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Galvez-Llompart M, Hierrezuelo J, Blasco M, Zanni R, Galvez J, de Vicente A, Pérez-García A, Romero D. Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR. J Enzyme Inhib Med Chem 2024; 39:2330907. [PMID: 38651823 DOI: 10.1080/14756366.2024.2330907] [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: 10/25/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024] Open
Abstract
Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.
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Affiliation(s)
- Maria Galvez-Llompart
- Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Faculty of Pharmacy, University of Valencia, Burjassot, Spain
- Department of Physical Chemistry, University of Valencia, Burjassot, Spain
- Department of Microbiology, Faculty of Science, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, IHSM-UMA-CSIC, University of Málaga, Málaga, Spain
| | - Jesús Hierrezuelo
- Department of Microbiology, Faculty of Science, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, IHSM-UMA-CSIC, University of Málaga, Málaga, Spain
| | - Mariluz Blasco
- Department of Microbiology, Faculty of Science, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, IHSM-UMA-CSIC, University of Málaga, Málaga, Spain
| | - Riccardo Zanni
- Department of Physical Chemistry, University of Valencia, Burjassot, Spain
| | - Jorge Galvez
- Department of Physical Chemistry, University of Valencia, Burjassot, Spain
| | - Antonio de Vicente
- Department of Microbiology, Faculty of Science, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, IHSM-UMA-CSIC, University of Málaga, Málaga, Spain
| | - Alejandro Pérez-García
- Department of Microbiology, Faculty of Science, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, IHSM-UMA-CSIC, University of Málaga, Málaga, Spain
| | - Diego Romero
- Department of Microbiology, Faculty of Science, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora, IHSM-UMA-CSIC, University of Málaga, Málaga, Spain
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Vittorio S, Lunghini F, Morerio P, Gadioli D, Orlandini S, Silva P, Jan Martinovic, Pedretti A, Bonanni D, Del Bue A, Palermo G, Vistoli G, Beccari AR. Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities. Comput Struct Biotechnol J 2024; 23:2141-2151. [PMID: 38827235 PMCID: PMC11141151 DOI: 10.1016/j.csbj.2024.05.024] [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: 01/23/2024] [Revised: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/04/2024] Open
Abstract
Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.
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Affiliation(s)
- Serena Vittorio
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Filippo Lunghini
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123 Naples, Italy
| | - Pietro Morerio
- Pattern Analysis and Computer Vision, Fondazione Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genova, Italy
| | - Davide Gadioli
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, I-20133 Milano, Italy
| | - Sergio Orlandini
- SCAI, SuperComputing Applications and Innovation Department, CINECA, Via dei Tizii 6, Rome 00185, Italy
| | - Paulo Silva
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 70800 Ostrava-Poruba, Czech Republic
| | - Jan Martinovic
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 70800 Ostrava-Poruba, Czech Republic
| | - Alessandro Pedretti
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Domenico Bonanni
- Department of Physical and Chemical Sciences, University of L′Aquila, via Vetoio, L′Aquila 67010, Italy
| | - Alessio Del Bue
- Pattern Analysis and Computer Vision, Fondazione Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genova, Italy
| | - Gianluca Palermo
- Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, I-20133 Milano, Italy
| | - Giulio Vistoli
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Andrea R. Beccari
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123 Naples, Italy
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Chen H, Lu D, Xiao Z, Li S, Zhang W, Luan X, Zhang W, Zheng G. Comprehensive applications of the artificial intelligence technology in new drug research and development. Health Inf Sci Syst 2024; 12:41. [PMID: 39130617 PMCID: PMC11310389 DOI: 10.1007/s13755-024-00300-y] [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: 08/31/2023] [Accepted: 07/27/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Methods Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. Results In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Conclusion Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.
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Affiliation(s)
- Hongyu Chen
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Lu
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyi Xiao
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Shensuo Li
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Luan
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weidong Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guangyong Zheng
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Benghanem S, Mesli F, Fatima Zohra HA, Nacereddine C, Hadjer C, Abdellatif M. Discovery of novel and highly potential inhibitors of glycogen synthase kinase 3-beta (GSK-3β) through structure-based pharmacophore modeling, virtual computational screening, docking and in silico ADMET analysis. J Biomol Struct Dyn 2024; 42:7091-7106. [PMID: 37498130 DOI: 10.1080/07391102.2023.2238062] [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: 02/15/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023]
Abstract
The protein Glycogen Synthase Kinase 3-Beta (GSK-3β), is a promising therapeutic target for treating various diseases such as neurodegenerative disorders, diabetes, inflammation and cancer. This study aims to investigate the potential of compounds targeting inflammation or carbohydrate metabolism to selectively inhibit GSK3β by binding to its ATP site. To achieve this goal, we filtered a database of 49367 molecules involved in carbohydrate metabolism or targeting inflammation using various computational analyses, including pharmacophore modeling, molecular docking, dynamic simulation, prime MM-GBSA calculation, and in silico ADME studies. We generated a pharmacophore model (hypo S: AADDHRR) using two different crystallographic complexes of GSK3β and evaluated the model's performance in identifying hits using various parameters, including EF, GH, ROC, AUC and BEDROC. Subsequently, we performed various dockings (HTVS, SP, XP and IFD) for the retrieved hits and found that, 5 out of the top 10 ranked compounds had the scaffold of pyrazolidine 3,5-dione, which has never been reported to inhibit kinases. We also conducted ADMET studies to and concluded that compound N6 exhibited the best pharmacokinetic profile passing the blood-brain barrier, possessing high lipophilicity and a high coefficient of skin permeability in the intestines, along with good bioavailability and low toxicity risk assessment. Dynamic simulation were also performed indicating that compounds N6 derived from pyrazolidine 3,5-dione demonstrated better binding potential for GSK3β during the simulation period. Therefore, we propose that compounds derived from pyrazolidine-3,5-dione, which modulate the activity of lysosomal alpha-glucosidase could serve as a novel scaffold for the selective inhibition of GSK-3β.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Soumia Benghanem
- Faculty of Medicine, Laboratory of Therapeutic Chemistry, Tlemcen University, Tlemcen, Algeria
| | - Fouzia Mesli
- Faculty of Science, Laboratory of Natural and Bio-Actives Substances, Tlemcen University, Tlemcen, Algeria
| | - Hadjadj Aoul Fatima Zohra
- Faculty of Pharmacy, Laboratory of Therapeutic Chemistry, Benyoucef Benkhadda University, Tlemcen, Algeria
| | - Chaida Nacereddine
- Faculty of Medicine, Laboratory of Therapeutic Chemistry, Tlemcen University, Tlemcen, Algeria
| | - Chenaffa Hadjer
- Faculty of Medicine, Laboratory of Therapeutic Chemistry, Tlemcen University, Tlemcen, Algeria
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Jiang J, Ye Y, Li F, Luo H, Wang W. A commentary on 'Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis'. Int J Surg 2024; 110:5157-5158. [PMID: 38597383 PMCID: PMC11325880 DOI: 10.1097/js9.0000000000001452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 03/30/2024] [Indexed: 04/11/2024]
Affiliation(s)
- Jing Jiang
- Department of Pharmacy, Beilun District People's Hospital
| | - Yun Ye
- Department of Pharmacy, Beilun District People's Hospital
| | - Fangxian Li
- Department of Gastroenterology, Beilun District People's Hospital
| | - Hedan Luo
- Department of Information, Beilun District People's Hospital, Ningbo City, Zhejiang Province, People's Republic of China
| | - Wenbo Wang
- Department of Pharmacy, Beilun District People's Hospital
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Islam MR, Tayyeb JZ, Paul HK, Islam MN, Oduselu GO, Bayıl I, Abdellattif MH, Al‐Ahmary KM, Al‐Mhyawi SR, Zaki MEA. In silico analysis of potential inhibitors for breast cancer targeting 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) catalyses. J Cell Mol Med 2024; 28:e18584. [PMID: 39135338 PMCID: PMC11319393 DOI: 10.1111/jcmm.18584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/12/2024] [Accepted: 05/22/2024] [Indexed: 08/16/2024] Open
Abstract
Breast cancer (BC) is still one of the major issues in world health, especially for women, which necessitates innovative therapeutic strategies. In this study, we investigated the efficacy of retinoic acid derivatives as inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), which plays a crucial role in the biosynthesis and metabolism of oestrogen and thereby influences the progression of BC and, the main objective of this investigation is to identify the possible drug candidate against BC through computational drug design approach including PASS prediction, molecular docking, ADMET profiling, molecular dynamics simulations (MD) and density functional theory (DFT) calculations. The result has reported that total eight derivatives with high binding affinity and promising pharmacokinetic properties among 115 derivatives. In particular, ligands 04 and 07 exhibited a higher binding affinity with values of -9.9 kcal/mol and -9.1 kcal/mol, respectively, than the standard drug epirubicin hydrochloride, which had a binding affinity of -8.2 kcal/mol. The stability of the ligand-protein complexes was further confirmed by MD simulations over a 100-ns trajectory, which included assessments of hydrogen bonds, root mean square deviation (RMSD), root mean square Fluctuation (RMSF), dynamic cross-correlation matric (DCCM) and principal component analysis. The study emphasizes the need for experimental validation to confirm the therapeutic utility of these compounds. This study enhances the computational search for new BC drugs and establishes a solid foundation for subsequent experimental and clinical research.
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Affiliation(s)
- Md. Rezaul Islam
- Department of Pharmacy, Faculty of Allied Health SciencesDaffodil International UniversityDhakaBangladesh
| | - Jehad Zuhair Tayyeb
- Department of Clinical Biochemistry, College of MedicineUniversity of JeddahJeddahSaudi Arabia
| | - Hridoy Kumar Paul
- Department of PharmacyJashore University of Science and TechnologyJashoreBangladesh
| | | | | | - Imren Bayıl
- Department of bioinformatics and computational biologyGaziantep UniversityGaziantepTurkey
| | - Magda H. Abdellattif
- Department of Chemistry, Sciences CollegeUniversity College of Taraba, Taif UniversityTaifSaudi Arabia
| | | | - Saedah R. Al‐Mhyawi
- Department of Chemistry, College of ScienceUniversity of JeddahJeddahSaudi Arabia
| | - Magdi E. A. Zaki
- Department of Chemistry, College of ScienceImam Mohammad Ibn Saud Islamic University RiyadhRiyadhSaudi Arabia
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7
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Adokoh CK, Boadu A, Asiamah I, Agoni C. Synthesis and characterization of gold(I) thiolate derivatives and bimetallic complexes for HIV inhibition. Front Chem 2024; 12:1424019. [PMID: 39119520 PMCID: PMC11306053 DOI: 10.3389/fchem.2024.1424019] [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: 04/26/2024] [Accepted: 07/01/2024] [Indexed: 08/10/2024] Open
Abstract
Introduction: The human immunodeficiency virus (HIV) remains a significant global health concern, with a reported high infection rate of 38.4 million cases globally; an estimated 2 million new infections and approximately 700,000 HIV/AIDS-related deaths were reported in 2021. Despite the advent of anti-retroviral therapy (ART), HIV/AIDS persists as a chronic disease. To combat this, several studies focus on developing inhibitors targeting various stages of the HIV infection cycle, including HIV-1 protease. This study aims to synthesize and characterize novel glyco diphenylphosphino metal complexes with potential HIV inhibitory properties. Method: A series of new gold(I) thiolate derivatives and three bimetallic complexes, incorporating amino phosphines and thiocarbohydrate as auxiliary ligands, were synthesized using procedures described by Jiang, et al. (2009) and Coetzee et al. (2007). Structural elucidation and purity assessment of the synthesized compounds (1-11) were conducted using micro-analysis, NMR, and infrared spectrometry. Results and Discussion: Using molecular modeling techniques, three of the metal complexes were identified as potential HIV protease inhibitors, exhibiting strong binding affinity interactions with binding pocket residues. These inhibitors demonstrated an ability to inhibit the flexibility of the flap regions of the HIV protease, similar to the known HIV protease inhibitor, darunavir. This study sheds light on the promising avenues for the development of novel therapeutic agents against HIV/AIDS.
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Affiliation(s)
- Christian K. Adokoh
- Department of Forensic Sciences, School of Biological Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Akwasi Boadu
- Discipline of Pharmaceutical Sciences, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Wesbury College of Science, KwaZuluNatal, South Africa
| | - Isaac Asiamah
- Department of Chemistry, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Clement Agoni
- Discipline of Pharmaceutical Sciences, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
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Bergasa-Caceres F, Rabitz HA. A Perspective on Interdicting in Protein Misfolding for Therapeutic Drug Design: Modulating the Formation of Nonlocal Contacts in α-Synuclein as a Strategy against Parkinson's Disease. J Phys Chem B 2024; 128:6439-6448. [PMID: 38940731 PMCID: PMC11247489 DOI: 10.1021/acs.jpcb.3c07519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/29/2024]
Abstract
In recent work we proposed that interdiction in the earliest contact-formation events along the folding pathway of key viral proteins could provide a novel avenue for therapeutic drug design. In this Perspective we explore the potential applicability of the protein folding interdiction strategy in the realm of neurodegenerative diseases with a specific focus on synucleinopathies. In order to fulfill this goal we review the interdiction proposal and its practical challenges, and we present new results concerning design strategies for possible peptide drugs that could be useful in preventing α-synuclein aggregation.
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Affiliation(s)
| | - Herschel A. Rabitz
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
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9
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Omrani E, Haramshahi MA, Najmoddin N, Saeed M, Pezeshki-Modaress M. Acceleration of chondrogenic differentiation utilizing biphasic core-shell alginate sulfate electrospun nanofibrous scaffold. Colloids Surf B Biointerfaces 2024; 242:114080. [PMID: 39003847 DOI: 10.1016/j.colsurfb.2024.114080] [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: 05/09/2024] [Revised: 07/04/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024]
Abstract
Engineering new biomedical materials with tailored physicochemical, mechanical and biological virtues in order to differentiate stem cells into chondrocytes for cartilage regeneration has garnered much scientific interest. In this study, core/shell nanofibrous scaffold based on poly(ɛ-caprolactone) (PCL) as a core material and alginate sulfate (AlgS)-poly(vinyl alcohol) (PVA) blend as shell materials (AlgS-PVA/PCL) was fabricated by emulsion electrospinning. In this vein, the influence of AlgS to PVA ratio (30:70, 50:50), organic to aqueous phase ratio (1:2, 1:3 and 1:5) and acid concentration (0, 10, 20, 30, 40 and 50 %) on nanofibers morphology were investigated. SEM images depicted that AlgS to PVA ratio of 30:70 and 50:50, organic to aqueous phase ratio of 1:3 and 1:5 and acid concentration of 30 % led to uniform, bead-free fibrous mats. AlgS-PVA/PCL scaffolds with AlgS to PVA ratio of 30:70 and organic to aqueous phase ratio of 1:3, showed admirable mechanical features, high porosity (>90 %) with desirable swelling ratio in wet condition. In vitro assays indicated that the AlgS-PVA/PCL scaffold surface had desirable interaction with stem cells and promotes cells attachment, proliferation and differentiation. Thus, we envision that this salient structure could be an intriguing construction as a cartilage tissue-engineered scaffold.
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Affiliation(s)
- Elmira Omrani
- Department of Biomedical Engineering, Medical Engineering and Biology Research Center, Science and Research Branch, Islamic Azad University, Tehran, the Islamic Republic of Iran
| | - Mohammad Amin Haramshahi
- Department of Tissue Engineering and Regenerative Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, the Islamic Republic of Iran
| | - Najmeh Najmoddin
- Department of Biomedical Engineering, Medical Engineering and Biology Research Center, Science and Research Branch, Islamic Azad University, Tehran, the Islamic Republic of Iran.
| | - Mahdi Saeed
- Soft Tissue Engineering Research Center, Tissue Engineering and Regenerative Medicine Institute, Central Tehran Branch, Islamic Azad University, Tehran, the Islamic Republic of Iran
| | - Mohamad Pezeshki-Modaress
- Burn Research Center, Iran University of Medical Sciences, Tehran, the Islamic Republic of Iran; Department of Plastic and Reconstructive Surgery, Hazrat Fatemeh Hospital, School of Medicine, Iran University of Medical Sciences, Tehran, the Islamic Republic of Iran; Stem Cell and Regenerative Medicine Research Center, Iran University of Medical Sciences, Tehran, the Islamic Republic of Iran.
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10
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Guo L, Chang Z, Tong J, Gao P, Zhang Y, Liu Y, Yang Y, Wang C. Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations. Phys Chem Chem Phys 2024; 26:18149-18161. [PMID: 38896464 DOI: 10.1039/d4cp01741b] [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: 06/21/2024]
Abstract
Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.
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Affiliation(s)
- Liyuan Guo
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Zelei Chang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Jianbo Tong
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Peng Gao
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Yakun Zhang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Yuan Liu
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Yulu Yang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
| | - Chunying Wang
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an 710021, China
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Kehrein J, Bunker A, Luxenhofer R. POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles. Mol Pharm 2024; 21:3356-3374. [PMID: 38805643 DOI: 10.1021/acs.molpharmaceut.4c00086] [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: 05/30/2024]
Abstract
Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.
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Affiliation(s)
- Josef Kehrein
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Alex Bunker
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Robert Luxenhofer
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
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Jamtsho T, Loukas A, Wangchuk P. Pharmaceutical Potential of Remedial Plants and Helminths for Treating Inflammatory Bowel Disease. Pharmaceuticals (Basel) 2024; 17:819. [PMID: 39065669 PMCID: PMC11279646 DOI: 10.3390/ph17070819] [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: 05/12/2024] [Revised: 06/16/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
Research is increasingly revealing that inflammation significantly contributes to various diseases, particularly inflammatory bowel disease (IBD). IBD is a major medical challenge due to its chronic nature, affecting at least one in a thousand individuals in many Western countries, with rising incidence in developing nations. Historically, indigenous people have used natural products to treat ailments, including IBD. Ethnobotanically guided studies have shown that plant-derived extracts and compounds effectively modulate immune responses and reduce inflammation. Similarly, helminths and their products offer unique mechanisms to modulate host immunity and alleviate inflammatory responses. This review explored the pharmaceutical potential of Aboriginal remedial plants and helminths for treating IBD, emphasizing recent advances in discovering anti-inflammatory small-molecule drug leads. The literature from Scopus, MEDLINE Ovid, PubMed, Google Scholar, and Web of Science was retrieved using keywords such as natural product, small molecule, cytokines, remedial plants, and helminths. This review identified 55 important Aboriginal medicinal plants and 9 helminth species that have been studied for their anti-inflammatory properties using animal models and in vitro cell assays. For example, curcumin, berberine, and triptolide, which have been isolated from plants; and the excretory-secretory products and their protein, which have been collected from helminths, have demonstrated anti-inflammatory activity with lower toxicity and fewer side effects. High-throughput screening, molecular docking, artificial intelligence, and machine learning have been engaged in compound identification, while clustered regularly interspaced short palindromic repeats (CRISPR) gene editing and RNA sequencing have been employed to understand molecular interactions and regulations. While there is potential for pharmaceutical application of Aboriginal medicinal plants and gastrointestinal parasites in treating IBD, there is an urgent need to qualify these plant and helminth therapies through reproducible clinical and mechanistic studies.
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Affiliation(s)
- Tenzin Jamtsho
- College of Public Health, Medical, and Veterinary Sciences (CPHMVS), Cairns Campus, James Cook University, Cairns, QLD 4878, Australia
- Australian Institute of Tropical Health and Medicine (AITHM), Cairns Campus, James Cook University, Cairns, QLD 4878, Australia;
| | - Alex Loukas
- Australian Institute of Tropical Health and Medicine (AITHM), Cairns Campus, James Cook University, Cairns, QLD 4878, Australia;
| | - Phurpa Wangchuk
- College of Public Health, Medical, and Veterinary Sciences (CPHMVS), Cairns Campus, James Cook University, Cairns, QLD 4878, Australia
- Australian Institute of Tropical Health and Medicine (AITHM), Cairns Campus, James Cook University, Cairns, QLD 4878, Australia;
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Cordova-Chávez RI, Trujillo-Ferrara JG, Padilla-Martínez II, González-Espinosa H, Abad-García A, Farfán-García ED, Ortega-Camarillo C, Contreras-Ramos A, Soriano-Ursúa MA. One-Step Synthesis, Crystallography, and Acute Toxicity of Two Boron-Carbohydrate Adducts That Induce Sedation in Mice. Pharmaceuticals (Basel) 2024; 17:781. [PMID: 38931447 PMCID: PMC11206247 DOI: 10.3390/ph17060781] [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: 05/20/2024] [Revised: 06/08/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Boronic acids form diester bonds with cis-hydroxyl groups in carbohydrates. The formation of these adducts could impair the physical and chemical properties of precursors, even their biological activity. Two carbohydrate derivatives from d-fructose and d-arabinose and phenylboronic acid were synthesized in a straightforward one-step procedure and chemically characterized via spectroscopy and X-ray diffraction crystallography. Additionally, an acute toxicity test was performed to determine their lethal dose 50 (LD50) values by using Lorke's method. Analytical chemistry assays confirmed the formation of adducts by the generation of diester bonds with the β-d-pyranose of carbohydrates, including signals corresponding to the formation of new bonds, such as the stretching of B-O bonds. NMR spectra yielded information about the stereoselectivity in the synthesis reaction: Just one signal was found in the range for the anomeric carbon in the 13C NMR spectra of both adducts. The acute toxicity tests showed that the LD50 value for both compounds was 1265 mg/kg, while the effective dose 50 (ED50) for sedation was 531 mg/kg. However, differences were found in the onset and lapse of sedation. For example, the arabinose derivative induced sedation for more than 48 h at 600 mg/kg, while the fructose derivative induced sedation for less than 6 h at the same dose without the death of the mice. Thus, we report for the first time two boron-containing carbohydrate derivatives inducing sedation after intraperitoneal administration. They are bioactive and highly safe agents. Further biological evaluation is desirable to explore their medical applications.
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Affiliation(s)
- Ricardo Ivan Cordova-Chávez
- Laboratorio de Neurofisiología, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico; (R.I.C.-C.); (H.G.-E.); (A.A.-G.)
- Laboratorio de Bioquímica, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico;
| | - José G. Trujillo-Ferrara
- Laboratorio de Bioquímica, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico;
| | - Itzia I. Padilla-Martínez
- Laboratorio de Química Supramolecular y Nanociencias, Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Av. Acueducto s/n, Barrio la Laguna Ticomán, Mexico City 07340, Mexico;
| | - Héctor González-Espinosa
- Laboratorio de Neurofisiología, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico; (R.I.C.-C.); (H.G.-E.); (A.A.-G.)
| | - Antonio Abad-García
- Laboratorio de Neurofisiología, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico; (R.I.C.-C.); (H.G.-E.); (A.A.-G.)
| | - Eunice D. Farfán-García
- Laboratorio de Bioquímica, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico;
| | - Clara Ortega-Camarillo
- Medical Research Unit in Biochemistry, Specialties Hospital, National Medical Center SXXI, Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Alc. Cuauhtémoc, Mexico City 06720, Mexico;
| | - Alejandra Contreras-Ramos
- Laboratory of Molecular Biology in the Congenital Malformations Unit, Children’s Hospital of Mexico Federico Gomez (HIMFG), Calle Dr. Marques 162, Col. Doctores, Alc. Cuahutémoc, Mexico City 06720, Mexico;
| | - Marvin A. Soriano-Ursúa
- Laboratorio de Neurofisiología, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico; (R.I.C.-C.); (H.G.-E.); (A.A.-G.)
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14
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Song W, Xu L, Han C, Tian Z, Zou Q. Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism. Bioinformatics 2024; 40:btae346. [PMID: 38837345 PMCID: PMC11164831 DOI: 10.1093/bioinformatics/btae346] [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: 12/22/2023] [Revised: 05/06/2024] [Accepted: 05/28/2024] [Indexed: 06/07/2024] Open
Abstract
MOTIVATION Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. RESULTS In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. AVAILABILITY AND IMPLEMENTATION https://github.com/XuLew/MIDTI.
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Affiliation(s)
- Wei Song
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Lewen Xu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Chenguang Han
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
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15
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Luo Y, Duan G, Zhao Q, Bi X, Wang J. DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph. Methods 2024; 226:21-27. [PMID: 38608849 DOI: 10.1016/j.ymeth.2024.04.010] [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: 07/14/2023] [Revised: 01/16/2024] [Accepted: 04/09/2024] [Indexed: 04/14/2024] Open
Abstract
Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN.
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Affiliation(s)
- Yi Luo
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
| | - Qichang Zhao
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Xuehua Bi
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
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16
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De Vita S, Colarusso E, Chini MG, Bifulco G, Lauro G. PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes. J Chem Inf Model 2024; 64:4263-4276. [PMID: 38728062 DOI: 10.1021/acs.jcim.3c01920] [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: 05/28/2024]
Abstract
In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.
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Affiliation(s)
- Simona De Vita
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
| | - Ester Colarusso
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
| | - Maria Giovanna Chini
- Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone, Pesche, Isernia 86090, Italy
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
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17
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Yang J, Wang H, Liu J, Ma E, Jin X, Li Y, Ma C. Screening approach by a combination of computational and in vitro experiments: identification of fluvastatin sodium as a potential SIRT2 inhibitor. J Mol Model 2024; 30:188. [PMID: 38801625 DOI: 10.1007/s00894-024-05988-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Sirtuins (SIRTs) are NAD+-dependent deacetylases that play various roles in numerous pathophysiological processes, holding promise as therapeutic targets worthy of further investigation. Among them, the SIRT2 subtype is closely associated with tumorigenesis and malignancies. Dysregulation of SIRT2 activation can regulate the expression levels of related genes in cancer cells, leading to tumor occurrence and metastasis. METHODS In this study, we used computer simulations to screen for novel SIRT2 inhibitors from the FDA database, based on which 10 compounds with high docking scores and good interactions were selected for in vitro anti-pancreatic cancer metastasis testing and enzyme binding inhibition experiments. The results showed that fluvastatin sodium may possess inhibitory activity against SIRT2. Subsequently, fluvastatin sodium was subjected to molecular docking experiments with various SIRT isoforms, and the combined results from Western blotting experiments indicated its potential as a SIRT2 inhibitor. Next, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were performed, revealing the binding mode of fluvastatin sodium at the SIRT2 active site, further validating the stability and interaction of the ligand-protein complex under physiological conditions. RESULTS Overall, this study provides a systematic virtual screening workflow for the discovery of SIRT2 activity inhibitors, identifies the potential inhibitory effect of fluvastatin sodium as a lead compound on SIRT2, and opens up a new direction for developing highly active and selectively targeted SIRT2 inhibitors.
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Affiliation(s)
- Jin Yang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China
| | - Hanxun Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China
| | - Jiale Liu
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China
| | - Enlong Ma
- School of Life Sciences and Biopharmaceutical Science, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China
| | - Xinxin Jin
- School of Life Sciences and Biopharmaceutical Science, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China
| | - Yanchun Li
- School of Life Sciences and Biopharmaceutical Science, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China.
| | - Chao Ma
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China.
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenhe District, 103 Wenhua Road, Shenyang, 110016, People's Republic of China.
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Ahammad I, Bushra Lamisa A, Sharmin S, Bhattacharjee A, Mahmud Chowdhury Z, Ahamed T, Uzzal Hossain M, Chandra Das K, Salimullah M, Ara Keya C. Subtractive genomics study for the identification of therapeutic targets against Cronobacter sakazakii: A threat to infants. Heliyon 2024; 10:e30332. [PMID: 38707387 PMCID: PMC11066692 DOI: 10.1016/j.heliyon.2024.e30332] [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: 06/04/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/07/2024] Open
Abstract
Cronobacter sakazakii is an opportunistic pathogen that has been associated with severe infection in neonates such as necrotizing enterocolitis (NEC), neonatal meningitis, and bacteremia. This pathogen can survive in a relatively dry environment, especially in powdered infant formula (PIF). Unfortunately, conventional drugs that were once effective against C. sakazakii are gradually losing their efficacy due to rising antibiotic resistance. In this study, a subtractive genomic approach was followed in order to identify potential therapeutic targets in the pathogen. The whole proteome of the pathogen was filtered through a step-by-step process, which involved removing paralogous proteins, human homologs, sequences that are less essential for survival, proteins with shared metabolic pathways, and proteins that are located in cells other than the cytoplasmic membrane. As a result, nine novel drug targets were identified. Further, the analysis also unveiled that the FDA-approved drug Terbinafine can be repurposed against the Glutathione/l-cysteine transport system ATP-binding/permease protein CydC of C. sakazakii. Moreover, molecular docking and dynamics studies of Terbinafine and CydC suggested that this drug can be used to treat C. sakazakii infection in neonates. However, for clinical purposes further in vitro and in vivo studies are necessary.
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Affiliation(s)
- Ishtiaque Ahammad
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh
| | - Anika Bushra Lamisa
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, 1229, Bangladesh
| | - Sadia Sharmin
- Department of Biotechnology & Genetic Engineering, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
| | - Arittra Bhattacharjee
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh
| | - Zeshan Mahmud Chowdhury
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh
| | - Tanvir Ahamed
- Department of Biotechnology & Genetic Engineering, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh
| | - Mohammad Uzzal Hossain
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh
| | - Keshob Chandra Das
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh
| | - Md Salimullah
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh
| | - Chaman Ara Keya
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, 1229, Bangladesh
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Kumawat P, Agarwal LK, Sharma K. An Overview of SARS-CoV-2 Potential Targets, Inhibitors, and Computational Insights to Enrich the Promising Treatment Strategies. Curr Microbiol 2024; 81:169. [PMID: 38733424 DOI: 10.1007/s00284-024-03671-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 03/18/2024] [Indexed: 05/13/2024]
Abstract
The rapid spread of the SARS-CoV-2 virus has emphasized the urgent need for effective therapies to combat COVID-19. Investigating the potential targets, inhibitors, and in silico approaches pertinent to COVID-19 are of utmost need to develop novel therapeutic agents and reprofiling of existing FDA-approved drugs. This article reviews the viral enzymes and their counter receptors involved in the entry of SARS-CoV-2 into host cells, replication of genomic RNA, and controlling the host cell physiology. In addition, the study provides an overview of the computational techniques such as docking simulations, molecular dynamics, QSAR modeling, and homology modeling that have been used to find the FDA-approved drugs and other inhibitors against SARS-CoV-2. Furthermore, a comprehensive overview of virus-based and host-based druggable targets from a structural point of view, together with the reported therapeutic compounds against SARS-CoV-2 have also been presented. The current study offers future perspectives for research in the field of network pharmacology investigating the large unexplored molecular libraries. Overall, the present in-depth review aims to expedite the process of identifying and repurposing drugs for researchers involved in the field of COVID-19 drug discovery.
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Affiliation(s)
- Pooja Kumawat
- Department of Chemistry, Mohanlal Sukhadia University, Udaipur, Rajasthan, 313001, India
| | - Lokesh Kumar Agarwal
- Department of Chemistry, Mohanlal Sukhadia University, Udaipur, Rajasthan, 313001, India.
| | - Kuldeep Sharma
- Department of Botany, Mohanlal Sukhadia University, Udaipur, Rajasthan, 313001, India
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20
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Curcio A, Rocca R, Alcaro S, Artese A. The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods. Pharmaceuticals (Basel) 2024; 17:620. [PMID: 38794190 PMCID: PMC11124352 DOI: 10.3390/ph17050620] [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: 04/18/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to the regulation of various biological processes. Thus, HDACs play pivotal roles in various diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews the structure and function of the four classes of human HDACs. While four HDAC inhibitors are currently available for treating hematological malignancies, numerous others are undergoing clinical trials. However, their non-selective toxicity necessitates ongoing research into safer and more efficient class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches, such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships, and structure-based virtual screening (molecular docking). Moreover, recent developments in the field of molecular dynamics simulations, combined with Poisson-Boltzmann/molecular mechanics generalized Born surface area techniques, have improved the prediction of ligand binding affinity. In this review, we delve into the ways in which these methods have contributed to designing and identifying HDAC inhibitors.
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Affiliation(s)
- Antonio Curcio
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
| | - Roberta Rocca
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
- Net4Science S.r.l., Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - Stefano Alcaro
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
- Net4Science S.r.l., Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
| | - Anna Artese
- Dipartimento di Scienze della Salute, Campus “S. Venuta”, Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy; (A.C.); (S.A.); (A.A.)
- Net4Science S.r.l., Università degli Studi “Magna Græcia” di Catanzaro, Viale Europa, 88100 Catanzaro, Italy
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21
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Liu Y, Yu H, Duan X, Zhang X, Cheng T, Jiang F, Tang H, Ruan Y, Zhang M, Zhang H, Zhang Q. TransGEM: a molecule generation model based on Transformer with gene expression data. Bioinformatics 2024; 40:btae189. [PMID: 38632084 PMCID: PMC11078772 DOI: 10.1093/bioinformatics/btae189] [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: 11/21/2023] [Revised: 03/26/2024] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
MOTIVATION It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. AVAILABILITY AND IMPLEMENTATION The code is available at https://github.com/hzauzqy/TransGEM.
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Affiliation(s)
- Yanguang Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hailong Yu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xinya Duan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xiaomin Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Ting Cheng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Feng Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hao Tang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Yao Ruan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Miao Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Hongyu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Qingye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China
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22
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Qiu X, Wang H, Tan X, Fang Z. G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction. Comput Biol Med 2024; 173:108376. [PMID: 38552281 DOI: 10.1016/j.compbiomed.2024.108376] [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: 12/21/2023] [Revised: 03/21/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024]
Abstract
Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.
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Affiliation(s)
- Xihe Qiu
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Haoyu Wang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Xiaoyu Tan
- INF Technology (Shanghai) Co., Ltd., Shanghai, China
| | - Zhijun Fang
- School of Computer Science and Technology, Donghua University, Shanghai, China.
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23
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Fu J, Feng Y, Sun Y, Yi R, Tian J, Zhao W, Sun D, Zhang C. A Multi-Drug Concentration Gradient Mixing Chip: A Novel Platform for High-Throughput Drug Combination Screening. BIOSENSORS 2024; 14:212. [PMID: 38785686 PMCID: PMC11117479 DOI: 10.3390/bios14050212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Combinatorial drug therapy has emerged as a critically important strategy in medical research and patient treatment and involves the use of multiple drugs in concert to achieve a synergistic effect. This approach can enhance therapeutic efficacy while simultaneously mitigating adverse side effects. However, the process of identifying optimal drug combinations, including their compositions and dosages, is often a complex, costly, and time-intensive endeavor. To surmount these hurdles, we propose a novel microfluidic device capable of simultaneously generating multiple drug concentration gradients across an interlinked array of culture chambers. This innovative setup allows for the real-time monitoring of live cell responses. With minimal effort, researchers can now explore the concentration-dependent effects of single-agent and combination drug therapies. Taking neural stem cells (NSCs) as a case study, we examined the impacts of various growth factors-epithelial growth factor (EGF), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF)-on the differentiation of NSCs. Our findings indicate that an overdose of any single growth factor leads to an upsurge in the proportion of differentiated NSCs. Interestingly, the regulatory effects of these growth factors can be modulated by the introduction of additional growth factors, whether singly or in combination. Notably, a reduced concentration of these additional factors resulted in a decreased number of differentiated NSCs. Our results affirm that the successful application of this microfluidic device for the generation of multi-drug concentration gradients has substantial potential to revolutionize drug combination screening. This advancement promises to streamline the process and accelerate the discovery of effective therapeutic drug combinations.
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Affiliation(s)
- Jiahao Fu
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an 710127, China
| | - Yibo Feng
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an 710127, China
| | - Yu Sun
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, Xi’an 710127, China (R.Y.)
| | - Ruiya Yi
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, Xi’an 710127, China (R.Y.)
| | - Jing Tian
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, Xi’an 710127, China (R.Y.)
- Huaxin Microfish Biotechnology Co., Ltd., Taicang 215400, China
- Center for Automated and Innovative Drug Discovery, Northwest University, Xi’an 710127, China
| | - Wei Zhao
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an 710127, China
| | - Dan Sun
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an 710127, China
- Huaxin Microfish Biotechnology Co., Ltd., Taicang 215400, China
- Center for Automated and Innovative Drug Discovery, Northwest University, Xi’an 710127, China
| | - Ce Zhang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an 710127, China
- Huaxin Microfish Biotechnology Co., Ltd., Taicang 215400, China
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24
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Eissa I, Yousef RG, Elkaeed EB, Alsfouk AA, Husein DZ, Ibrahim IM, Ismail A, Elkady H, Metwaly AM. New Theobromine Apoptotic Analogue with Anticancer Potential Targeting the EGFR Protein: Computational and In Vitro Studies. ACS OMEGA 2024; 9:15861-15881. [PMID: 38617602 PMCID: PMC11007702 DOI: 10.1021/acsomega.3c08148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
AIM The aim of this study was to design and examine a novel epidermal growth factor receptor (EGFR) inhibitor with apoptotic properties by utilizing the essential structural characteristics of existing EGFR inhibitors as a foundation. METHOD The study began with the natural alkaloid theobromine and developed a new semisynthetic derivative (T-1-PMPA). Computational ADMET assessments were conducted first to evaluate its anticipated safety and general drug-likeness. Deep density functional theory (DFT) computations were initially performed to validate the three-dimensional (3D) structure and reactivity of T-1-PMPA. Molecular docking against the EGFR proteins was conducted to investigate T-1-PMPA's binding affinity and inhibitory potential. Additional molecular dynamics (MD) simulations over 200 ns along with MM-GPSA, PLIP, and principal component analysis of trajectories (PCAT) experiments were employed to verify the binding and inhibitory properties of T-1-PMPA. Afterward, T-1-PMPA was semisynthesized to validate the proposed design and in silico findings through several in vitro examinations. RESULTS DFT studies indicated T-1-PMPA's reactivity using electrostatic potential, global reactive indices, and total density of states. Molecular docking, MD simulations, MM-GPSA, PLIP, and ED suggested the binding and inhibitory properties of T-1-PMPA against the EGFR protein. The in silico ADMET predicted T-1-PMPA's safety and general drug-likeness. In vitro experiments demonstrated that T-1-PMPA effectively inhibited EGFRWT and EGFR790m, with IC50 values of 86 and 561 nM, respectively, compared to Erlotinib (31 and 456 nM). T-1-PMPA also showed significant suppression of the proliferation of HepG2 and MCF7 malignant cell lines, with IC50 values of 3.51 and 4.13 μM, respectively. The selectivity indices against the two cancer cell lines indicated the overall safety of T-1-PMPA. Flow cytometry confirmed the apoptotic effects of T-1-PMPA by increasing the total percentage of apoptosis to 42% compared to 31, and 3% in Erlotinib-treated and control cells, respectively. The qRT-PCR analysis further supported the apoptotic effects by revealing significant increases in the levels of Casp3 and Casp9. Additionally, T-1-PMPA controlled the levels of TNFα and IL2 by 74 and 50%, comparing Erlotinib's values (84 and 74%), respectively. CONCLUSION In conclusion, our study's findings suggest the potential of T-1-PMPA as a promising apoptotic anticancer lead compound targeting the EGFR.
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Affiliation(s)
- Ibrahim
H. Eissa
- Pharmaceutical
Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy
(Boys), Al-Azhar University, Cairo 11884, Egypt
| | - Reda G. Yousef
- Pharmaceutical
Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy
(Boys), Al-Azhar University, Cairo 11884, Egypt
| | - Eslam B. Elkaeed
- Department
of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh 13713, Saudi Arabia
| | - Aisha A. Alsfouk
- Department
of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Dalal Z. Husein
- Chemistry
Department, Faculty of Science, New Valley
University, El-Kharja 72511, Egypt
| | - Ibrahim M. Ibrahim
- Biophysics
Department, Faculty of Science, Cairo University, Giza 12613, Egypt
| | - Ahmed Ismail
- Biochemistry
and Molecular Biology Department, Faculty of Pharmacy, Al-Azhar University, Cairo 11884, Egypt
| | - Hazem Elkady
- Pharmaceutical
Medicinal Chemistry & Drug Design Department, Faculty of Pharmacy
(Boys), Al-Azhar University, Cairo 11884, Egypt
| | - Ahmed M. Metwaly
- Pharmacognosy
and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical
Products Research Department, Genetic Engineering and Biotechnology
Research Institute, City of Scientific Research
and Technological Applications (SRTA-City), Alexandria 21934, Egypt
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25
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Waseem T, Rajput TA, Mushtaq MS, Babar MM, Rajadas J. Computational biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:91-109. [PMID: 38789189 DOI: 10.1016/bs.pmbts.2024.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.
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Affiliation(s)
- Tanya Waseem
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Tausif Ahmed Rajput
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | | | - Mustafeez Mujtaba Babar
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan; Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States.
| | - Jayakumar Rajadas
- Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States
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26
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Das AP, Agarwal SM. Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches. Mol Divers 2024; 28:901-925. [PMID: 36670282 PMCID: PMC9859751 DOI: 10.1007/s11030-022-10590-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 12/18/2022] [Indexed: 01/22/2023]
Abstract
Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.
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Affiliation(s)
- Agneesh Pratim Das
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida, Uttar Pradesh, 201301, India
| | - Subhash Mohan Agarwal
- Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida, Uttar Pradesh, 201301, India.
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27
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Tong JB, Xiao XC, Luo D, Xu HY, Xing YC, Gao P, Liu Y. Discovery of novel BRD4-BD2 inhibitors via in silico approaches: QSAR techniques, molecular docking, and molecular dynamics simulations. Mol Divers 2024; 28:671-692. [PMID: 36773087 DOI: 10.1007/s11030-023-10611-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/23/2023] [Indexed: 02/12/2023]
Abstract
Bromodomain-containing protein 4(BRD4) plays an important role in the occurrence and development of various malignant tumors, which has attracted the attention of scientific research institutions and pharmaceutical companies. The structural modification of most currently available BRD4 inhibitors is relatively simple, but the drug effectiveness is limited. Research has found that the inhibition of BD1 may promote the differentiation of oligodendrocyte progenitor cell; however, the inhibition of BD2 will not cause this outcome. Therefore, newly potential drugs which target BRD4-BD2 need further research. Herein, we initially built QSAR models out of 49 compounds using HQSAR, CoMFA, CoMSIA, and Topomer CoMFA technology. All of the models have shown suitable reliabilities (q2 = 0.778, 0.533, 0.640, 0.702, respectively) and predictive abilities (r2pred = 0.716, 0.6289, 0.6153, 0.7968, respectively) for BRD4-BD2 inhibitors. On the basis of QSAR results and the search of the R-group in the topomer search module, we designed 20 new compounds with high activity that showed appropriate docking score and suitable ADMET. Docking studies and MD simulation were carried out to reveal the amino acid residues (Asn351, Cys347, Tyr350, Pro293, and Asp299) at the active site of BRD4-BD2. Free energy calculations and free energy landscapes verified the stable binding results and indicated stable conformations of the complexes. These theoretical studies provide guidance and theoretical basis for designing and developing novel BRD4-BD2 inhibitors.
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Affiliation(s)
- Jian-Bo Tong
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an,, 710021, People's Republic of China.
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an, 710021, People's Republic of China.
| | - Xue-Chun Xiao
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an,, 710021, People's Republic of China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an, 710021, People's Republic of China
| | - Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, 361005, People's Republic of China
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, People's Republic of China
| | - Hai-Yin Xu
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an,, 710021, People's Republic of China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an, 710021, People's Republic of China
| | - Yi-Chuang Xing
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an,, 710021, People's Republic of China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an, 710021, People's Republic of China
| | - Peng Gao
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an,, 710021, People's Republic of China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an, 710021, People's Republic of China
| | - Yuan Liu
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an,, 710021, People's Republic of China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Xi'an, 710021, People's Republic of China
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28
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Fukunaga I, Matsukiyo Y, Kaitoh K, Yamanishi Y. Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization. Mol Inform 2024; 43:e202300148. [PMID: 38182544 DOI: 10.1002/minf.202300148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 02/03/2024] [Accepted: 02/03/2024] [Indexed: 01/07/2024]
Abstract
Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.
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Affiliation(s)
- Itsuki Fukunaga
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yuki Matsukiyo
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
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29
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Velásquez-López Y, Ruiz-Escudero A, Arrasate S, González-Díaz H. Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family. J Chem Inf Model 2024; 64:1841-1852. [PMID: 38466369 PMCID: PMC10966645 DOI: 10.1021/acs.jcim.3c01796] [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: 11/07/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/13/2024]
Abstract
The Flaviviridae family consists of single-stranded positive-sense RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there is an outbreak of viral diseases caused by this family affecting millions of people worldwide, leading to significant morbidity and mortality rates. Advances in computational chemistry have greatly facilitated the discovery of novel drugs and treatments for diseases associated with this family. Chemoinformatic techniques, such as the perturbation theory machine learning method, have played a crucial role in developing new approaches based on ML models that can effectively aid drug discovery. The IFPTML models have shown its capability to handle, classify, and process large data sets with high specificity. The results obtained from different models indicates that this methodology is proficient in processing the data, resulting in a reduction of the false positive rate by 4.25%, along with an accuracy of 83% and reliability of 92%. These values suggest that the model can serve as a computational tool in assisting drug discovery efforts and the development of new treatments against Flaviviridae family diseases.
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Affiliation(s)
- Yendrek Velásquez-López
- Departamento
de Química Orgánica e Inorgánica, Facultad de
Ciencia y Tecnología, Universidad
del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU. Apdo. 644. 48080 Bilbao (Spain)
- Bio-Cheminformatics
Research Group, Universidad de Las Américas, Quito 170504, (Ecuador)
| | - Andrea Ruiz-Escudero
- Department
of Pharmacology, University of the Basque
Country UPV/EHU, 48940 Leioa, (Spain)
- IKERDATA
S.L., ZITEK, University of Basque Country
UPV/EHU, Rectorate Building, 48940 Leioa, Spain
| | - Sonia Arrasate
- Departamento
de Química Orgánica e Inorgánica, Facultad de
Ciencia y Tecnología, Universidad
del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU. Apdo. 644. 48080 Bilbao (Spain)
| | - Humberto González-Díaz
- Departamento
de Química Orgánica e Inorgánica, Facultad de
Ciencia y Tecnología, Universidad
del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU. Apdo. 644. 48080 Bilbao (Spain)
- BIOFISIKA, Basque
Center for Biophysics CSIC-UPV/EHU, 48940 Bilbao (Spain)
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao (Spain)
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30
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Giorgi C, Lombardozzi G, Ammannito F, Scenna MS, Maceroni E, Quintiliani M, d’Angelo M, Cimini A, Castelli V. Brain Organoids: A Game-Changer for Drug Testing. Pharmaceutics 2024; 16:443. [PMID: 38675104 PMCID: PMC11054008 DOI: 10.3390/pharmaceutics16040443] [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: 02/07/2024] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 04/28/2024] Open
Abstract
Neurological disorders are the second cause of death and the leading cause of disability worldwide. Unfortunately, no cure exists for these disorders, but the actual therapies are only able to ameliorate people's quality of life. Thus, there is an urgent need to test potential therapeutic approaches. Brain organoids are a possible valuable tool in the study of the brain, due to their ability to reproduce different brain regions and maturation stages; they can be used also as a tool for disease modelling and target identification of neurological disorders. Recently, brain organoids have been used in drug-screening processes, even if there are several limitations to overcome. This review focuses on the description of brain organoid development and drug-screening processes, discussing the advantages, challenges, and limitations of the use of organoids in modeling neurological diseases. We also highlighted the potential of testing novel therapeutic approaches. Finally, we examine the challenges and future directions to improve the drug-screening process.
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Affiliation(s)
| | | | | | | | | | | | | | - Annamaria Cimini
- Department of Life, Health and Environmental Science, University of L’Aquila, 67100 L’Aquila, Italy; (C.G.); (G.L.); (F.A.); (M.S.S.); (E.M.); (M.Q.); (M.d.)
| | - Vanessa Castelli
- Department of Life, Health and Environmental Science, University of L’Aquila, 67100 L’Aquila, Italy; (C.G.); (G.L.); (F.A.); (M.S.S.); (E.M.); (M.Q.); (M.d.)
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Afzal M, Qais FA, Abduh NA, Christy M, Ayub R, Alarifi A. Identification of bioactive compounds of Zanthoxylum armatum as potential inhibitor of pyruvate kinase M2 (PKM2): Computational and virtual screening approaches. Heliyon 2024; 10:e27361. [PMID: 38495183 PMCID: PMC10943388 DOI: 10.1016/j.heliyon.2024.e27361] [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: 09/30/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/19/2024] Open
Abstract
PKM2 (Pyruvate kinase M2) is the isoform of pyruvate kinase which is known to catalyse the last step of glycolysis that is responsible for energy production. This specific isoform is known to be highly expressed in certain cancerous conditions. Considering the role of this protein in various cancer conditions, we used PKM2 as a target protein to identify the potential compounds against this target. In this study, we have examined 96 compounds of Zanthoxylum armatum using an array of computational and in silico tools. The compounds were assessed for toxicity then their anticancer potential was predicted. The virtual screening was done with molecular docking followed by a detailed examination using molecular dynamics simulation. The majority of the compounds showed a higher probability of being antineoplastic. Based on toxicity, predicted anticancer potential, binding affinity, and binding site, three compounds (nevadensin, asarinin, and kaempferol) were selected as hit compounds. The binding energy of these compounds with PKM2 ranged from -7.7 to -8.3 kcal/mol and all hit compounds interact at the active site of the protein. The selected hit compounds formed a stable complex with PKM2 when simulated under physiological conditions. The dynamic analysis showed that these compounds remained attached to the active site till the completion of molecular simulation. MM-PBSA analysis showed that nevadensin exhibited a higher affinity towards PKM2 compared to asarinin and kaempferol. These compounds need to be assessed properties in vivo and in vitro to validate their efficacy.
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Affiliation(s)
- Mohd Afzal
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Faizan Abul Qais
- Department of Agricultural Microbiology, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh, UP, 202002, India
| | - Naaser A.Y. Abduh
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Maria Christy
- Department of Energy Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Rashid Ayub
- Department of Science Technology and Innovation, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Abdullah Alarifi
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
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Wang M, Wu Z, Wang J, Weng G, Kang Y, Pan P, Li D, Deng Y, Yao X, Bing Z, Hsieh CY, Hou T. Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space. J Chem Inf Model 2024; 64:1213-1228. [PMID: 38302422 DOI: 10.1021/acs.jcim.3c01964] [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: 02/03/2024]
Abstract
Deep learning-based de novo molecular design has recently gained significant attention. While numerous DL-based generative models have been successfully developed for designing novel compounds, the majority of the generated molecules lack sufficiently novel scaffolds or high drug-like profiles. The aforementioned issues may not be fully captured by commonly used metrics for the assessment of molecular generative models, such as novelty, diversity, and quantitative estimation of the drug-likeness score. To address these limitations, we proposed a genetic algorithm-guided generative model called GARel (genetic algorithm-based receptor-ligand interaction generator), a novel framework for training a DL-based generative model to produce drug-like molecules with novel scaffolds. To efficiently train the GARel model, we utilized dense net to update the parameters based on molecules with novel scaffolds and drug-like features. To demonstrate the capability of the GARel model, we used it to design inhibitors for three targets: AA2AR, EGFR, and SARS-Cov2. The results indicate that GARel-generated molecules feature more diverse and novel scaffolds and possess more desirable physicochemical properties and favorable docking scores. Compared with other generative models, GARel makes significant progress in balancing novelty and drug-likeness, providing a promising direction for the further development of DL-based de novo design methodology with potential impacts on drug discovery.
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Affiliation(s)
- Mingyang Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Zhengjian Wu
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- School of Computer Science, Wuhan University, Wuhan 430072, Hubei ,China
| | - Jike Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Gaoqi Weng
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yu Kang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Peichen Pan
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Dan Li
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang ,China
| | - Xiaojun Yao
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery Macau Institute for Applied Research in Medicine and Health State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Zhitong Bing
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
| | - Tingjun Hou
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou 310058, Zhejiang ,China
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Abubakar M, Mohamad SB, Abd Halim AA, Tayyab S. Unveiling the molecular interaction of hepatitis B virus inhibitor, entecavir with human serum albumin through computational, microscopic and spectroscopic approaches. J Biomol Struct Dyn 2024:1-14. [PMID: 38315445 DOI: 10.1080/07391102.2024.2311331] [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: 09/11/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024]
Abstract
Molecular docking, molecular dynamics (MD) simulation, atomic force microscopy (AFM) and multi-spectroscopic techniques were selected to unveil the molecular association between the hepatitis B virus (HBV) inhibitor, entecavir (ETR), and the major blood plasma transporter, human serum albumin (HSA). The entire docking and simulation analyses recognized ETR binding to subdomain IIA (Site I) of HSA through hydrogen bonds, hydrophobic and van der Waals forces while maintaining the complex's stability throughout the 100 ns. A gradual lessening in the Stern-Volmer quenching constant (K sv ) with rising temperatures registered ETR-induced quenching of HBV fluorescence as static quenching, thus advising complexation between ETR and HSA. The further advocation of this conclusion was seen from a larger value of the biomolecular quenching rate constant ((kq ) > 1010 M-1s-1), changes in the spectra (UV-Vis absorption) of HSA following ETR inclusion and ETR-induced swelling of HSA in the AFM results. The ETR appeared to bind to HSA with moderate affinity (K a = 1.87 - 1.19 × 10 4 M-1) at 290, 300 and 310 K. Significant alterations in the protein's secondary and tertiary structures, including changes in the protein's Tyr/Trp microenvironment, were also detected by circular dichroism and three-dimensional fluorescence spectra when the protein was bound to ETR. The findings of the drug displacement study backed the docking results of Site I as ETR's preferred binding site in HSA.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mujaheed Abubakar
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Saharuddin B Mohamad
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Adyani Azizah Abd Halim
- Department of Oral and Craniofacial Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Saad Tayyab
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, UCSI University, Kuala Lumpur, Malaysia
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Azevedo PHRDA, Peçanha BRDB, Flores-Junior LAP, Alves TF, Dias LRS, Muri EMF, Lima CHDS. In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor. J Biomol Struct Dyn 2024; 42:1417-1428. [PMID: 37054524 DOI: 10.1080/07391102.2023.2199868] [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: 10/20/2022] [Accepted: 04/01/2023] [Indexed: 04/15/2023]
Abstract
O-linked N-acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.
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Affiliation(s)
| | | | | | - Tatiana Fialho Alves
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Luiza Rosaria Sousa Dias
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Estela Maris Freitas Muri
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
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Tian C, Wang L, Cui Z, Wu H. GTAMP-DTA: Graph transformer combined with attention mechanism for drug-target binding affinity prediction. Comput Biol Chem 2024; 108:107982. [PMID: 38039800 DOI: 10.1016/j.compbiolchem.2023.107982] [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: 08/08/2023] [Revised: 10/21/2023] [Accepted: 11/07/2023] [Indexed: 12/03/2023]
Abstract
Drug target affinity prediction (DTA) is critical to the success of drug development. While numerous machine learning methods have been developed for this task, there remains a necessity to further enhance the accuracy and reliability of predictions. Considerable bias in drug target binding prediction may result due to missing structural information or missing information. In addition, current methods focus only on simulating individual non-covalent interactions between drugs and proteins, thereby neglecting the intricate interplay among different drugs and their interactions with proteins. GTAMP-DTA combines special Attention mechanisms, assigning each atom or amino acid an attention vector. Interactions between drug forms and protein forms were considered to capture information about their interactions. And fusion transformer was used to learn protein characterization from raw amino acid sequences, which were then merged with molecular map features extracted from SMILES. A self-supervised pre-trained embedding that uses pre-trained transformers to encode drug and protein attributes is introduced in order to address the lack of labeled data. Experimental results demonstrate that our model outperforms state-of-the-art methods on both the Davis and KIBA datasets. Additionally, the model's performance undergoes evaluation using three distinct pooling layers (max-pooling, mean-pooling, sum-pooling) along with variations of the attention mechanism. GTAMP-DTA shows significant performance improvements compared to other methods.
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Affiliation(s)
- Chuangchuang Tian
- College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Luping Wang
- College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Zhiming Cui
- College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Hongjie Wu
- College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China; Suzhou Smart City Research Institute, Suzhou University of Science and Technology, Suzhou 215009, China.
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36
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Khan MZI, Khan D, Akbar MY, Wang H, Haq IU, Chen JZ. 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, MD simulations, in vitro and in vivo studies to identify potential anti-hyperplasia drugs. Biotechnol J 2024; 19:e2300437. [PMID: 38403464 DOI: 10.1002/biot.202300437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/13/2023] [Accepted: 01/02/2024] [Indexed: 02/27/2024]
Abstract
Psoriasis is a common immune-mediated skin condition characterized by aberrant keratinocytes and cell proliferation. The purpose of this study was to explore the FDA-approved drugs by 3D-QSAR pharmacophore model and evaluate their efficiency by in-silico, in vitro, and in vivo psoriasis animal model. A 3D-QSAR pharmacophore model was developed by utilizing HypoGen algorithm using the structural features of 48 diaryl derivatives with diverse molecular patterns. The model was validated by a test set of 27 compounds, by cost analysis method, and Fischer's randomization test. The correlation coefficient of the best model (Hypo2) was 0.9601 for the training set while it was 0.805 for the test set. The selected model was taken as a 3D query for the virtual screening of over 3000 FDA-approved drugs. Compounds mapped with the pharmacophore model were further screened through molecular docking. The hits that showed the best docking results were screened through in silico skin toxicity approach. Top five hits were selected for the MD simulation studies. Based on MD simulations results, the best two hit molecules, that is, ebastine (Ebs) and mebeverine (Mbv) were selected for in vitro and in vivo antioxidant studies performed in mice. TNF-α and COX pro-inflammatory mediators, biochemical assays, histopathological analyses, and immunohistochemistry observations confirmed the anti-inflammatory response of the selected drugs. Based on these findings, it appeared that Ebs can effectively treat psoriasis-like skin lesions and down-regulate inflammatory responses which was consistent with docking predictions and could potentially be employed for further research on inflammation-related skin illnesses such as psoriasis.
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Affiliation(s)
| | - Dildar Khan
- Faculty of Biological Sciences, Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan
| | - Muhammad Yasir Akbar
- Computational Biology Lab, National Centre for Bioinformatics Quaid-i-Azam University, Islamabad, Pakistan
| | - Hao Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ihsan-Ul Haq
- Faculty of Biological Sciences, Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan
| | - Jian-Zhong Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
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37
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Ang D, Rakovski C, Atamian HS. De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search. Pharmaceuticals (Basel) 2024; 17:161. [PMID: 38399376 PMCID: PMC10892138 DOI: 10.3390/ph17020161] [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: 10/30/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.
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Affiliation(s)
- Dony Ang
- Computational and Data Sciences Program, Chapman University, Orange, CA 92866, USA; (D.A.); (C.R.)
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Cyril Rakovski
- Computational and Data Sciences Program, Chapman University, Orange, CA 92866, USA; (D.A.); (C.R.)
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
| | - Hagop S. Atamian
- Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
- Biological Sciences Program, Chapman University, Orange, CA 92866, USA
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Sharma T, Mondal T, Khan S, Churqui MP, Nyström K, Thombare K, Baig MH, Dong JJ. Identifying novel inhibitors targeting Exportin-1 for the potential treatment of COVID-19. Arch Microbiol 2024; 206:69. [PMID: 38240823 DOI: 10.1007/s00203-023-03761-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/09/2023] [Accepted: 11/19/2023] [Indexed: 01/23/2024]
Abstract
The nuclear export protein 1 (XPO1) mediates the nucleocytoplasmic transport of proteins and ribonucleic acids (RNAs) and plays a prominent role in maintaining cellular homeostasis. XPO1 has emerged as a promising therapeutic approach to interfere with the lifecycle of many viruses. In our earlier study, we proved the inhibition of XPO1 as a therapeutic strategy for managing SARS-COV-2 and its variants. In this study, we have utilized pharmacophore-assisted computational methods to identify prominent XPO1 inhibitors. After several layers of screening, a few molecules were shortlisted for further experimental validation on the in vitro SARS-CoV-2 cell infection model. It was observed that these compounds reduced spike positivity, suggesting inhibition of SARS-COV-2 infection. The outcome of this study could be considered further for developing novel antiviral therapeutic strategies against SARS-CoV-2.
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Affiliation(s)
- Tanuj Sharma
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea
| | - Tanmoy Mondal
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Sajid Khan
- Department of Biochemistry, Aligarh Muslim University, Aligarh, India
| | - Marianela Patzi Churqui
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 41345, Gothenburg, Sweden
| | - Kristina Nyström
- Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 41345, Gothenburg, Sweden
| | - Ketan Thombare
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mohammad Hassan Baig
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea.
| | - Jae-June Dong
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul, 06273, Republic of Korea.
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Chunarkar-Patil P, Kaleem M, Mishra R, Ray S, Ahmad A, Verma D, Bhayye S, Dubey R, Singh HN, Kumar S. Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies. Biomedicines 2024; 12:201. [PMID: 38255306 PMCID: PMC10813144 DOI: 10.3390/biomedicines12010201] [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: 12/02/2023] [Revised: 01/01/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.
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Affiliation(s)
- Pritee Chunarkar-Patil
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Mohammed Kaleem
- Department of Pharmacology, Dadasaheb Balpande, College of Pharmacy, Nagpur 440037, Maharashtra, India;
| | - Richa Mishra
- Department of Computer Engineering, Parul University, Ta. Waghodia, Vadodara 391760, Gujarat, India;
| | - Subhasree Ray
- Department of Life Science, Sharda School of Basic Sciences and Research, Greater Noida 201310, Uttar Pradesh, India
| | - Aftab Ahmad
- Health Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Pharmacovigilance and Medication Safety Unit, Center of Research Excellence for Drug Research and Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Devvret Verma
- Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun 248002, Uttarkhand, India;
| | - Sagar Bhayye
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Rajni Dubey
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Himanshu Narayan Singh
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sanjay Kumar
- Biological and Bio-Computational Lab, Department of Life Science, Sharda School of Basic Sciences and Research, Sharda University, Greater Noida 201310, Uttar Pradesh, India
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Suleman M, Said A, Khan H, Rehman SU, Alshammari A, Crovella S, Yassine HM. Mutational analysis of SARS-CoV-2 ORF6-KPNA2 binding interface and identification of potent small molecule inhibitors to recuse the host immune system. Front Immunol 2024; 14:1266776. [PMID: 38283360 PMCID: PMC10811244 DOI: 10.3389/fimmu.2023.1266776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/14/2023] [Indexed: 01/30/2024] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surfaced on 31 December, 2019, and was identified as the causative agent of the global COVID-19 pandemic, leading to a pneumonia-like disease. One of its accessory proteins, ORF6, has been found to play a critical role in immune evasion by interacting with KPNA2 to antagonize IFN signaling and production pathways, resulting in the inhibition of IRF3 and STAT1 nuclear translocation. Since various mutations have been observed in ORF6, therefore, a comparative binding, biophysical, and structural analysis was used to reveal how these mutations affect the virus's ability to evade the human immune system. Among the identified mutations, the V9F, V24A, W27L, and I33T, were found to have a highly destabilizing effect on the protein structure of ORF6. Additionally, the molecular docking analysis of wildtype and mutant ORF6 and KPNA2 revealed the docking score of - 53.72 kcal/mol for wildtype while, -267.90 kcal/mol, -258.41kcal/mol, -254.51 kcal/mol and -268.79 kcal/mol for V9F, V24A, W27L, and I33T respectively. As compared to the wildtype the V9F showed a stronger binding affinity with KPNA2 which is further verified by the binding free energy (-42.28 kcal/mol) calculation. Furthermore, to halt the binding interface of the ORF6-KPNA2 complex, we used a computational molecular search of potential natural products. A multi-step virtual screening of the African natural database identified the top 5 compounds with best docking scores of -6.40 kcal/mol, -6.10 kcal/mol, -6.09 kcal/mol, -6.06 kcal/mol, and -6.03 kcal/mol for tophit1-5 respectively. Subsequent all-atoms simulations of these top hits revealed consistent dynamics, indicating their stability and their potential to interact effectively with the interface residues. In conclusion, our study represents the first attempt to establish a foundation for understanding the heightened infectivity of new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.
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Affiliation(s)
- Muhammad Suleman
- Laboratory of Animal Research Center (LARC), Qatar University, Doha, Qatar
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Afsheen Said
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Haji Khan
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Shoaib Ur Rehman
- Department of Biotechnology, University of Science and Technology, Bannu, Pakistan
- Wilhelm Johansen Centre for Functional Genome Research, Department of Cellular and Molecular Medicine, The PANUM Institute, University of Copenhagen, Copenhagen, Denmark
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Sergio Crovella
- Laboratory of Animal Research Center (LARC), Qatar University, Doha, Qatar
| | - Hadi M. Yassine
- Biomedical Research Center, Qatar University, Doha, Qatar
- College of Health Sciences-Qatar University (QU) Health, Qatar University, Doha, Qatar
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Suleman M, Ahmad T, shah K, Albekairi NA, Alshammari A, Khan A, Wei DQ, Yassine HM, Crovella S. Exploring the natural products chemical space to abrogate the F3L-dsRNA interface of monkeypox virus to enhance the immune responses using molecular screening and free energy calculations. Front Pharmacol 2024; 14:1328308. [PMID: 38269277 PMCID: PMC10805857 DOI: 10.3389/fphar.2023.1328308] [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: 10/26/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024] Open
Abstract
Amid the ongoing monkeypox outbreak, there is an urgent need for the rapid development of effective therapeutic interventions capable of countering the immune evasion mechanisms employed by the monkeypox virus (MPXV). The evasion strategy involves the binding of the F3L protein to dsRNA, resulting in diminished interferon (IFN) production. Consequently, our current research focuses on utilizing virtual drug screening techniques to target the RNA binding domain of the F3L protein. Out of the 954 compounds within the South African natural compound database, only four demonstrated notable docking scores: -6.55, -6.47, -6.37, and -6.35 kcal/mol. The dissociation constant (KD) analysis revealed a stronger binding affinity of the top hits 1-4 (-5.34, -5.32, -5.29, and -5.36 kcal/mol) with the F3L in the MPXV. All-atom simulations of the top-ranked hits 1 to 4 consistently exhibited stable dynamics, suggesting their potential to interact effectively with interface residues. This was further substantiated through analyses of parameters such as radius of gyration (Rg), Root Mean Square Fluctuation, and hydrogen bonding. Cumulative assessments of binding free energy confirmed the top-performing candidates among all the compounds, with values of -35.90, -52.74, -28.17, and -32.11 kcal/mol for top hits 1-4, respectively. These results indicate that compounds top hit 1-4 could hold significant promise for advancing innovative drug therapies, suggesting their suitability for both in vivo and in vitro experiments.
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Affiliation(s)
- Muhammad Suleman
- Laboratory of Animal Research Center (LARC), Qatar University, Doha, Qatar
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Tanveer Ahmad
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Khadim shah
- Center for Biotechnology and Microbiology, University of Swat, Swat, Pakistan
| | - Norah A. Albekairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Abbas Khan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Dong-Qing Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Hadi M. Yassine
- Biomedical Research Center, Qatar University, Doha, Qatar
- College of Health Sciences-QU Health, Qatar University, Doha, Qatar
| | - Sergio Crovella
- Laboratory of Animal Research Center (LARC), Qatar University, Doha, Qatar
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Zari A, Kurdi LAF, Jaber FA, Alghamdi KMS, Zari TA, Bahieldin A, Hakeem KR, Alnahdi HS, Edris S, Ashraf GM. Investigation and drug design for novel molecules from natural products as inhibitors for controlling multiple myeloma disease using in-silico tools. J Biomol Struct Dyn 2024:1-16. [PMID: 38173181 DOI: 10.1080/07391102.2023.2300409] [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: 06/07/2023] [Accepted: 10/02/2023] [Indexed: 01/05/2024]
Abstract
Multiple myeloma (MM) is a disease that causes plasma cell growth in the bone marrow and immune globulin buildup in blood and urine. Despite recent advances in MM therapy, many still die due to its high mortality rate. A study using computational simulations analyzed 100 natural ingredients from the SANC database to determine if they inhibited the IgH domain, a known cause of multiple myeloma. Natural component Diospyrin inhibited the IgH enzyme with the best binding energy of -10.3 kcal/mol and three carbon-hydrogen bonds, followed by Parviflorone F complex with a binding energy of -10.1 kcal/mol and two conventional-hydrogen bonds. As a result, the Molecular Dynamic simulation was used to test the stability of the two complexes. During the simulation, the Diospyrin molecule dissociated from the protein at roughly 67.5 ns, whereas the Parviflorone F molecule stayed attached to the protein throughout. The latter was the subject of the investigation. The analysis of the production run data revealed that the Parviflorone F molecule exhibits a variety of conformations within the binding pocket while keeping a relatively constant distance from the protein's center of mass. The analysis of the production run data revealed that the Parviflorone F molecule exhibited a variety of conformations within the binding pocket while keeping a relatively constant distance from the protein's center of mass. The root mean square deviation (RMSD) plots for both the protein and complex showed a stable and steady average value of 4.4 Å for the first 82 nanoseconds of manufacture. As a result, the average value increased to 8.3 Å. Furthermore, the components of the binding free energy, as computed by MM-GBSA, revealed that the mean binding energy of the Parviflorone F molecule was -23.88 kcal/mol. Finally, after analyzing all of the examination data, Parviflorone F was identified as a powerful inhibitor of the IgH domain and hence of the MM disease, which requires further in-vivo conformation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ali Zari
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Lina A F Kurdi
- Department of Biology, College of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Fatima A Jaber
- Department of Biology, College of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Khalid M S Alghamdi
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Talal A Zari
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed Bahieldin
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetics, Faculty of Agriculture, Ain Shams University, Cairo, Egypt
| | - Khalid Rehman Hakeem
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Public Health, Daffodil International University, Dhaka, Bangladesh
| | - Hanan S Alnahdi
- Department of Biochemistry, College of Science, University of Jeddah, Saudi Arabia
| | - Sherif Edris
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetics, Faculty of Agriculture, Ain Shams University, Cairo, Egypt
- Al Borg Medical Laboratories, Jeddah, Saudi Arabia
| | - Ghulam Md Ashraf
- Department of Medical Laboratory Sciences, College of Health Sciences and Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
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Nurani A, Yamashita Y, Taki Y, Takada Y, Itoh Y, Suzuki T. Identification of a Histone Deacetylase 8 Inhibitor through Drug Screenings Based on Machine Learning. Chem Pharm Bull (Tokyo) 2024; 72:173-178. [PMID: 38296560 DOI: 10.1248/cpb.c23-00577] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Histone deacetylase 8 (HDAC8) is a zinc-dependent HDAC that catalyzes the deacetylation of nonhistone proteins. It is involved in cancer development and HDAC8 inhibitors are promising candidates as anticancer agents. However, most reported HDAC8 inhibitors contain a hydroxamic acid moiety, which often causes mutagenicity. Therefore, we used machine learning for drug screening and attempted to identify non-hydroxamic acids as HDAC8 inhibitors. In this study, we established a prediction model based on the random forest (RF) algorithm for screening HDAC8 inhibitors because it exhibited the best predictive accuracy in the training dataset, including data generated by the synthetic minority over-sampling technique (SMOTE). Using the trained RF-SMOTE model, we screened the Osaka University library for compounds and selected 50 virtual hits. However, the 50 hits in the first screening did not show HDAC8-inhibitory activity. In the second screening, using the RF-SMOTE model, which was established by retraining the dataset including 50 inactive compounds, we identified non-hydroxamic acid 12 as an HDAC8 inhibitor with an IC50 of 842 nM. Interestingly, its IC50 values for HDAC1 and HDAC3-inhibitory activity were 38 and 12 µM, respectively, showing that compound 12 has high HDAC8 selectivity. Using machine learning, we expanded the chemical space for HDAC8 inhibitors and identified non-hydroxamic acid 12 as a novel HDAC8 selective inhibitor.
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Delgado-Maldonado T, Gonzalez-Morales LD, Juarez-Saldivar A, Lara-Ramírez EE, Rojas-Verde G, Moreno-Rodriguez A, Bandyopadhyay D, Rivera G. Structure-based Virtual Screening from Natural Products as Inhibitors of SARS-CoV-2 Spike Protein and ACE2 Receptor Binding and their Biological Evaluation In vitro. Med Chem 2024; 20:546-553. [PMID: 38204279 DOI: 10.2174/0115734064279323231206091314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND In the last years, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused more than 760 million infections and 6.9 million deaths. Currently, remains a public health problem with limited pharmacological treatments. Among the virus drug targets, the SARS-CoV-2 spike protein attracts the development of new anti-SARS-CoV-2 agents. OBJECTIVE The aim of this work was to identify new compounds derived from natural products (BIOFACQUIM and Selleckchem databases) as potential inhibitors of the spike receptor binding domain (RBD)-ACE2 binding complex. METHODS Molecular docking, molecular dynamics simulations, and ADME-Tox analysis were performed to screen and select the potential inhibitors. ELISA-based enzyme assay was done to confirm our predictive model. RESULTS Twenty compounds were identified as potential binders of RBD of the spike protein. In vitro assay showed compound B-8 caused 48% inhibition at 50 μM, and their binding pattern exhibited interactions via hydrogen bonds with the key amino acid residues present on the RBD. CONCLUSION Compound B-8 can be used as a scaffold to develop new and more efficient antiviral drugs.
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Affiliation(s)
- Timoteo Delgado-Maldonado
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, 88710 Reynosa, México
| | - Luis Donaldo Gonzalez-Morales
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, 88710 Reynosa, México
| | - Alfredo Juarez-Saldivar
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, 88710 Reynosa, México
| | - Edgar E Lara-Ramírez
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, 88710 Reynosa, México
| | - Guadalupe Rojas-Verde
- Instituto de Biotecnología, Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León, Nuevo León CP. 66451, México
| | - Adriana Moreno-Rodriguez
- Laboratorio de Estudios Epidemiológicos, Clínicos, Diseños Experimentales e Investigación, Facultad de Ciencias Químicas, Universidad Autónoma "Benito Juárez" de Oaxaca, Avenida Universidad S/N, Ex Hacienda Cinco Señores, Oaxaca 68120, México
| | - Debasish Bandyopadhyay
- School of Integrative Biological and Chemical Sciences (SIBCS) and School of Earth, Environmental, and Marine Sciences (SEEMS), University of Texas Rio Grande Valley, Edinburg, Texas 78539, United States of America
| | - Gildardo Rivera
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, 88710 Reynosa, México
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Sampath S, Yadav SA, Meti M, Kaveri S, Subban R, Subramanyam R. Elucidation of binding mechanism of rhodanine derivative P4OC on bovine serum albumin. J Biomol Struct Dyn 2024; 42:475-482. [PMID: 36974960 DOI: 10.1080/07391102.2023.2194001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/15/2023] [Indexed: 03/29/2023]
Abstract
Rhodanine is an important scaffold in medicinal chemistry and it act as potent anticancer agent and other pharmacological effects. In pharmacokinetics and pharmacodynamics studies of the drug, the drug binding properties on serum protein is crucial for producing better drug. This study was designed to explore the binding interactions between the Rhodanine derivative (P4OC) on Bovine Serum Albumin (BSA). The interactions between P4OC and BSA were investigated using biophysical approach and molecular docking. The quenching mechanism and binding constants of P4OC on BSA were determined by biophysical approach through fluorescence spectroscopic experiments. Circular dichroism (CD) spectroscopy was used to study the secondary structural changes of BSA upon P4OC binding. The fluorescence experiments of P4OC binding on BSA show good drug binding with static quenching constants using stern Volmer plot and found the quenching constant value KP4OC = 1.12762 × 1013 M-1 with corresponding binding free energy (ΔG) -2.303 kcal/mol. The molecular displacement fluorescence emission on BSA-P4OC complex by site specific markers shows that P4OC binds at I A sub-domain of BSA further confirmed peak shift by synchronous fluorescence of P4OC on BSA with tyrosine, tryptophan and phenylalanine amino acids. Increasing concentration of P4OC on BSA found secondary structural changes, the percentage of α-helix was decreased as well increase percentage of β-sheet and random coil. The binding of P4OC to BSA was computationally studied by molecular docking methods. Thus, results obtained are in excellent agreement with experimental and theoretical results with respect to the binding mechanism and binding constant of P4OC on BSA. We concluded that, the rhodanine derivative P4OC possesses good drug binding properties on BSA. Further P4OC may be evaluated its potential pharmacological activities on clinical trial.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sakthiprabha Sampath
- Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
| | | | - Manjunath Meti
- Department of Plant Sciences, University of Hyderabad, Gachibowli, Hyderabad, India
| | - Sundaram Kaveri
- Department of Chemistry, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
| | - Ravi Subban
- Department of Chemistry, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
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Nazli A, Qiu J, Tang Z, He Y. Recent Advances and Techniques for Identifying Novel Antibacterial Targets. Curr Med Chem 2024; 31:464-501. [PMID: 36734893 DOI: 10.2174/0929867330666230123143458] [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: 05/24/2022] [Revised: 10/30/2022] [Accepted: 11/11/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly. METHODS In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification. RESULTS Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well. CONCLUSION The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.
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Affiliation(s)
- Adila Nazli
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
| | - Jingyi Qiu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Ziyi Tang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China
| | - Yun He
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [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: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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48
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Tavares CA, Santos TMR, Gonçalves MA, da Cunha EFF, Ramalho TC. Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior? Mini Rev Med Chem 2024; 24:1063-1069. [PMID: 38258786 DOI: 10.2174/0113895575250433231103063707] [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: 02/22/2023] [Revised: 08/01/2023] [Accepted: 09/14/2023] [Indexed: 01/24/2024]
Abstract
Since its early days in the 19th century, medicinal chemistry has concentrated its efforts on the treatment of diseases, using tools from areas such as chemistry, pharmacology, and molecular biology. The understanding of biological mechanisms and signaling pathways is crucial information for the development of potential agents for the treatment of diseases mainly because they are such complex processes. Given the limitations that the experimental approach presents, computational chemistry is a valuable alternative for the study of these systems and their behavior. Thus, classical molecular dynamics, based on Newton's laws, is considered a technique of great accuracy, when appropriated force fields are used, and provides satisfactory contributions to the scientific community. However, as many configurations are generated in a large MD simulation, methods such as Statistical Inefficiency and Optimal Wavelet Signal Compression Algorithm are great tools that can reduce the number of subsequent QM calculations. Accordingly, this review aims to briefly discuss the importance and relevance of medicinal chemistry allied to computational chemistry as well as to present a case study where, through a molecular dynamics simulation of AMPK protein (50 ns) and explicit solvent (TIP3P model), a minimum number of snapshots necessary to describe the oscillation profile of the protein behavior was proposed. For this purpose, the RMSD calculation, together with the sophisticated OWSCA method was used to propose the minimum number of snapshots.
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Affiliation(s)
- Camila A Tavares
- Laboratory of Molecular Modelling, Department of Chemistry, Federal University of Lavras, Lavras - MG, 37200-000, Brazil
| | - Taináh M R Santos
- Laboratory of Molecular Modelling, Department of Chemistry, Federal University of Lavras, Lavras - MG, 37200-000, Brazil
| | - Mateus A Gonçalves
- Laboratory of Molecular Modelling, Department of Chemistry, Federal University of Lavras, Lavras - MG, 37200-000, Brazil
| | - Elaine F F da Cunha
- Laboratory of Molecular Modelling, Department of Chemistry, Federal University of Lavras, Lavras - MG, 37200-000, Brazil
| | - Teodorico C Ramalho
- Laboratory of Molecular Modelling, Department of Chemistry, Federal University of Lavras, Lavras - MG, 37200-000, Brazil
- Department of Chemistry, Faculty of Science, University of Hradec Králové, Hradec Králové, 500 03, Czech Republic
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Liu K, Tong J, Liu X, Liang D, Ren F, Jiang N, Hao Z, Li S, Wang Q. The Discovery of Novel Agents against Staphylococcus aureus by Targeting Sortase A: A Combination of Virtual Screening and Experimental Validation. Pharmaceuticals (Basel) 2023; 17:58. [PMID: 38256891 PMCID: PMC11100315 DOI: 10.3390/ph17010058] [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: 11/15/2023] [Revised: 12/12/2023] [Accepted: 12/16/2023] [Indexed: 01/24/2024] Open
Abstract
Staphylococcus aureus (S. aureus), commonly known as "superbugs", is a highly pathogenic bacterium that poses a serious threat to human health. There is an urgent need to replace traditional antibiotics with novel drugs to combat S. aureus. Sortase A (SrtA) is a crucial transpeptidase involved in the adhesion process of S. aureus. The reduction in virulence and prevention of S. aureus infections have made it a significant target for antimicrobial drugs. In this study, we combined virtual screening with experimental validation to identify potential drug candidates from a drug library. Three hits, referred to as Naldemedine, Telmisartan, and Azilsartan, were identified based on docking binding energy and the ratio of occupied functional sites of SrtA. The stability analysis manifests that Naldemedine and Telmisartan have a higher binding affinity to the hydrophobic pockets. Specifically, Telmisartan forms stable hydrogen bonds with SrtA, resulting in the highest binding energy. Our experiments prove that the efficiency of adhesion and invasion by S. aureus can be decreased without significantly affecting bacterial growth. Our work identifies Telmisartan as the most promising candidate for inhibiting SrtA, which can help combat S. aureus infection.
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Affiliation(s)
- Kang Liu
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China; (K.L.); (J.T.); (D.L.); (F.R.); (N.J.); (Z.H.)
| | - Jiangbo Tong
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China; (K.L.); (J.T.); (D.L.); (F.R.); (N.J.); (Z.H.)
| | - Xu Liu
- College of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou 225009, China;
| | - Dan Liang
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China; (K.L.); (J.T.); (D.L.); (F.R.); (N.J.); (Z.H.)
| | - Fangzhe Ren
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China; (K.L.); (J.T.); (D.L.); (F.R.); (N.J.); (Z.H.)
| | - Nan Jiang
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China; (K.L.); (J.T.); (D.L.); (F.R.); (N.J.); (Z.H.)
| | - Zhenyu Hao
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China; (K.L.); (J.T.); (D.L.); (F.R.); (N.J.); (Z.H.)
| | - Shixin Li
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China; (K.L.); (J.T.); (D.L.); (F.R.); (N.J.); (Z.H.)
| | - Qiang Wang
- Department of the Heart and Great Vessels, Affiliated Hospital of Yangzhou University, Yangzhou 225009, China
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Subaramaniyam U, Ramalingam D, Balan R, Paital B, Sar P, Ramalingam N. Annonaceous acetogenins as promising DNA methylation inhibitors to prevent and treat leukemogenesis - an in silico approach. J Biomol Struct Dyn 2023:1-14. [PMID: 38149859 DOI: 10.1080/07391102.2023.2297010] [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: 09/06/2023] [Accepted: 12/10/2023] [Indexed: 12/28/2023]
Abstract
Leukemia is a haematological malignancy affecting blood and bone marrow, ranking 10th among the other common cancers. DNA methylation is an epigenetic dysregulation that plays a critical role in leukemogenesis. DNA methyltransferases (DNMTs) such as DNMT1, DNMT3A and DNMT3B are the key enzymes catalysing DNA methylation. Inhibition of DNMT1 with secondary metabolites from medicinal plants helps reverse DNA methylation. The present study focuses on inhibiting DNMT1 protein (PDB ID: 3PTA) with annonaceous acetogenins through in-silico studies. The docking and molecular dynamic (MD) simulation study was carried out using Schrödinger Maestro and Desmond, respectively. These compounds' drug likeliness, ADMET properties and bioactivity scores were analysed. About 76 different acetogenins were chosen for this study, among which 17 showed the highest binding energy in the range of -8.312 to -10.266 kcal/mol. The compounds with the highest negative binding energy were found to be annohexocin (-10.266 kcal/mol), isoannonacinone (-10.209 kcal/mol) and annonacin (-9.839 kcal/mol). MD simulation results reveal that annonacin remains stable throughout the simulation time of 100 ns and also binds to the catalytic domain of DNMT1 protein. From the above results, it can be concluded that annonacin has the potential to inhibit the DNA methylation process and prevent leukemogenesis.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Udayadharshini Subaramaniyam
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
| | - Divya Ramalingam
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
| | - Ranjini Balan
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
| | - Biswaranjan Paital
- Redox Regulation Laboratory, Department of Zoology, College of Basic Science and Humanities, Odisha University of Agriculture and Technology, Bhubaneswar, India
| | - Pranati Sar
- Biotechnology Department, Silver Oak Institute of Science, Silver Oak University, Ahmedabad, India
| | - Nirmaladevi Ramalingam
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
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