action of compound 16. The inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019 was also achieved by compounds 2, 9, 12, 14 and 15. Computational experiments combining target fishing, molecular docking and molecular dynamics simulations were performed to study the potential mechanism of action of compound 16 against C. krusei. From these, a multi-target mechanism of action is proposed for this compound that involves proteins related to critical cellular processes such as the redox balance, kinases-mediated signaling, protein folding and cell wall synthesis. The modeling results might guide future experiments focusing on the wet-lab investigation of the mechanism of action of this series of compounds, as well as on the optimization of their inhibitory potency.
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18
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Xu Z, Wauchope OR, Frank AT. Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking. J Chem Inf Model 2021; 61:5589-5600. [PMID: 34633194 DOI: 10.1021/acs.jcim.1c00746] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.
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Affiliation(s)
- Ziqiao Xu
- Chemistry Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Orrette R Wauchope
- Department of Natural Sciences, City University of New York, Baruch College, New York, New York 10010, United States
| | - Aaron T Frank
- Biophysics Program, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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19
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Mousivand M, Bagherzadeh K, Anfossi L, Javan-Nikkhah M. Key criteria for engineering mycotoxin binding aptamers via computational simulations: Aflatoxin B1 as a case study. Biotechnol J 2021; 17:e2100280. [PMID: 34800084 DOI: 10.1002/biot.202100280] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 11/16/2021] [Accepted: 11/16/2021] [Indexed: 12/30/2022]
Abstract
Due to the difficulties in monoclonal antibody production specific to mycotoxins, aptameric probes have been considered as suitable alternatives. The low efficiency of the SELEX procedure in screening high affinity aptamers for binding mycotoxins as small molecules can be significantly improved through computational techniques. Previously, we designed five new aptamers to aflatoxin B1 (AFB1) based on a known aptamer sequence (Patent: PCT/CA2010/001 292, Apt1) through a genetic algorithm-based in silico maturation strategy and experimentally measured their affinity to the target toxin. Here, integrated molecular dynamic simulation (MDs) studies with molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis to clarify the binding modes, critical interacting nucleic bases and energy component contributions in the six AFB1-binding aptamers. The aptamer F20, which was selected in the first work, showed the best free binding energy and complex stability compared to other aptamers. The trajectory analysis revealed that AFB1 recognized F20 through the groove binding mode along with precise shape complementarity. The MD simulation results revealed that dynamic water intermediate interactions also play a key role in promoting complex stability. According to the MM-PBSA calculations, van der Waals contacts were identified as dominant energy components in all complexes. Interestingly, a high consistency is observed between the experimentally obtained binding affinities of the six aptamers with their free energy solvation. The computational findings, confirmed via previous experiments, highlighted the binding modes, the dynamic hydration of complex components and the total free interacting energy as the crucial criteria in discovering high functional aptameric probes.
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Affiliation(s)
- Maryam Mousivand
- Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran, Agricultural Research, Education and Extension Organization, Department of Plant Protection, College of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Kowsar Bagherzadeh
- Stem Cell and Regenerative Medicine Research Center, Iran University of Medical Sciences, Eye Research Center, the Five Senses Health Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Laura Anfossi
- Department of Chemistry, University of Turin, Department of Plant Protection, College of Agricultural Sciences and Engineering, University of Tehran, Turin, Italy
| | - Mohammad Javan-Nikkhah
- Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran, Agricultural Research, Education and Extension Organization, Department of Plant Protection, College of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
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20
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Cytotoxic and Antifungal Amides Derived from Ferulic Acid: Molecular Docking and Mechanism of Action. BIOMED RESEARCH INTERNATIONAL 2021; 2021:3598000. [PMID: 34761004 PMCID: PMC8575619 DOI: 10.1155/2021/3598000] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/30/2021] [Accepted: 10/15/2021] [Indexed: 01/01/2023]
Abstract
Amides derived from ferulic acid have a wide spectrum of pharmacological activities, including antitumor and antifungal activity. In the present study, a series of ten amides were obtained by coupling reactions using the reagents (benzotriazol-1-yloxy) tripyrrolidinophosphonium hexafluorophosphate (PyBOP) and N,N′-dicyclohexylcarbodiimide (DCC). All the compounds were identified on the basis of their IR, 1H- and 13C-NMR, HRMS data, and with yields ranging from 43.17% to 91.37%. The compounds were subjected to cytotoxic tests by the alamar blue technique and antifungal screening by the broth microdilution method to determine the minimum inhibitory concentration (MIC). The amides 10 and 11 displayed the best result in both biological evaluations, and compound 10 was the most potent and selective in HL-60 cancer cells, with no cytotoxicity on healthy cells. This amide had antifungal activity in all strains and had the lowest MIC against Candida albicans and Candida tropicalis. The possible mechanism of antifungal action occurs via the fungal cell wall. Molecular modeling suggested that compounds 10 and 11 interact with the enzymes GWT1 and GSC1, which are essential for the development of C. albicans. The findings of the present study demonstrated that compounds 10 and 11 may be used as a platform in drug development in the future.
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21
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In silico analyses of predicted substitutions in fibrinolytic protein ‘Lumbrokinase-6’ suggest enhanced activity. Process Biochem 2021. [DOI: 10.1016/j.procbio.2021.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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22
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Zhao Y, Chen X, Ding Z, He C, Gao G, Lyu S, Gao Y, Du J. Identification of Novel CD39 Inhibitors Based on Virtual Screening and Enzymatic Assays. J Chem Inf Model 2021; 62:5289-5304. [PMID: 34648290 DOI: 10.1021/acs.jcim.1c00590] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The accumulation of adenosine in the tumor microenvironment mediates immunosuppression and promotes tumor growth and proliferation. Intervention of the adenosine pathway is an important direction of antitumor immunity research. CD39 is an important ecto-nucleotidases for adenosine generation, therefore targeting the CD39-adenosine pathway is an emerging immune checkpoint for anticancer treatment. However, currently no CD39 inhibitor has been approved by the U.S. Food and Drug Administration. The development of CD39 drugs is urgent for clinical application. In this study, we combined homology modeling, virtual screening, and in vitro enzymatic activity to characterize the structural features of the CD39 protein and identify a triazinoindole-based compound as a CD39 inhibitor. The identified inhibitor and one of its analogues could effectively prevent the enzymatic activity of CD39 with IC50 values of 27.42 ± 5.52 and 79.24 ± 12.21 μM, respectively. At the same time, the inhibitor significantly inhibited the adenosine monophosphate production in colorectal cancer cell lines (HT29 and MC38) and thereafter prevented cell proliferation. Molecular docking studies, mutagenesis, and microscale thermophoresis indicated that residues such as R85 could be the main contributor in binding triazinoindole compounds. The binding mode can potentially be utilized for hit-to-lead optimization, and the identified inhibitor can be further tested for its anticancer activity in vivo or may serve as a chemical agent to study CD39-related functions.
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Affiliation(s)
- Yunshuo Zhao
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaotong Chen
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Zhe Ding
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Chuanjie He
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Guanfei Gao
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Sifan Lyu
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Yanfeng Gao
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Jiangfeng Du
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
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23
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Hernández González JE, Salas-Sarduy E, Hernández Alvarez L, Barreto Gomes DE, Pascutti PG, Oostenbrink C, Leite VBP. In silico identification of noncompetitive inhibitors targeting an uncharacterized allosteric site of falcipain-2. J Comput Aided Mol Des 2021; 35:1067-1079. [PMID: 34617191 DOI: 10.1007/s10822-021-00420-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 09/24/2021] [Indexed: 01/05/2023]
Abstract
Falcipain-2 (FP-2) is a Plasmodium falciparum hemoglobinase widely targeted in the search for antimalarials. FP-2 can be allosterically modulated by various noncompetitive inhibitors that have been serendipitously identified. Moreover, the crystal structures of two inhibitors bound to an allosteric site, termed site 6, of the homolog enzyme human cathepsin K (hCatK) suggest that the equivalent region in FP-2 might play a similar role. Here, we conduct the rational identification of FP-2 inhibitors through virtual screenings (VS) of compounds into several pocket-like conformations of site 6, sampled during molecular dynamics (MD) simulations of the free enzyme. Two noncompetitive inhibitors, ZINC03225317 and ZINC72290660, were confirmed using in vitro enzymatic assays and their poses into site 6 led to calculated binding free energies matching the experimental ones. Our results provide strong evidence about the allosteric inhibition of FP-2 through binding of small molecules to site 6, thus opening the way toward the discovery of new inhibitors against this enzyme.
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Affiliation(s)
- Jorge Enrique Hernández González
- Departamento de Física, Instituto de Biociências, Letras e Ciências Exatas - Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Rua Cristóvão Colombo 2265, Jardim Nazareth, São José do Rio Preto, SP, CEP 15054-000, Brazil. .,Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofı́sica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Ave. Carlos Chagas Filho - Universidade Federal do Rio de Janeiro (UFRJ), Ave. Carlos Chagas Filho, 373, CCS-Bloco D sala 30, Cidade Universitária Ilha de Fundão, Rio de Janeiro, RJ, CEP 21941-902, Brazil. .,Institute for Molecular Modeling and Simulation, Department for Material Sciences and Process Engineering - University of Natural Resources and Life Sciences (BOKU), Vienna, Muthgasse 18, 1190, Vienna, Austria.
| | - Emir Salas-Sarduy
- Instituto de Investigaciones Biotecnológicas Dr. Rodolfo Ugalde, Universidad Nacional de San Martín, CONICET, San Martín, Buenos Aires, Argentina
| | - Lilian Hernández Alvarez
- Departamento de Física, Instituto de Biociências, Letras e Ciências Exatas - Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Rua Cristóvão Colombo 2265, Jardim Nazareth, São José do Rio Preto, SP, CEP 15054-000, Brazil
| | - Diego Enry Barreto Gomes
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofı́sica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Ave. Carlos Chagas Filho - Universidade Federal do Rio de Janeiro (UFRJ), Ave. Carlos Chagas Filho, 373, CCS-Bloco D sala 30, Cidade Universitária Ilha de Fundão, Rio de Janeiro, RJ, CEP 21941-902, Brazil.,Instituto de Ciências Exatas, Universidade Federal de Juiz de Fora (UFJF), Rua José Lourenço Kelmer, s/n - Campus Universitário, Bairro São Pedro, Juiz de Fora, MG, CEP 36036-900, Brazil
| | - Pedro Geraldo Pascutti
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofı́sica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Ave. Carlos Chagas Filho - Universidade Federal do Rio de Janeiro (UFRJ), Ave. Carlos Chagas Filho, 373, CCS-Bloco D sala 30, Cidade Universitária Ilha de Fundão, Rio de Janeiro, RJ, CEP 21941-902, Brazil
| | - Chris Oostenbrink
- Institute for Molecular Modeling and Simulation, Department for Material Sciences and Process Engineering - University of Natural Resources and Life Sciences (BOKU), Vienna, Muthgasse 18, 1190, Vienna, Austria
| | - Vitor B P Leite
- Departamento de Física, Instituto de Biociências, Letras e Ciências Exatas - Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP), Rua Cristóvão Colombo 2265, Jardim Nazareth, São José do Rio Preto, SP, CEP 15054-000, Brazil
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24
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Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective. Pharmacol Res 2021; 167:105577. [PMID: 33774182 DOI: 10.1016/j.phrs.2021.105577] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/23/2021] [Accepted: 03/21/2021] [Indexed: 12/11/2022]
Abstract
The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.
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25
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Fischer A, Smieško M, Sellner M, Lill MA. Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results. J Med Chem 2021; 64:2489-2500. [PMID: 33617246 DOI: 10.1021/acs.jmedchem.0c02227] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.
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Affiliation(s)
- André Fischer
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Martin Smieško
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Manuel Sellner
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Markus A Lill
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
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26
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Pinzi L, Tinivella A, Caporuscio F, Rastelli G. Drug Repurposing and Polypharmacology to Fight SARS-CoV-2 Through Inhibition of the Main Protease. Front Pharmacol 2021; 12:636989. [PMID: 33692695 PMCID: PMC7938350 DOI: 10.3389/fphar.2021.636989] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 01/12/2021] [Indexed: 12/15/2022] Open
Abstract
The outbreak of a new coronavirus (SARS-CoV-2), which is responsible for the COVID-19 disease and is spreading rapidly around the world, urgently requires effective therapeutic treatments. In this context, drug repurposing represents a valuable strategy, as it enables accelerating the identification of drug candidates with already known safety profiles, possibly aiding in the late stages of clinical evaluation. Moreover, therapeutic treatments based on drugs with beneficial multi-target activities (polypharmacology) may show an increased antiviral activity or help to counteract severe complications concurrently affecting COVID-19 patients. In this study, we present the results of a computational drug repurposing campaign that aimed at identifying potential inhibitors of the main protease (Mpro) of the SARS-CoV-2. The performed in silico screening allowed the identification of 22 candidates with putative SARS-CoV-2 Mpro inhibitory activity. Interestingly, some of the identified compounds have recently entered clinical trials for COVID-19 treatment, albeit not being assayed for their SARS-CoV-2 antiviral activity. Some candidates present a polypharmacology profile that may be beneficial for COVID-19 treatment and, to the best of our knowledge, have never been considered in clinical trials. For each repurposed compound, its therapeutic relevance and potential beneficial polypharmacological effects that may arise due to its original therapeutic indication are thoroughly discussed.
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Affiliation(s)
- Luca Pinzi
- Molecular Modelling and Drug Design Lab, Life Sciences Department, University of Modena and Reggio Emilia, Modena, Italy
| | - Annachiara Tinivella
- Molecular Modelling and Drug Design Lab, Life Sciences Department, University of Modena and Reggio Emilia, Modena, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Fabiana Caporuscio
- Molecular Modelling and Drug Design Lab, Life Sciences Department, University of Modena and Reggio Emilia, Modena, Italy
| | - Giulio Rastelli
- Molecular Modelling and Drug Design Lab, Life Sciences Department, University of Modena and Reggio Emilia, Modena, Italy
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27
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Yang D, Zhou Q, Labroska V, Qin S, Darbalaei S, Wu Y, Yuliantie E, Xie L, Tao H, Cheng J, Liu Q, Zhao S, Shui W, Jiang Y, Wang MW. G protein-coupled receptors: structure- and function-based drug discovery. Signal Transduct Target Ther 2021; 6:7. [PMID: 33414387 PMCID: PMC7790836 DOI: 10.1038/s41392-020-00435-w] [Citation(s) in RCA: 208] [Impact Index Per Article: 69.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/30/2020] [Accepted: 12/05/2020] [Indexed: 02/08/2023] Open
Abstract
As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.
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Affiliation(s)
- Dehua Yang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Qingtong Zhou
- School of Basic Medical Sciences, Fudan University, 200032, Shanghai, China
| | - Viktorija Labroska
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Shanshan Qin
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Sanaz Darbalaei
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Yiran Wu
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Elita Yuliantie
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Linshan Xie
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Houchao Tao
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Jianjun Cheng
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China
| | - Qing Liu
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.,The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, 201210, Shanghai, China. .,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
| | - Yi Jiang
- The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China.
| | - Ming-Wei Wang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China. .,The CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China. .,School of Basic Medical Sciences, Fudan University, 200032, Shanghai, China. .,University of Chinese Academy of Sciences, 100049, Beijing, China. .,School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China. .,School of Pharmacy, Fudan University, 201203, Shanghai, China.
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28
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Lin L, Lin K, Wu X, Liu J, Cheng Y, Xu LY, Li EM, Dong G. Potential Inhibitors of Fascin From A Database of Marine Natural Products: A Virtual Screening and Molecular Dynamics Study. Front Chem 2021; 9:719949. [PMID: 34692638 PMCID: PMC8529705 DOI: 10.3389/fchem.2021.719949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/17/2021] [Indexed: 02/05/2023] Open
Abstract
Marine nature products are unique compounds that are produced by the marine environment including plants, animals, and microorganisms. The wide diversity of marine natural products have great potential and are versatile in terms of drug discovery. In this paper, we use state-of-the-art computational methods to discover inhibitors from marine natural products to block the function of Fascin, an overexpressed protein in various cancers. First, virtual screening (pharmacophore model and molecular docking) was carried out based on a marine natural products database (12015 molecules) and provided eighteen molecules that could potentially inhibit the function of Fascin. Next, molecular mechanics generalized Born surface area (MM/GBSA) calculations were conducted and indicated that four molecules have higher binding affinities than the inhibitor NP-G2-029, which was validated experimentally. ADMET analyses of pharmacokinetics demonstrated that one of the four molecules does not match the criterion. Finally, ligand Gaussian accelerated molecular dynamics (LiGaMD) simulations were carried out to validate the three inhibitors binding to Fascin stably. In addition, dynamic interactions between protein and ligands were analyzed systematically. Our study will accelerate the development of the cancer drugs targeting Fascin.
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Affiliation(s)
- Lirui Lin
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Medical Informatics Research Center, Shantou University Medical College, Shantou, China
| | - Kai Lin
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Medical Informatics Research Center, Shantou University Medical College, Shantou, China
| | - Xiaodong Wu
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Jia Liu
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Yinwei Cheng
- Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
- Cancer Research Center, Shantou University Medical College, Shantou, China
| | - Li-Yan Xu
- Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
- Cancer Research Center, Shantou University Medical College, Shantou, China
- *Correspondence: Li-Yan Xu, ; En-Min Li, ; Geng Dong,
| | - En-Min Li
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Key Laboratory of Molecular Biology in High Cancer Incidence Coastal Area of Guangdong Higher Education Institutes, Shantou University Medical College, Shantou, China
- *Correspondence: Li-Yan Xu, ; En-Min Li, ; Geng Dong,
| | - Geng Dong
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Medical Informatics Research Center, Shantou University Medical College, Shantou, China
- *Correspondence: Li-Yan Xu, ; En-Min Li, ; Geng Dong,
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29
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Singh N, Villoutreix BO. Demystifying the Molecular Basis of Pyrazoloquinolinones Recognition at the Extracellular α1+/β3- Interface of the GABA A Receptor by Molecular Modeling. Front Pharmacol 2020; 11:561834. [PMID: 33041802 PMCID: PMC7518038 DOI: 10.3389/fphar.2020.561834] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
GABAA receptors are pentameric ligand-gated ion channels that serve as major inhibitory neurotransmitter receptors in the mammalian brain and the target of numerous clinically relevant drugs interacting with different ligand binding sites. Here, we report an in silico approach to investigate the binding of pyrazoloquinolinones (PQs) that mediate allosteric effects through the extracellular α+/β- interface of GABAA receptors. First, we docked a potent prototype of PQs into the α1+/β3- site of a homology model of the human α1β3γ2 subtype of the GABAA receptor. Next, for each docking pose, we computationally derived protein-ligand complexes for 18 PQ analogs with known experimental potency. Subsequently, binding energy was calculated for all complexes using the molecular mechanics-generalized Born surface area method. Finally, docking poses were quantitatively assessed in the light of experimental data to derive a binding hypothesis. Collectively, the results indicate that PQs at the α1+/β3- site likely exhibit a common binding mode that can be characterized by a hydrogen bond interaction with β3Q64 and hydrophobic interactions involving residues α1F99, β3Y62, β3M115, α1Y159, and α1Y209. Importantly, our results are in good agreement with the recently resolved cryo-Electron Microscopy structures of the human α1β3γ2 and α1β2γ2 subtypes of GABAA receptors.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, Lille, France.,Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Bruno O Villoutreix
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, Lille, France
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30
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Targeting the Protein Tunnels of the Urease Accessory Complex: A Theoretical Investigation. Molecules 2020; 25:molecules25122911. [PMID: 32599898 PMCID: PMC7355429 DOI: 10.3390/molecules25122911] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 12/13/2022] Open
Abstract
Urease is a nickel-containing enzyme that is essential for the survival of several and often deadly pathogenic bacterial strains, including Helicobacter pylori. Notwithstanding several attempts, the development of direct urease inhibitors without side effects for the human host remains, to date, elusive. The recently solved X-ray structure of the HpUreDFG accessory complex involved in the activation of urease opens new perspectives for structure-based drug discovery. In particular, the quaternary assembly and the presence of internal tunnels for nickel translocation offer an intriguing possibility to target the HpUreDFG complex in the search of indirect urease inhibitors. In this work, we adopted a theoretical framework to investigate such a hypothesis. Specifically, we searched for putative binding sites located at the protein–protein interfaces on the HpUreDFG complex, and we challenged their druggability through structure-based virtual screening. We show that, by virtue of the presence of tunnels, some protein–protein interfaces on the HpUreDFG complex are intrinsically well suited for hosting small molecules, and, as such, they possess good potential for future drug design endeavors.
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31
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Garofalo M, Grazioso G, Cavalli A, Sgrignani J. How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs. Molecules 2020; 25:E1756. [PMID: 32290224 PMCID: PMC7180704 DOI: 10.3390/molecules25071756] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/06/2020] [Accepted: 04/08/2020] [Indexed: 01/17/2023] Open
Abstract
The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.
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Affiliation(s)
- Mariangela Garofalo
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
| | - Giovanni Grazioso
- Department of Pharmaceutical Sciences, University of Milano, 20133 Milan, Italy
| | - Andrea Cavalli
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Institute for Research in Biomedicine (IRB), Università della Svizzera Italiana (USI), 6500 Bellinzona, Switzerland
| | - Jacopo Sgrignani
- Institute for Research in Biomedicine (IRB), Università della Svizzera Italiana (USI), 6500 Bellinzona, Switzerland
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32
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Tutumlu G, Dogan B, Avsar T, Orhan MD, Calis S, Durdagi S. Integrating Ligand and Target-Driven Based Virtual Screening Approaches With in vitro Human Cell Line Models and Time-Resolved Fluorescence Resonance Energy Transfer Assay to Identify Novel Hit Compounds Against BCL-2. Front Chem 2020; 8:167. [PMID: 32328476 PMCID: PMC7160371 DOI: 10.3389/fchem.2020.00167] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 02/25/2020] [Indexed: 12/13/2022] Open
Abstract
Antiapoptotic members of B-cell leukemia/lymphoma-2 (BCL-2) family proteins are one of the overexpressed proteins in cancer cells that are oncogenic targets. As such, targeting of BCL-2 family proteins raises hopes for new therapeutic discoveries. Thus, we used multistep screening and filtering approaches that combine structure and ligand-based drug design to identify new, effective BCL-2 inhibitors from a small molecule database (Specs SC), which includes more than 210,000 compounds. This database is first filtered based on binary “cancer-QSAR” model constructed with 886 training and 167 test set compounds and common 26 toxicity quantitative structure-activity relationships (QSAR) models. Predicted non-toxic compounds are considered for target-driven studies. Here, we applied two different approaches to filter and select hit compounds for further in vitro biological assays and human cell line experiments. In the first approach, a molecular docking and filtering approach is used to rank compounds based on their docking scores and only a few top-ranked molecules are selected for further long (100-ns) molecular dynamics (MD) simulations and in vitro tests. While docking algorithms are promising in predicting binding poses, they can be less prone to precisely predict ranking of compounds leading to decrease in the success rate of in silico studies. Hence, in the second approach, top-docking poses of each compound filtered through QSAR studies are subjected to initially short (1 ns) MD simulations and their binding energies are calculated via molecular mechanics generalized Born surface area (MM/GBSA) method. Then, the compounds are ranked based on their average MM/GBSA energy values to select hit molecules for further long MD simulations and in vitro studies. Additionally, we have applied text-mining approaches to identify molecules that contain “indol” phrase as many of the approved drugs contain indole and indol derivatives. Around 2700 compounds are filtered based on “cancer-QSAR” model and are then docked into BCL-2. Short MD simulations are performed for the top-docking poses for each compound in complex with BCL-2. The complexes are again ranked based on their MM/GBSA values to select hit molecules for further long MD simulations and in vitro studies. In total, seven molecules are subjected to biological activity tests in various human cancer cell lines as well as Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) assay. Inhibitory concentrations are evaluated, and biological activities and apoptotic potentials are assessed by cell culture studies. Four molecules are found to be limiting the proliferation capacity of cancer cells while increasing the apoptotic cell fractions.
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Affiliation(s)
- Gurbet Tutumlu
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey
| | - Berna Dogan
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey
| | - Timucin Avsar
- Department of Medical Biology, Bahcesehir University, School of Medicine, Istanbul, Turkey.,Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey.,Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey
| | - Muge Didem Orhan
- Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey.,Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey
| | - Seyma Calis
- Neuroscience Laboratory, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey.,Molecular Biology, Genetics and Biotechnology Graduate Program, Istanbul Technical University, Istanbul, Turkey
| | - Serdar Durdagi
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey.,Neuroscience Program, Health Sciences Institute, Bahcesehir University, Istanbul, Turkey
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33
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Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci 2019; 20:ijms20184331. [PMID: 31487867 PMCID: PMC6769923 DOI: 10.3390/ijms20184331] [Citation(s) in RCA: 706] [Impact Index Per Article: 141.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 09/02/2019] [Accepted: 09/02/2019] [Indexed: 12/11/2022] Open
Abstract
Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.
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In Silico Repositioning of Cannabigerol as a Novel Inhibitor of the Enoyl Acyl Carrier Protein (ACP) Reductase (InhA). Molecules 2019; 24:molecules24142567. [PMID: 31311157 PMCID: PMC6680637 DOI: 10.3390/molecules24142567] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 07/08/2019] [Accepted: 07/13/2019] [Indexed: 12/22/2022] Open
Abstract
Cannabigerol (CBG) and cannabichromene (CBC) are non-psychoactive cannabinoids that have raised increasing interest in recent years. These compounds exhibit good tolerability and low toxicity, representing promising candidates for drug repositioning. To identify novel potential therapeutic targets for CBG and CBC, an integrated ligand-based and structure-based study was performed. The results of the analysis led to the identification of CBG as a low micromolar inhibitor of the Enoyl acyl carrier protein (ACP) reductase (InhA) enzyme.
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