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Chandraghatgi R, Ji HF, Rosen GL, Sokhansanj BA. Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening. J Chem Inf Model 2024; 64:3826-3840. [PMID: 38696451 PMCID: PMC11197033 DOI: 10.1021/acs.jcim.4c00234] [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: 02/09/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/04/2024]
Abstract
Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.
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Affiliation(s)
- Rohan Chandraghatgi
- Department
of Biology, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Hai-Feng Ji
- Department
of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Gail L. Rosen
- Department
of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Bahrad A. Sokhansanj
- Department
of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
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2
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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3
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Humayun F, Khan F, Khan A, Alshammari A, Ji J, Farhan A, Fawad N, Alam W, Ali A, Wei DQ. De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations. J Biomol Struct Dyn 2024; 42:3019-3029. [PMID: 37449757 DOI: 10.1080/07391102.2023.2234481] [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: 12/26/2022] [Accepted: 04/30/2023] [Indexed: 07/18/2023]
Abstract
De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Fahad Humayun
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Fatima Khan
- National Institute of Health, Islamabad, Pakistan
| | - Abbas Khan
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Jun Ji
- Henan Provincial Engineering and Technology Center of Health Products for Livestock and Poultry, Henan Provincial Engineering and Technology Center of Animal Disease Diagnosis and Integrated Control, Nanyang Normal University, Nanyang, PR China
| | - Ali Farhan
- Department of Chemistry, Chung Yuan Christian University, Taoyuan, Taiwan
| | - Nasim Fawad
- Poultry Research Institute, Rawalpindi, Pakistan
| | - Waheed Alam
- National Institute of Health, Islamabad, Pakistan
| | - Arif Ali
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Dong-Qing Wei
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- Centre for Research in Molecular Modeling, Concordia University, Québec, Canada
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4
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Saquib Q, Bakheit AH, Ahmed S, Ansari SM, Al-Salem AM, Al-Khedhairy AA. Identification of Phytochemicals from Arabian Peninsula Medicinal Plants as Strong Binders to SARS-CoV-2 Proteases (3CL Pro and PL Pro) by Molecular Docking and Dynamic Simulation Studies. Molecules 2024; 29:998. [PMID: 38474509 DOI: 10.3390/molecules29050998] [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: 10/13/2023] [Revised: 02/04/2024] [Accepted: 02/14/2024] [Indexed: 03/14/2024] Open
Abstract
We provide promising computational (in silico) data on phytochemicals (compounds 1-10) from Arabian Peninsula medicinal plants as strong binders, targeting 3-chymotrypsin-like protease (3CLPro) and papain-like proteases (PLPro) of SARS-CoV-2. Compounds 1-10 followed the Lipinski rules of five (RO5) and ADMET analysis, exhibiting drug-like characters. Non-covalent (reversible) docking of compounds 1-10 demonstrated their binding with the catalytic dyad (CYS145 and HIS41) of 3CLPro and catalytic triad (CYS111, HIS272, and ASP286) of PLPro. Moreover, the implementation of the covalent (irreversible) docking protocol revealed that only compounds 7, 8, and 9 possess covalent warheads, which allowed the formation of the covalent bond with the catalytic dyad (CYS145) in 3CLPro and the catalytic triad (CYS111) in PLPro. Root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and radius of gyration (Rg) analysis from molecular dynamic (MD) simulations revealed that complexation between ligands (compounds 7, 8, and 9) and 3CLPro and PLPro was stable, and there was less deviation of ligands. Overall, the in silico data on the inherent properties of the above phytochemicals unravel the fact that they can act as reversible inhibitors for 3CLPro and PLPro. Moreover, compounds 7, 8, and 9 also showed their novel properties to inhibit dual targets by irreversible inhibition, indicating their effectiveness for possibly developing future drugs against SARS-CoV-2. Nonetheless, to confirm the theoretical findings here, the effectiveness of the above compounds as inhibitors of 3CLPro and PLPro warrants future investigations using suitable in vitro and in vivo tests.
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Affiliation(s)
- Quaiser Saquib
- Zoology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Ahmed H Bakheit
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Sarfaraz Ahmed
- Department of Pharmacognosy, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Sabiha M Ansari
- Botany & Microbiology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Abdullah M Al-Salem
- Zoology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Abdulaziz A Al-Khedhairy
- Zoology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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Haghir Ebrahim Abadi MH, Ghasemlou A, Bayani F, Sefidbakht Y, Vosough M, Mozaffari-Jovin S, Uversky VN. AI-driven covalent drug design strategies targeting main protease (m pro) against SARS-CoV-2: structural insights and molecular mechanisms. J Biomol Struct Dyn 2024:1-29. [PMID: 38287509 DOI: 10.1080/07391102.2024.2308769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
The emergence of new SARS-CoV-2 variants has raised concerns about the effectiveness of COVID-19 vaccines. To address this challenge, small-molecule antivirals have been proposed as a crucial therapeutic option. Among potential targets for anti-COVID-19 therapy, the main protease (Mpro) of SARS-CoV-2 is important due to its essential role in the virus's life cycle and high conservation. The substrate-binding region of the core proteases of various coronaviruses, including SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), could be used for the generation of new protease inhibitors. Various drug discovery methods have employed a diverse range of strategies, targeting both monomeric and dimeric forms, including drug repurposing, integrating virtual screening with high-throughput screening (HTS), and structure-based drug design, each demonstrating varying levels of efficiency. Covalent inhibitors, such as Nirmatrelvir and MG-101, showcase robust and high-affinity binding to Mpro, exhibiting stable interactions confirmed by molecular docking studies. Development of effective antiviral drugs is imperative to address potential pandemic situations. This review explores recent advances in the search for Mpro inhibitors and the application of artificial intelligence (AI) in drug design. AI leverages vast datasets and advanced algorithms to streamline the design and identification of promising Mpro inhibitors. AI-driven drug discovery methods, including molecular docking, predictive modeling, and structure-based drug repurposing, are at the forefront of identifying potential candidates for effective antiviral therapy. In a time when COVID-19 potentially threat global health, the quest for potent antiviral solutions targeting Mpro could be critical for inhibiting the virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | - Fatemeh Bayani
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Yahya Sefidbakht
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Massoud Vosough
- Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Sina Mozaffari-Jovin
- Department of Medical Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Vladimir N Uversky
- Department of Molecular Medicine, University of South Florida, Tampa, FL, USA
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7
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Zhang Y, Zhao Y, Liang H, Xu Y, Zhou C, Yao Y, Wang H, Yang X. Innovation-driven trend shaping COVID-19 vaccine development in China. Front Med 2023; 17:1096-1116. [PMID: 38102402 DOI: 10.1007/s11684-023-1034-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/15/2023] [Indexed: 12/17/2023]
Abstract
Confronted with the Coronavirus disease 2019 (COVID-19) pandemic, China has become an asset in tackling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and mutation, with several innovative platforms, which provides various technical means in this persisting combat. Derived from collaborated researches, vaccines based on the spike protein of SARS-CoV-2 or inactivated whole virus are a cornerstone of the public health response to COVID-19. Herein, we outline representative vaccines in multiple routes, while the merits and plights of the existing vaccine strategies are also summarized. Likewise, new technologies may provide more potent or broader immunity and will contribute to fight against hypermutated SARS-CoV-2 variants. All in all, with the ultimate aim of delivering robust and durable protection that is resilient to emerging infectious disease, alongside the traditional routes, the discovery of innovative approach to developing effective vaccines based on virus properties remains our top priority.
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Affiliation(s)
- Yuntao Zhang
- China National Biotec Group Company Limited, Beijing, 100029, China
| | - Yuxiu Zhao
- China National Biotec Group Company Limited, Beijing, 100029, China
| | - Hongyang Liang
- China National Biotec Group Company Limited, Beijing, 100029, China
| | - Ying Xu
- China National Biotec Group Company Limited, Beijing, 100029, China
| | - Chuge Zhou
- China National Biotec Group Company Limited, Beijing, 100029, China
| | - Yuzhu Yao
- China National Biotec Group Company Limited, Beijing, 100029, China
| | - Hui Wang
- China National Biotec Group Company Limited, Beijing, 100029, China.
| | - Xiaoming Yang
- China National Biotec Group Company Limited, Beijing, 100029, China.
- National Engineering Technology Research Center of Combined Vaccines, Wuhan, 430207, China.
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8
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Hasan MN, Ray M, Saha A. Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery. J Phys Chem B 2023; 127:9663-9684. [PMID: 37921534 DOI: 10.1021/acs.jpcb.3c04710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.
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Affiliation(s)
- Md Nazmul Hasan
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
| | - Manisha Ray
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Arjun Saha
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
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Zou J, Zhao L, Shi S. Generation of focused drug molecule library using recurrent neural network. J Mol Model 2023; 29:361. [PMID: 37932607 DOI: 10.1007/s00894-023-05772-5] [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: 07/30/2023] [Accepted: 10/26/2023] [Indexed: 11/08/2023]
Abstract
CONTEXT With the wide application of deep learning in drug research and development, de novo molecular design methods based on recurrent neural network (RNN) have strong advantages in drug molecule generation. The RNN model can be used to learn the internal chemical structure of molecules, which is similar to a natural language processing task. Although techniques for generating target-specific molecular libraries based on RNN models are mature, research related to drug design and screening continues around the clock. Research based on de novo drug design methods to generate larger quantities of valid compounds is necessary. METHODS In this study, a molecular generation model based on RNN was designed, which abandoned the traditional way of stacked RNN and introduced the Nested long short-term memory network structure. To enrich the library of focused molecules for specific targets, we fine-tuned the model using active molecules from novel coronavirus pneumonia and screened the molecules using machine learning models. Following rigorous screening, the selected molecules underwent molecular docking with the SARS-CoV-2 M-pro receptor using AutoDock2.4 to identify the top 3 potential inhibitors. Subsequently, 100-ns molecular dynamics simulations were conducted using Amber22. Molecule parameterization involved the GAFF2 force field, while the proteins were modeled using the ff19SB force field, with solvation facilitated by a truncated octahedral TIP3P solvent environment. Upon completion of molecular dynamics simulations, stability of ligand-protein complexes was assessed by analysis of RMSD, H-bonds, and MM-GBSA. Reasonable results prove that the model can complete the task of de novo drug design and has the potential to be ideal drug molecules.
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Affiliation(s)
- Jinping Zou
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China
- Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China
| | - Long Zhao
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China
- Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China
| | - Shaoping Shi
- Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
- Institute of Mathematics and Interdisciplinary Sciences, Nanchang University, Nanchang, 330031, China.
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Sivangi KB, Amilpur S, Dasari CM. ReGen-DTI: A novel generative drug target interaction model for predicting potential drug candidates against SARS-COV2. Comput Biol Chem 2023; 106:107927. [PMID: 37499436 DOI: 10.1016/j.compbiolchem.2023.107927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023]
Abstract
Covid-19 has caused massive numbers of infections and fatalities globally. In response, there has been a large-scale experimental and computational research effort to study and develop drugs. Towards this, Deep learning techniques are used for the generation of potential novel drug candidates that are proven to be effective against exploring large molecular search spaces. Recent advances in reinforcement learning in conjunction with generative techniques has proven to be a promising field in the area of drug discovery. In this regard, we propose a generative drug discovery approach using reinforcement techniques for sampling novel molecules that bind to the main protease of SARS-COV2. The generative method reported significant validity scores for the generated novel molecules and captured the underlying features of the training molecules. Further, the model is fine-tuned on existing re-purposed molecules which are active towards specific target proteins based on similarity metrics. Upon fine tuning the model generated 92.71% valid, 93.55% unique, and 100% novel molecules. Unlike previous methods which are dependent on docking procedures, we proposed a deep learning based novel drug target interaction (DTI) model to find the binding affinity between candidate molecules and target protease sequence. Finally, the binding affinity of the generated molecules is predicted against the 3CLPro main protease by using the proposed DTI model. Most of the generated molecules have shown binding affinity scores <100 nM (lower the better), which are significantly better compared to the existing commercial drugs including Remdesevir.
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Affiliation(s)
- Kaushik Bhargav Sivangi
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India
| | - Santhosh Amilpur
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India
| | - Chandra Mohan Dasari
- Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India.
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11
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Hongyu H, Wu T, He F, Chao M, Huang J, Wang X, Niu Z, Tang B. The binding mechanism of failed, in processing and succeed inhibitors target SARS-CoV-2 main protease. J Biomol Struct Dyn 2023:1-12. [PMID: 37735887 DOI: 10.1080/07391102.2023.2257800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 09/02/2023] [Indexed: 09/23/2023]
Abstract
Since the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), several variants have caused a persistent pandemic. Consequently, it is crucial to develop new potential anti-SARS-CoV-2 drugs with specificity. To minimize potential failures and preserve valuable clinical resources for the development of other useful drugs, researchers must enhance their understanding of the interactions between drugs and SARS-CoV-2. While numerous crystal structures of the SARS-CoV-2 main protease (SCM) and its inhibitors have been reported, they provide only static snapshots and fail to capture the dynamic nature of SCM/inhibitor interactions. Herein, we conducted molecular dynamics simulations for five SCM complexes: ritonavir (SCM/RTV), lopinavir (SCM/LPV), the identified inhibitor N3 (SCM/N3), the approved inhibitor ensitrelvir (SCM/ESV), and the approved drug nirmatrelvir (SCM/NMV). Additionally, we explored the potential for covalent bond formation in the N3 and NMV inhibitors through QM/MM calculations using Umbrella sampling. The results show that the binding site is highly flexible to fit those five different inhibitors and each compound has its unique binding mode at the same binding site. Moreover, the binding affinities of positive and negative inhibitors to SCM exhibit significant differences. By gaining insights into the dynamics, we can potentially elucidate why lopinavir/ritonavir, initially considered promising, failed to effectively treat COVID-19. Furthermore, understanding the mechanistic aspects of N3 and NMV inhibition on SCM not only contributes to rational drug discovery against COVID-19 but also aids future studies on the catalytic mechanisms of main proteases in other novel coronaviruses.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hu Hongyu
- Xingzhi College, Zhejiang Normal University, Lanxi, China
| | - Tong Wu
- School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Fengming He
- School of Pharmaceutical Sciences, Xiamen University, Xiamen, China
| | - Ma Chao
- MindRank AI Ltd, Hangzhou, China
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12
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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13
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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14
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Bono A, Lauria A, La Monica G, Alamia F, Mingoia F, Martorana A. In Silico Design of New Dual Inhibitors of SARS-CoV-2 M PRO through Ligand- and Structure-Based Methods. Int J Mol Sci 2023; 24:ijms24098377. [PMID: 37176082 PMCID: PMC10179319 DOI: 10.3390/ijms24098377] [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: 03/25/2023] [Revised: 04/28/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
The viral main protease is one of the most attractive targets among all key enzymes involved in the life cycle of SARS-CoV-2. Considering its mechanism of action, both the catalytic and dimerization regions could represent crucial sites for modulating its activity. Dual-binding the SARS-CoV-2 main protease inhibitors could arrest the replication process of the virus by simultaneously preventing dimerization and proteolytic activity. To this aim, in the present work, we identified two series' of small molecules with a significant affinity for SARS-CoV-2 MPRO, by a hybrid virtual screening protocol, combining ligand- and structure-based approaches with multivariate statistical analysis. The Biotarget Predictor Tool was used to filter a large in-house structural database and select a set of benzo[b]thiophene and benzo[b]furan derivatives. ADME properties were investigated, and induced fit docking studies were performed to confirm the DRUDIT prediction. Principal component analysis and docking protocol at the SARS-CoV-2 MPRO dimerization site enable the identification of compounds 1b,c,i,l and 2i,l as promising drug molecules, showing favorable dual binding site affinity on SARS-CoV-2 MPRO.
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Affiliation(s)
- Alessia Bono
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche "STEBICEF", University of Palermo, Viale delle Scienze, Ed. 17, 90128 Palermo, Italy
| | - Antonino Lauria
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche "STEBICEF", University of Palermo, Viale delle Scienze, Ed. 17, 90128 Palermo, Italy
| | - Gabriele La Monica
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche "STEBICEF", University of Palermo, Viale delle Scienze, Ed. 17, 90128 Palermo, Italy
| | - Federica Alamia
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche "STEBICEF", University of Palermo, Viale delle Scienze, Ed. 17, 90128 Palermo, Italy
| | - Francesco Mingoia
- Istituto per lo Studio dei Materiali Nanostrutturati (ISMN), Consiglio Nazionale delle Ricerche (CNR), Via Ugo La Malfa, 153, 90146 Palermo, Italy
| | - Annamaria Martorana
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche "STEBICEF", University of Palermo, Viale delle Scienze, Ed. 17, 90128 Palermo, Italy
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15
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Sarmiento Varón L, González-Puelma J, Medina-Ortiz D, Aldridge J, Alvarez-Saravia D, Uribe-Paredes R, Navarrete MA. The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management. Front Public Health 2023; 11:1140353. [PMID: 37113165 PMCID: PMC10126380 DOI: 10.3389/fpubh.2023.1140353] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.
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Affiliation(s)
| | - Jorge González-Puelma
- Centro Asistencial Docente y de Investigación, Universidad de Magallanes, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile
| | - David Medina-Ortiz
- Departamento de Ingeniería en Computación, Facultad de Ingeniería, Universidad de Magallanes, Punta Arenas, Chile
| | - Jacqueline Aldridge
- Departamento de Ingeniería en Computación, Facultad de Ingeniería, Universidad de Magallanes, Punta Arenas, Chile
| | - Diego Alvarez-Saravia
- Centro Asistencial Docente y de Investigación, Universidad de Magallanes, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile
| | - Roberto Uribe-Paredes
- Departamento de Ingeniería en Computación, Facultad de Ingeniería, Universidad de Magallanes, Punta Arenas, Chile
| | - Marcelo A. Navarrete
- Centro Asistencial Docente y de Investigación, Universidad de Magallanes, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile
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16
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Liu X, Zhang W, Tong X, Zhong F, Li Z, Xiong Z, Xiong J, Wu X, Fu Z, Tan X, Liu Z, Zhang S, Jiang H, Li X, Zheng M. MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules. J Cheminform 2023; 15:42. [PMID: 37031191 PMCID: PMC10082991 DOI: 10.1186/s13321-023-00711-1] [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: 10/15/2022] [Accepted: 03/14/2023] [Indexed: 04/10/2023] Open
Abstract
Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering method, MolFilterGAN, based on a progressively augmented generative adversarial network. Comparative analysis shows that MolFilterGAN outperforms conventional screening approaches based on drug-likeness or synthetic ability metrics. Retrospective analysis of AI-designed discoidin domain receptor 1 (DDR1) inhibitors shows that MolFilterGAN significantly increases the efficiency of molecular triaging. Further evaluation of MolFilterGAN on eight external ligand sets suggests that MolFilterGAN is useful in triaging or enriching bioactive compounds across a wide range of target types. These results highlighted the importance of MolFilterGAN in evaluating molecules integrally and further accelerating molecular discovery especially combined with advanced AI generative models.
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Affiliation(s)
- Xiaohong Liu
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou, 215128, China
| | - Wei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Feisheng Zhong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Zhaojun Li
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou, 215128, China
| | - Zhaoping Xiong
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jiacheng Xiong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xiaolong Wu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Zunyun Fu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
| | - Xiaoqin Tan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
- ByteDance AI Lab, No. 1999 Yishan Road, Shanghai, 201103, China
| | - Zhiguo Liu
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou, 215128, China
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Hualiang Jiang
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 310024, Hangzhou, China.
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17
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Kuang Y, Ma X, Shen W, Rao Q, Yang S. Discovery of 3CLpro inhibitor of SARS-CoV-2 main protease. Future Sci OA 2023; 9:FSO853. [PMID: 37090493 PMCID: PMC10116374 DOI: 10.2144/fsoa-2023-0020] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
Coronavirus main protease (3CLpro), a special cysteine protease in coronavirus family, is highly desirable in the life cycle of coronavirus. Here, molecular docking, ADMET pharmacokinetic profiles and molecular dynamics (MD) simulation were performed to develop specific 3CLpro inhibitor. The results showed that the 137 compounds originated from Chinese herbal have good binding affinity to 3CLpro. Among these, Cleomiscosin C, (+)-Norchelidonine, Protopine, Turkiyenine, Isochelidonine and Mallotucin A possessed prominent drug-likeness properties. Cleomiscosin C and Turkiyenine exhibited excellent pharmacokinetic profiles. Furthermore, the complex of Cleomiscosin C with SARS-CoV-2 main protease presented high stability. The findings in this work indicated that Cleomiscosin C is highly promising as a potential 3CLpro inhibitor, thus facilitating the development of effective drugs for COVID-19.
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Affiliation(s)
- Yi Kuang
- College of Chemical & Materials Engineering, Zhejiang A&F University, Lin'an, Zhejiang, 311300, PR China
| | - Xiaodong Ma
- College of Chemical & Materials Engineering, Zhejiang A&F University, Lin'an, Zhejiang, 311300, PR China
| | - Wenjing Shen
- College of Chemical & Materials Engineering, Zhejiang A&F University, Lin'an, Zhejiang, 311300, PR China
| | - Qingqing Rao
- College of Chemical & Materials Engineering, Zhejiang A&F University, Lin'an, Zhejiang, 311300, PR China
| | - Shengxiang Yang
- College of Chemical & Materials Engineering, Zhejiang A&F University, Lin'an, Zhejiang, 311300, PR China
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18
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Ma Q, Wang G, Li N, Wang X, Kang X, Mao Y, Wang G. Insights into the Effects and Mechanism of Andrographolide-Mediated Recovery of Susceptibility of Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics. Microbiol Spectr 2023; 11:e0297822. [PMID: 36602386 PMCID: PMC9927479 DOI: 10.1128/spectrum.02978-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023] Open
Abstract
The frequent resistance associated with β-lactam antibiotics and the high frequency of mutations in β-lactamases constitute a major clinical challenge that can no longer be ignored. Andrographolide (AP), a natural active compound, has been shown to restore susceptibility to β-lactam antibiotics. Fluorescence quenching and molecular simulation showed that AP quenched the intrinsic fluorescence of β-lactamase BlaZ and stably bound to the residues in the catalytic cavity of BlaZ. Of note, AP was found to reduce the stability of the cell wall (CW) in methicillin-resistant Staphylococcus aureus (MRSA), and in combination with penicillin G (PEN), it significantly induced CW roughness and dispersion and even caused its disintegration, while the same concentration of PEN did not. In addition, transcriptome sequencing revealed that AP induced a significant stress response and increased peptidoglycan (PG) synthesis but disrupted its cross-linking, and it repressed the expression of critical genes such as mecA, blaZ, and sarA. We also validated these findings by quantitative reverse transcription-PCR (qRT-PCR). Association analysis using the GEO database showed that the alterations caused by AP were similar to those caused by mutations in the sarA gene. In summary, AP was able to restore the susceptibility of MRSA to β-lactam antibiotics, mainly by inhibiting the β-lactamase BlaZ, by downregulating the expression of critical resistance genes such as mecA and blaZ, and by disrupting CW homeostasis. In addition, restoration of susceptibility to antibiotics could be achieved by inhibiting the global regulator SarA, providing an effective solution to alleviate the problem of bacterial resistance. IMPORTANCE Increasingly, alternatives to antibiotics are being used to mitigate the rapid onset and development of bacterial resistance, and the combination of natural compounds with traditional antibiotics has become an effective therapeutic strategy. Therefore, we attempted to discover more mechanisms to restore susceptibility and effective dosing strategies. Andrographolide (AP), as a natural active ingredient, can mediate recovery of susceptibility of MRSA to β-lactam antibiotics. AP bound stably to the β-lactamase BlaZ and impaired its hydrolytic activity. Notably, AP was able to downregulate the expression of critical resistance genes such as mecA, blaZ, and sarA. Meanwhile, it disrupted the CW cross-linking and homeostasis, while the same concentration of penicillin could not. The multiple inhibitory effect of AP resensitizes intrinsically resistant bacteria to β-lactam antibiotics, effectively prolonging the use cycle of these antibiotics and providing an effective solution to reduce the dosage of antibiotics and providing a theoretical reference for the prevention and control of MRSA.
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Affiliation(s)
- Qiang Ma
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Guilai Wang
- Yinchuan Hospital of Traditional Chinese Medicine, Yinchuan, Ningxia, China
| | - Na Li
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Xin Wang
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Xinyun Kang
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Yanni Mao
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
| | - Guiqin Wang
- Veterinary Pharmacology Lab, College of Agriculture, Ningxia University, Yinchuan, Ningxia, China
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19
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Bio-Guided Isolation of SARS-CoV-2 Main Protease Inhibitors from Medicinal Plants: In Vitro Assay and Molecular Dynamics. PLANTS 2022; 11:plants11151914. [PMID: 35893619 PMCID: PMC9332707 DOI: 10.3390/plants11151914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/21/2022] [Accepted: 07/22/2022] [Indexed: 12/24/2022]
Abstract
Since the emergence of the pandemic of the coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the discovery of antiviral phytoconstituents from medicinal plants against SARS-CoV-2 has been comprehensively researched. In this study, thirty-three plants belonging to seventeen different families used traditionally in Saudi Arabia were tested in vitro for their ability to inhibit the SARS-CoV-2 main protease (MPRO). Major constituents of the bio-active extracts were isolated and tested for their inhibition potential against this enzyme; in addition, their antiviral activity against the SARS-CoV-2 Egyptian strain was assessed. Further, the thermodynamic stability of the best active compounds was studied through focused comparative insights for the active metabolites regarding ligand–target binding characteristics at the molecular level. Additionally, the obtained computational findings provided useful directions for future drug optimization and development. The results revealed that Psiadia punctulata, Aframomum melegueta, and Nigella sativa extracts showed a high percentage of inhibition of 66.4, 58.7, and 31.5%, against SARS-CoV-2 MPRO, respectively. The major isolated constituents of these plants were identified as gardenins A and B (from P. punctulata), 6-gingerol and 6-paradol (from A. melegueta), and thymoquinone (from N. sativa). These compounds are the first to be tested invitro against SARS-CoV-2 MPRO. Among the isolated compounds, only thymoquinone (THY), gardenin A (GDA), 6-gingerol (GNG), and 6-paradol (PAD) inhibited the SARS-CoV-2 MPRO enzyme with inhibition percentages of 63.21, 73.80, 65.2, and 71.8%, respectively. In vitro assessment of SARS-CoV-2 (hCoV-19/Egypt/NRC-03/2020 (accession number on GSAID: EPI_ISL_430820) revealed a strong-to-low antiviral activity of the isolated compounds. THY showed relatively high cytotoxicity and was anti-SARS-CoV-2, while PAD demonstrated a cytotoxic effect on the tested VERO cells with a selectivity index of CC50/IC50 = 1.33 and CC50/IC50 = 0.6, respectively. Moreover, GNG had moderate activity at non-cytotoxic concentrations in vitro with a selectivity index of CC50/IC50 = 101.3/43.45 = 2.3. Meanwhile, GDA showed weak activity with a selectivity index of CC50/IC50 = 246.5/83.77 = 2.9. The thermodynamic stability of top-active compounds revealed preferential stability and SARS-CoV-2 MPRO binding affinity for PAD through molecular-docking-coupled molecular dynamics simulation. The obtained results suggest the treating potential of these plants and/or their active metabolites for COVID-19. However, further in-vivo and clinical investigations are required to establish the potential preventive and treatment effectiveness of these plants and/or their bio-active compounds in COVID-19.
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20
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Ma A, Wang J, Xu D, Ma Q. Deep learning analysis of single-cell data in empowering clinical implementation. Clin Transl Med 2022; 12:e950. [PMID: 35858171 PMCID: PMC9299757 DOI: 10.1002/ctm2.950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 11/12/2022] Open
Affiliation(s)
- Anjun Ma
- Department of Biomedical Informatics, College of MedicineThe Ohio State UniversityColumbusOhioUSA
- Pelotonia Institute for Immuno‐Oncology, The James Comprehensive Cancer CenterThe Ohio State UniversityColumbusOhioUSA
| | - Juexin Wang
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences CenterUniversity of MissouriColumbiaMissouriUSA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences CenterUniversity of MissouriColumbiaMissouriUSA
| | - Qin Ma
- Department of Biomedical Informatics, College of MedicineThe Ohio State UniversityColumbusOhioUSA
- Pelotonia Institute for Immuno‐Oncology, The James Comprehensive Cancer CenterThe Ohio State UniversityColumbusOhioUSA
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21
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Kulyar MFEA, Li R, Mehmood K, Waqas M, Li K, Li J. Potential influence of Nagella sativa (Black cumin) in reinforcing immune system: A hope to decelerate the COVID-19 pandemic. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2021; 85:153277. [PMID: 32773257 PMCID: PMC7347483 DOI: 10.1016/j.phymed.2020.153277] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/14/2020] [Accepted: 07/02/2020] [Indexed: 05/10/2023]
Abstract
The world is witnessing a difficult time. The race of developing a new coronavirus (COVID-19) vaccine is becoming more urgent. Many preliminary studies on the pathophysiology of COVID-19 patients have provided some clues to treat this pandemic. However, no suitable treatment has found yet. Various symptoms of patients infected with COVID-19 indicated the importance of immune regulation in the human body. Severe cases admitted to the intensive care unit showed high level of pro-inflammatory cytokines which enhanced the disease severity. Acute Respiratory Distress Syndrome (ARDS) in COVID-19 patients is another critical factor of disease severity and mortality. So, Immune modulation is the only way of regulating immune system. Nigella sativa has been used for medicinal purposes for centuries. The components of this plant are known for its intense immune-regulatory, anti-inflammatory, and antioxidant benefits in obstructive respiratory disorders. A molecular docking study also gave evidences that N. sativa decelerates COVID-19 and might give the same or better results than the FDA approved drugs. The aim of this review was to investigate the possible immune-regulatory effects of N. sativa on COVID-19 pandemic. Our review found N. sativa's Thymoquinone, Nigellidine, and α-hederin can be a potential influencer in reinforcing the immune response on molecular grounds.
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Affiliation(s)
| | - Rongrong Li
- Department of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, Jiangsu, China
| | - Khalid Mehmood
- Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur-63100, Pakistan
| | - Muhammad Waqas
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China; Faculty of Veterinary & Animal Sciences, University of the Poonch, Rawalakot, District Poonch 12350, Azad Jammu & Kashmir, Pakistan
| | - Kun Li
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Jiakui Li
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
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