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Zhou Y, Jiang Y, Chen SJ. SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening. J Chem Theory Comput 2024. [PMID: 39150889 DOI: 10.1021/acs.jctc.4c00681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2024]
Abstract
The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States
| | - Yangwei Jiang
- Department of Physics and Astronomy, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States
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Li X, Zhang F, Zheng L, Guo J. Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction. JOURNAL OF HAZARDOUS MATERIALS 2024; 475:134828. [PMID: 38876015 DOI: 10.1016/j.jhazmat.2024.134828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/09/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024]
Abstract
The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor performance and limited predictive capabilities. In this study, we propose a novel approach for ecological toxicity assessment based on pre-trained models. By leveraging pre-training techniques and graph neural network models, we establish a highperformance predictive model. Furthermore, we incorporate a variational autoencoder to optimize the model, enabling simultaneous discrimination of toxicity to bees and molecular degradability. Additionally, despite the low similarity between the endogenous hormones in bees and the compounds in our dataset, our model confidently predicts that these hormones are non-toxic to bees, which further strengthens the credibility and accuracy of our model. We also discovered the negative correlation between the degradation and bee toxicity of compounds. In summary, this study presents an ecological toxicity assessment model with outstanding performance. The proposed model accurately predicts the toxicity of chemicals to bees and their degradability capabilities, offering valuable technical support to relevant fields.
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Affiliation(s)
- Xinkang Li
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
| | - Feng Zhang
- College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China
| | - Liangzhen Zheng
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; Zelixir Biotech Company Ltd. Shanghai, China.
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao.
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3
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Qandeel BM, Mowafy S, Abouzid K, Farag NA. Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations. BMC Chem 2024; 18:14. [PMID: 38245752 PMCID: PMC10800075 DOI: 10.1186/s13065-023-01110-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/21/2023] [Indexed: 01/22/2024] Open
Abstract
Undecaprenyl Pyrophosphate Synthase (UPPS) is a vital target enzyme in the early stages of bacterial cell wall biosynthesis. UPPS inhibitors have antibacterial activity against resistant strains such as MRSA and VRE. In this study, we used several consecutive computer-based protocols to identify novel UPPS inhibitors. The 3D QSAR pharmacophore model generation (HypoGen algorithm) protocol was used to generate a valid predictive pharmacophore model using a set of UPPS inhibitors with known reported activity. The developed model consists of four pharmacophoric features: one hydrogen bond acceptor, two hydrophobic, and one aromatic ring. It had a correlation coefficient of 0.86 and a null cost difference of 191.39, reflecting its high predictive power. Hypo1 was proven to be statistically significant using Fischer's randomization at a 95% confidence level. The validated pharmacophore model was used for the virtual screening of several databases. The resulting hits were filtered using SMART and Lipinski filters. The hits were docked into the binding site of the UPPS protein, affording 70 hits with higher docking affinities than the reference compound (6TC, - 21.17 kcal/mol). The top five hits were selected through extensive docking analysis and visual inspection based on docking affinities, fit values, and key residue interactions with the UPPS receptor. Moreover, molecular dynamic simulations of the top hits were performed to confirm the stability of the protein-ligand complexes, yielding five promising novel UPPS inhibitors.
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Affiliation(s)
- Basma M Qandeel
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Km28 Cairo-Ismailia Road, Ahmed Orabi District, Cairo, Egypt.
| | - Samar Mowafy
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Km28 Cairo-Ismailia Road, Ahmed Orabi District, Cairo, Egypt
| | - Khaled Abouzid
- Department of Pharmaceutical Chemistry, College of Pharmacy, Ain-Shams University, Abbasia, 11566, Egypt
| | - Nahla A Farag
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Misr International University, Km28 Cairo-Ismailia Road, Ahmed Orabi District, Cairo, Egypt.
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4
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Nascimento IJDS, de Aquino TM, da Silva-Júnior EF. The New Era of Drug Discovery: The Power of Computer-aided Drug
Design (CADD). LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180819666220405225817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Abstract:
Drug design and discovery is a process that requires high financial costs and is timeconsuming.
For many years, this process focused on empirical pharmacology. However, over the years,
the target-based approach allowed a significant discovery in this field, initiating the rational design era. In
view, to decrease the time and financial cost, rational drug design is benefited by increasing computer
engineering and software development, and computer-aided drug design (CADD) emerges as a promising
alternative. Since the 1970s, this approach has been able to identify many important and revolutionary
compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified
through this approach have shown their importance, being CADD essential in any drug discovery campaign.
Thus, this perspective will present the prominent successful cases utilizing this approach and entering
into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics,
using high-performance computing with molecular dynamics protocols faster and more effectively.
In addition, artificial intelligence and machine learning will be the next process in the rational design
of new drugs. Here, we hope that this paper generates new ideas and instigates research groups
worldwide to use these methods and stimulate progress in drug design.
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Affiliation(s)
| | | | - Edeildo Ferreira da Silva-Júnior
- Chemistry and Biotechnology Institute, Federal University of Alagoas, Maceió, Brazil
- Laboratory of Medicinal
Chemistry, Pharmaceutical Sciences Institute, Federal University of Alagoas, Maceió, Brazil
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Zheng Q, Zhang W, Rao GW. Protein Lysine Methyltransferase SMYD2: A Promising Small Molecule Target for Cancer Therapy. J Med Chem 2022; 65:10119-10132. [PMID: 35914250 DOI: 10.1021/acs.jmedchem.2c00325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In epigenetic research, the abnormality of protein methylation modification is closely related to the occurrence and development of tumors, which stimulates the interest of researchers in protein methyltransferase research and the efforts to develop corresponding specific small molecule inhibitors. Currently, the protein lysine methyltransferase SMYD2 has been identified as a promising new small molecule target for cancer therapy. But its biological functions have not been fully studied and relatively few inhibitors have been reported, thus this field needs to be further explored. This perspective provides a comprehensive and systematic review of the available resources in this field, including its research status, biological structure, related substrates and methylation mechanisms, and research status of inhibitors. In addition, this perspective elaborates in detail the current challenges in this field, our insights into what needs to be done next, rational drug design of novel SMYD2 inhibitors, and foreseeable development directions in the future.
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Affiliation(s)
- Quan Zheng
- College of Pharmaceutical Science, Zhejiang University of Technology, and Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Hangzhou 310014, China
| | - Wen Zhang
- College of Pharmaceutical Science, Zhejiang University of Technology, and Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Hangzhou 310014, China
| | - Guo-Wu Rao
- College of Pharmaceutical Science, Zhejiang University of Technology, and Institute of Drug Development & Chemical Biology, Zhejiang University of Technology, Hangzhou 310014, China
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Design, synthesis and molecular docking studies of some 1-(5-(2-fluoro-5-(trifluoromethoxy)phenyl)-1,2,4-oxadiazol-3-yl)piperazine derivatives as potential anti-inflammatory agents. Mol Divers 2021; 26:2893-2905. [PMID: 34817768 DOI: 10.1007/s11030-021-10340-1] [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: 06/08/2021] [Accepted: 10/11/2021] [Indexed: 10/19/2022]
Abstract
We herein report the facile synthesis of a series of 3,5-substituted-1,2,4-oxadiazole derivatives in good to excellent yields. The anti-inflammatory potential of the newly synthesized compounds was evaluated by anti-denaturation assay using diclofenac sodium as the reference standard. Some of the compounds exhibited profound activity profile when compared to the standard drug. The molecular docking and SAR studies were carried out at the later stage for gaining more insights about the promising activity profile of the synthesized molecules.
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Berdigaliyev N, Aljofan M. An overview of drug discovery and development. Future Med Chem 2020; 12:939-947. [PMID: 32270704 DOI: 10.4155/fmc-2019-0307] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 02/18/2020] [Indexed: 01/01/2023] Open
Abstract
A new medicine will take an average of 10-15 years and more than US$2 billion before it can reach the pharmacy shelf. Traditionally, drug discovery relied on natural products as the main source of new drug entities, but was later shifted toward high-throughput synthesis and combinatorial chemistry-based development. New technologies such as ultra-high-throughput drug screening and artificial intelligence are being heavily employed to reduce the cost and the time of early drug discovery, but they remain relatively unchanged. However, are there other potentially faster and cheaper means of drug discovery? Is drug repurposing a viable alternative? In this review, we discuss the different means of drug discovery including their advantages and disadvantages.
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Affiliation(s)
- Nurken Berdigaliyev
- Department of biomedical Science, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan
| | - Mohamad Aljofan
- Department of biomedical Science, Nazarbayev University School of Medicine, Nur-Sultan 010000, Kazakhstan
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Ma Y, Li HL, Chen XB, Jin WY, Zhou H, Ma Y, Wang RL. 3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B. Comput Biol Chem 2018; 73:1-12. [PMID: 29413811 DOI: 10.1016/j.compbiolchem.2018.01.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2017] [Revised: 01/06/2018] [Accepted: 01/17/2018] [Indexed: 11/19/2022]
Abstract
Owing to its fundamental roles in cell cycle phases, the cell division cycle 25B (CDC25B) was broadly considered as potent clinical drug target for cancers. In this study, 3D QSAR pharmacophore models for CDC25B inhibitors were developed by the module of Hypogen. Three methods (cost analysis, test set prediction, and Fisher's test) were applied to validate that the models could be used to predict the biological activities of compounds. Subsequently, 26 compounds satisfied Lipinski's rule of five were obtained by the virtual screening of the Hypo-1-CDC25B against ZINC databases. It was then discovered that 9 identified molecules had better binding affinity than a known CDC25B inhibitors-compound 1 using docking studies. The molecular dynamics simulations showed that the compound had favorable conformations for binding to the CDC25B. Thus, our findings here would be helpful to discover potent lead compounds for the treatment of cancers.
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Affiliation(s)
- Ying Ma
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China
| | - Hong-Lian Li
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China
| | - Xiu-Bo Chen
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China; Eye Hospital, Tianjin Medical University, School of Optometry and Ophthalmology, Tianjin Medical University, China
| | - Wen-Yan Jin
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China
| | - Hui Zhou
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China
| | - Ying Ma
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China.
| | - Run-Ling Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, 300070, China.
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Abstract
CONSPECTUS: The role dynamics plays in proteins is of intense contemporary interest. Fundamental insights into how dynamics affects reactivity and product distributions will facilitate the design of novel catalysts that can produce high quality compounds that can be employed, for example, as fuels and life saving drugs. We have used molecular dynamics (MD) methods and combined quantum mechanical/molecular mechanical (QM/MM) methods to study a series of proteins either whose substrates are too far away from the catalytic center or whose experimentally resolved substrate binding modes cannot explain the observed product distribution. In particular, we describe studies of farnesyl transferase (FTase) where the farnesyl pyrophosphate (FPP) substrate is ∼8 Å from the zinc-bound peptide in the active site of FTase. Using MD and QM/MM studies, we explain how the FPP substrate spans the gulf between it and the active site, and we have elucidated the nature of the transition state (TS) and offered an alternate explanation of experimentally observed kinetic isotope effects (KIEs). Our second story focuses on the nature of substrate dynamics in the aromatic prenyltransferase (APTase) protein NphB and how substrate dynamics affects the observed product distribution. Through the examples chosen we show the power of MD and QM/MM methods to provide unique insights into how protein substrate dynamics affects catalytic efficiency. We also illustrate how complex these reactions are and highlight the challenges faced when attempting to design de novo catalysts. While the methods used in our previous studies provided useful insights, several clear challenges still remain. In particular, we have utilized a semiempirical QM model (self-consistent charge density functional tight binding, SCC-DFTB) in our QM/MM studies since the problems we were addressing required extensive sampling. For the problems illustrated, this approach performed admirably (we estimate for these systems an uncertainty of ∼2 kcal/mol), but it is still a semiempirical model, and studies of this type would benefit greatly from more accurate ab initio or DFT models. However, the challenge with these methods is to reach the level of sampling needed to study systems where large conformational changes happen in the many nanoseconds to microsecond time regimes. Hence, how to couple expensive and accurate QM methods with sophisticated sampling algorithms is an important future challenge especially when large-scale studies of catalyst design become of interest. The use of MD and QM/MM models to elucidate enzyme catalytic pathways and to design novel catalytic agents is in its infancy but shows tremendous promise. While this Account summarizes where we have been, we also discuss briefly future directions that improve our fundamental ability to understand enzyme catalysis.
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Affiliation(s)
- Dhruva K. Chakravorty
- Department of Chemistry, 2000 Lakeshore Drive, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Kenneth M. Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing Michigan 48824-1322, United States
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Abstract
Conspectus Quantum mechanics (QM) has revolutionized our understanding of the structure and reactivity of small molecular systems. Given the tremendous impact of QM in this research area, it is attractive to believe that this could also be brought into the biological realm where systems of a few thousand atoms and beyond are routine. Applying QM methods to biological problems brings an improved representation to these systems by the direct inclusion of inherently QM effects such as polarization and charge transfer. Because of the improved representation, novel insights can be gleaned from the application of QM tools to biomacromolecules in aqueous solution. To achieve this goal, the computational bottlenecks of QM methods had to be addressed. In semiempirical theory, matrix diagonalization is rate limiting, while in density functional theory or Hartree-Fock theory electron repulsion integral computation is rate-limiting. In this Account, we primarily focus on semiempirical models where the divide and conquer (D&C) approach linearizes the matrix diagonalization step with respect to the system size. Through the D&C approach, a number of applications to biological problems became tractable. Herein, we provide examples of QM studies on biological systems that focus on protein solvation as viewed by QM, QM enabled structure-based drug design, and NMR and X-ray biological structure refinement using QM derived restraints. Through the examples chosen, we show the power of QM to provide novel insights into biological systems, while also impacting practical applications such as structure refinement. While these methods can be more expensive than classical approaches, they make up for this deficiency by the more realistic modeling of the electronic nature of biological systems and in their ability to be broadly applied. Of the tools and applications discussed in this Account, X-ray structure refinement using QM models is now generally available to the community in the refinement package Phenix. While the power of this approach is manifest, challenges still remain. In particular, QM models are generally applied to static structures, so ways in which to include sampling is an ongoing challenge. Car-Parrinello or Born-Oppenheimer molecular dynamics approaches address the short time scale sampling issue, but how to effectively use QM to study phenomenon covering longer time scales will be the focus of future research. Finally, how to accurately and efficiently include electron correlation effects to facilitate the modeling of, for example, dispersive interactions, is also a major hurdle that a broad range of groups are addressing The use of QM models in biology is in its infancy, leading to the expectation that the most significant use of these tools to address biological problems will be seen in the coming years. It is hoped that while this Account summarizes where we have been, it will also help set the stage for future research directions at the interface of quantum mechanics and biology.
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Affiliation(s)
- Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University , 578 S. Shaw Lane, East Lansing Michigan 48824-1322, United States
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Subramanian G, Rao SN. Comprehending renin inhibitor's binding affinity using structure-based approaches. Bioorg Med Chem Lett 2013; 23:6667-72. [PMID: 24239018 DOI: 10.1016/j.bmcl.2013.10.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2013] [Revised: 10/19/2013] [Accepted: 10/22/2013] [Indexed: 10/26/2022]
Abstract
The performance of several structure-based design (SBD) approaches in predicting the binding affinity of diverse small molecule inhibitors co-crystallized to human renin was assessed to ascertain the modeling tool and method of choice required when dealing with structure-based lead optimization projects. Most of the SBD approaches investigated here were able to provide qualitative guidance, but quantitative accuracy as well as decisive discrimination between [in]actives is still not within reach. Such an outcome suggests that the current methods need improvement to capture the overall physics of the binding phenomenon for consistent applications in a lead optimization setting.
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