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Chen L, Gu R, Li Y, Liu H, Han W, Yan Y, Chen Y, Zhang Y, Jiang Y. Epigenetic target identification strategy based on multi-feature learning. J Biomol Struct Dyn 2024; 42:5946-5962. [PMID: 37827992 DOI: 10.1080/07391102.2023.2259511] [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: 01/16/2023] [Accepted: 06/20/2023] [Indexed: 10/14/2023]
Abstract
The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Lingfeng Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Rui Gu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yuanyuan Li
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Weijie Han
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yingchao Yan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yulei Jiang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
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Zeng X, Li SJ, Lv SQ, Wen ML, Li Y. A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning. Front Pharmacol 2024; 15:1375522. [PMID: 38628639 PMCID: PMC11019008 DOI: 10.3389/fphar.2024.1375522] [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: 01/24/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control and Prevention, Dali, China
| | - Shuang-Qing Lv
- Institute of Surveying and Information Engineering West Yunnan University of Applied Science, Dali, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, China
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3
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Altiner P, Çınaroğlu SS, Timucin AC, Timucin E. Computational completion of the Aurora interaction region of N-Myc in the Aurora a kinase complex. Sci Rep 2023; 13:18399. [PMID: 37884585 PMCID: PMC10603048 DOI: 10.1038/s41598-023-45272-3] [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: 07/26/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
Inhibiting protein-protein interactions of the Myc family is a viable pharmacological strategy for modulation of the levels of Myc oncoproteins in cancer. Aurora A kinase (AurA) and N-Myc interaction is one of the most attractive targets of this strategy because formation of this complex blocks proteasomal degradation of N-Myc in neuroblastoma. Two crystallization studies have captured this complex (PDB IDs: 5g1x, 7ztl), partially resolving the AurA interaction region (AIR) of N-Myc. Prompted by the missing N-Myc fragment in these crystal structures, we modeled the complete structure between AurA and N-Myc, and comprehensively analyzed how the incomplete and complete N-Myc behave in complex by molecular dynamics simulations. Molecular dynamics simulations of the incomplete PDB complex (5g1x) repeatedly showed partial dissociation of the short N-Myc fragment (61-89) from the kinase. The missing N-Myc (19-60) fragment was modeled utilizing the N-terminal lobe of AurA as the protein-protein interaction surface, wherein TPX2, a well-known partner of AurA, also binds. Binding free energy calculations along with flexibility analysis confirmed that the complete AIR of N-Myc stabilizes the complex, accentuating the N-terminal lobe of AurA as a binding site for the missing N-Myc fragment (19-60). We further generated additional models consisting of only the missing N-Myc (19-60), and the fused form of TPX2 (7-43) and N-Myc (61-89). These partners also formed more stable interactions with the N-terminal lobe of AurA than did the incomplete N-Myc fragment (61-89) in the 5g1x complex. Altogether, this study provides structural insights into the involvement of the N-terminus of the AIR of N-Myc and the N-terminal lobe of AurA in formation of a stable complex, reflecting its potential for effective targeting of N-Myc.
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Affiliation(s)
- Pinar Altiner
- Institut de Pharmacologie et de Biologie Structurale (IPBS), CNRS, Université Toulouse III - Paul Sabatier (UT3), 31077, Toulouse, France
| | | | - Ahmet Can Timucin
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Acibadem University, 34752, Istanbul, Turkey.
| | - Emel Timucin
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem University, 34752, Istanbul, Turkey.
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Li J, Liu Y, Liu D, Xu T, Zhang C, Li J, Wang ZA, Du Y. In Silico Selection and Validation of High-Affinity ssDNA Aptamers Targeting Paromomycin. Anal Chem 2023. [PMID: 37384819 DOI: 10.1021/acs.analchem.3c01575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Glycans are promising for disease diagnosis since glycan biosynthesis is significantly affected by disease states, and glycosylation changes are probably more pronounced than protein expression during the transformation to the diseased condition. Glycan-specific aptamers can be developed for challenging applications such as cancer targeting; however, the high flexibility of glycosidic bonds and scarcity of studies on glycan-aptamer binding mechanisms increased the difficulty of screening. In this work, the model of interactions between glycans and ssDNA aptamers synthesized based on the sequence of rRNA genes was developed. Our simulation-based approach revealed that paromomycin as a representative example of glycans is preferred to bind base-restricted stem structures of aptamers because they are more critical in stabilizing the flexible structures of glycans. Combined experiments and simulations have identified two optimal mutant aptamers. Our work would provide a potential strategy that the glycan-binding rRNA genes could act as the initial aptamer pools to accelerate aptamer screening. In addition, this in silico workflow would be potentially applied in the more extensive in vitro development and application of RNA-templated ssDNA aptamers targeting glycans.
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Affiliation(s)
- Jiaqing Li
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, 100190 Beijing, China
- School of Chemical Engineering, University of Chinese Academy of Sciences, No.19A Yuquan Road, Shijingshan District, 100049 Beijing, China
| | - Yangyang Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Dongdong Liu
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, 100190 Beijing, China
| | - Tong Xu
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, 100190 Beijing, China
- School of Chemical Engineering, University of Chinese Academy of Sciences, No.19A Yuquan Road, Shijingshan District, 100049 Beijing, China
| | - Chen Zhang
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, 100190 Beijing, China
| | - Jianjun Li
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, 100190 Beijing, China
| | - Zhuo A Wang
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, 100190 Beijing, China
| | - Yuguang Du
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North 2nd Street, Zhongguancun, Haidian District, 100190 Beijing, China
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Yue J, Li Y, Li F, Zhang P, Li Y, Xu J, Zhang Q, Zhang C, He X, Wang Y, Liu Z. Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments. Comput Biol Med 2023; 152:106350. [PMID: 36493735 DOI: 10.1016/j.compbiomed.2022.106350] [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: 09/13/2022] [Revised: 11/11/2022] [Accepted: 11/23/2022] [Indexed: 11/27/2022]
Abstract
As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC50s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.
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Affiliation(s)
- Jianda Yue
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Yaqi Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Fengjiao Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Peng Zhang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Yimin Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Jiawei Xu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Qianqian Zhang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Cheng Zhang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China
| | - Xiao He
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China; New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai, 200062, China
| | - Ying Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China.
| | - Zhonghua Liu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China.
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Yu YX, Wang W, Sun HB, Zhang LL, Wang LF, Yin YY. Decoding drug resistant mechanism of V32I, I50V and I84V mutations of HIV-1 protease on amprenavir binding by using molecular dynamics simulations and MM-GBSA calculations. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:805-831. [PMID: 36322686 DOI: 10.1080/1062936x.2022.2140708] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
Mutations V32I, I50V and I84V in the HIV-1 protease (PR) induce drug resistance towards drug amprenavir (APV). Multiple short molecular dynamics (MSMD) simulations and molecular mechanics generalized Born surface area (MM-GBSA) method were utilized to investigate drug-resistant mechanism of V32I, I50V and I84V towards APV. Dynamic information arising from MSMD simulations suggest that V32I, I50V and I84V highly affect structural flexibility, motion modes and conformational behaviours of two flaps in the PR. Binding free energies calculated by MM-GBSA method suggest that the decrease in binding enthalpy and the increase in binding entropy induced by mutations V32I, I50V and I84V are responsible for drug resistance of the mutated PRs on APV. The energetic contributions of separate residues on binding of APV to the PR show that V32I, I50V and I84V highly disturb the interactions of two flaps with APV and mostly drive the decrease in binding ability of APV to the PR. Thus, the conformational changes of two flaps in the PR caused by V32I, I50V and I84V play key roles in drug resistance of three mutated PR towards APV. This study can provide useful dynamics information for the design of potent inhibitors relieving drug resistance.
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Affiliation(s)
- Y X Yu
- School of Science, Shandong Jiaotong University, Jinan, China
| | - W Wang
- School of Science, Shandong Jiaotong University, Jinan, China
| | - H B Sun
- School of Science, Shandong Jiaotong University, Jinan, China
| | - L L Zhang
- School of Science, Shandong Jiaotong University, Jinan, China
| | - L F Wang
- School of Science, Shandong Jiaotong University, Jinan, China
| | - Y Y Yin
- School of Science, Shandong Jiaotong University, Jinan, China
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7
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Yalçin-Özkat G. Computational studies with flavonoids and terpenoids as BRPF1 inhibitors: in silico biological activity prediction, molecular docking, molecular dynamics simulations, MM/PBSA calculations. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:533-550. [PMID: 35822928 DOI: 10.1080/1062936x.2022.2096113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
The BRPF1 protein is encoded by the BRPF1 gene. In addition, the BRPF1 gene is known to be upregulated in leukaemia. Recent studies have shown that it is also overexpressed in hepatocellular carcinoma (HCC) as well. Therefore, BRPF1 is a significant target for anti-cancer drug development studies, especially on HCC. 40 terpenoids and flavonoids were chosen because of their anticancer properties given in the literature. In this study, the biological activity of molecules was also investigated with in silico structure-activity relationship analysis. In addition, interactions between a series of terpenoids and flavonoids and the BRPF1 protein were investigated by molecular docking and molecular dynamics simulations. The energy change caused by the interactions of BRPF1 with different compounds was also evaluated by MM/PBSA calculations. It has been revealed that compound 5 (-9.2 kcal/mol), a kind of secoclerodane type diterpenoid, has a higher affinity both compared to other flavonoids and terpenoids, and 9F9 (-7.9 kcal/mol), a selective BRPF1 inhibitor. The study presented in this article demonstrates that compound 5, as a natural product, could form a chemical scaffold for the development of selective BRPF1 bromodomain inhibitors.
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Affiliation(s)
- G Yalçin-Özkat
- Max Planck Institute for Dynamics of Complex Technical Systems, Molecular Simulations and Design Group, Magdeburg, Germany
- Bioengineering Department, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, Rize, Turkey
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8
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Wang J, Ishchenko A, Zhang W, Razavi A, Langley D. A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein-protein and protein-ligand binding potencies. Sci Rep 2022; 12:2024. [PMID: 35132139 PMCID: PMC8821539 DOI: 10.1038/s41598-022-05875-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/17/2022] [Indexed: 12/13/2022] Open
Abstract
Although seeking to develop a general and accurate binding free energy calculation method for protein-protein and protein-ligand interactions has been a continuous effort for decades, only limited successes have been obtained so far. Here, we report the development of a metadynamics-based procedure that calculates Dissociation Free Energy (DFE) and its application to 19 non-congeneric protein-protein complexes and hundreds of protein-ligand complexes covering eight targets. We achieved very high correlations in comparison to experimental binding free energies for these diverse sets of systems, demonstrating the generality and accuracy of the method. Since structures of most proteins are available owing to the recent success of prediction by artificial intelligence, a general free energy method such as DFE, combined with other methods, can make structure-based drug design a widely viable and reliable solution to develop both traditional small molecule drugs and biologic drugs as well as PROTACS.
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Affiliation(s)
- Jing Wang
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA.
| | | | - Wei Zhang
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
| | - Asghar Razavi
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
| | - David Langley
- Arvinas, Inc., 5 Science Park, New Haven, CT, 06511, USA
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Pharmacophore-Based Virtual Screening, Quantum Mechanics Calculations, and Molecular Dynamics Simulation Approaches Identified Potential Natural Antiviral Drug Candidates against MERS-CoV S1-NTD. Molecules 2021; 26:molecules26164961. [PMID: 34443556 PMCID: PMC8401589 DOI: 10.3390/molecules26164961] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 11/16/2022] Open
Abstract
Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly infectious zoonotic virus first reported into the human population in September 2012 on the Arabian Peninsula. The virus causes severe and often lethal respiratory illness in humans with an unusually high fatality rate. The N-terminal domain (NTD) of receptor-binding S1 subunit of coronavirus spike (S) proteins can recognize a variety of host protein and mediates entry into human host cells. Blocking the entry by targeting the S1-NTD of the virus can facilitate the development of effective antiviral drug candidates against the pathogen. Therefore, the study has been designed to identify effective antiviral drug candidates against the MERS-CoV by targeting S1-NTD. Initially, a structure-based pharmacophore model (SBPM) to the active site (AS) cavity of the S1-NTD has been generated, followed by pharmacophore-based virtual screening of 11,295 natural compounds. Hits generated through the pharmacophore-based virtual screening have re-ranked by molecular docking and further evaluated through the ADMET properties. The compounds with the best ADME and toxicity properties have been retrieved, and a quantum mechanical (QM) based density-functional theory (DFT) has been performed to optimize the geometry of the selected compounds. Three optimized natural compounds, namely Taiwanhomoflavone B (Amb23604132), 2,3-Dihydrohinokiflavone (Amb23604659), and Sophoricoside (Amb1153724), have exhibited substantial docking energy >-9.00 kcal/mol, where analysis of frontier molecular orbital (FMO) theory found the low chemical reactivity correspondence to the bioactivity of the compounds. Molecular dynamics (MD) simulation confirmed the stability of the selected natural compound to the binding site of the protein. Additionally, molecular mechanics generalized born surface area (MM/GBSA) predicted the good value of binding free energies (ΔG bind) of the compounds to the desired protein. Convincingly, all the results support the potentiality of the selected compounds as natural antiviral candidates against the MERS-CoV S1-NTD.
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Blake S, Hemming I, Heng JIT, Agostino M. Structure-Based Approaches to Classify the Functional Impact of ZBTB18 Missense Variants in Health and Disease. ACS Chem Neurosci 2021; 12:979-989. [PMID: 33621064 DOI: 10.1021/acschemneuro.0c00758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The Cys2His2 type zinc finger is a motif found in many eukaryotic transcription factor proteins that facilitates binding to genomic DNA so as to influence cellular gene expression. One such transcription factor is ZBTB18, characterized as a repressor that orchestrates the development of mammalian tissues including skeletal muscle and brain during embryogenesis. In humans, it has been recognized that disease-associated ZBTB18 missense variants mapping to the coding sequence of the zinc finger domain influence sequence-specific DNA binding, disrupt transcriptional regulation, and impair neural circuit formation in the brain. Furthermore, general population ZBTB18 missense variants that influence DNA binding and transcriptional regulation have also been documented within this domain; however, the molecular traits that explain why some variants cause disease while others do not are poorly understood. Here, we have applied five structure-based approaches to evaluate their ability to discriminate between disease-associated and general population ZBTB18 missense variants. We found that thermodynamic integration and Residue Scanning in the Schrodinger Biologics Suite were the best approaches for distinguishing disease-associated variants from general population variants. Our results demonstrate the effectiveness of structure-based approaches for the functional characterization of missense alleles to DNA binding, zinc finger transcription factor protein-coding genes that underlie human health and disease.
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Affiliation(s)
- Steven Blake
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
| | - Isabel Hemming
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- The Faculty of Health and Medical Sciences, Medical School, The University of Western Australia, Crawley, Western Australia 6009, Australia
| | - Julian Ik-Tsen Heng
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
| | - Mark Agostino
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
- Curtin Institute for Computation, Curtin University, Bentley, Western Australia, Australia
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Khalid RR, Maryam A, Çınaroğlu SS, Siddiqi AR, Sezerman OU. A recursive molecular docking coupled with energy-based pose-rescoring and MD simulations to identify hsGC βH-NOX allosteric modulators for cardiovascular dysfunctions. J Biomol Struct Dyn 2021; 40:6128-6150. [PMID: 33522438 DOI: 10.1080/07391102.2021.1877818] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Modulating the activity of human soluble guanylate cyclase (hsGC) through allosteric regulation of the βH-NOX domain has been considered as an immediate treatment for cardiovascular disorder (CVDs). Currently available βH-NOX domain-specific agonists including cinaciguat are unable to deal with the conundrum raised due to oxidative stress in the case of CVDs and their associated comorbidities. Therefore, the idea of investigating novel compounds for allosteric regulation of hsGC activation has been rekindled to circumvent CVDs. Current study aims to identify novel βH-NOX domain-specific compounds that can selectively turn on sGC functions by modulating the conformational dynamics of the target protein. Through a comprehensive computational drug-discovery approach, we first executed a target-based performance assessment of multiple docking (PLANTS, QVina, LeDock, Vinardo, Smina) scoring functions based on multiple performance metrices. QVina showed the highest capability of selecting true-positive ligands over false positives thus, used to screen 4.8 million ZINC15 compounds against βH-NOX domain. The docked ligands were further probed in terms of contact footprint and pose reassessment through clustering analysis and PLANTS docking, respectively. Subsequently, energy-based AMBER rescoring of top 100 low-energy complexes, per-residue energy decomposition analysis, and ADME-Tox analysis yielded the top three compounds i.e. ZINC000098973660, ZINC001354120371, and ZINC000096022607. The impact of three selected ligands on the internal structural dynamics of the βH-NOX domain was also investigated through molecular dynamics simulations. The study revealed potential electrostatic interactions for better conformational dialogue between βH-NOX domain and allosteric ligands that are critical for the activation of hsGC as compared to the reference compound.
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Affiliation(s)
- Rana Rehan Khalid
- Department of Biosciences, COMSATS University, Islamabad, Pakistan.,Department of Biostatistics and Medical Informatics, Acibadem M. A. A. University, Istanbul, Turkey
| | - Arooma Maryam
- Department of Biosciences, COMSATS University, Islamabad, Pakistan
| | - Süleyman Selim Çınaroğlu
- Department of Biostatistics and Medical Informatics, Acibadem M. A. A. University, Istanbul, Turkey.,Department of Biochemistry, University of Oxford, Oxford, UK
| | | | - Osman Ugur Sezerman
- Department of Biostatistics and Medical Informatics, Acibadem M. A. A. University, Istanbul, Turkey
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