1
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Dulay ANG, de Guzman JCC, Marquez ZYD, Santana ESD, Arce J, Orosco FL. The potential of Chlorella spp. as antiviral source against African swine fever virus through a virtual screening pipeline. J Mol Graph Model 2024; 132:108846. [PMID: 39151375 DOI: 10.1016/j.jmgm.2024.108846] [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: 04/01/2024] [Revised: 06/26/2024] [Accepted: 08/02/2024] [Indexed: 08/19/2024]
Abstract
African swine fever (ASF) causes high mortality in pigs and threatens global swine production. There is still a lack of therapeutics available, with two vaccines under scrutiny and no approved small-molecule drugs. Eleven (11) viral proteins were used to identify potential antivirals in in silico screening of secondary metabolites (127) from Chlorella spp. The metabolites were screened for affinity and binding selectivity. High-scoring compounds were assessed through in silico ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) predictions, compared to structurally similar drugs, and checked for off-target docking with prepared swine receptors. Molecular dynamics (MD) simulations determined binding stability while binding energy was measured in Molecular Mechanics - Generalized Born Surface Area (MMGBSA) or Poisson-Boltzmann Surface Area (MMPBSA). Only six (6) compounds passed until MD analyses, of which five (5) were stable after 100 ns of MD runs. Of these five compounds, only three had binding affinities that were comparable to or stronger than controls. Specifically, phytosterols 24,25-dihydrolanosterol and CID 4206521 that interact with the RNA capping enzyme (pNP868R), and ergosterol which bound to the Erv-like thioreductase (pB119L). The compounds identified in this study can be used as a theoretical basis for in vitro screening to develop potent antiviral drugs against ASFV.
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Affiliation(s)
- Albert Neil G Dulay
- Virology and Vaccine Research Program, Industrial Technology Development Institute, Department of Science and Technology, Taguig, 1632, Philippines
| | - John Christian C de Guzman
- Virology and Vaccine Research Program, Industrial Technology Development Institute, Department of Science and Technology, Taguig, 1632, Philippines
| | - Zyra Ysha D Marquez
- Department of Biology, College of Arts and Sciences, University of the Philippines - Manila, Manila, 1000, Philippines
| | - Elisha Sofia D Santana
- Department of Biology, College of Arts and Sciences, University of the Philippines - Manila, Manila, 1000, Philippines
| | - Jessamine Arce
- Department of Biology, College of Arts and Sciences, University of the Philippines - Manila, Manila, 1000, Philippines
| | - Fredmoore L Orosco
- Virology and Vaccine Research Program, Industrial Technology Development Institute, Department of Science and Technology, Taguig, 1632, Philippines; Department of Biology, College of Arts and Sciences, University of the Philippines - Manila, Manila, 1000, Philippines; S&T Fellows Program, Department of Science and Technology, Taguig, 1632, Philippines.
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2
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Yang K, Liu H. Mining the Dynamical Properties of Substrate and FAD Binding Pockets of LSD1: Hints for New Inhibitor Design Direction. J Chem Inf Model 2024; 64:4773-4780. [PMID: 38837697 DOI: 10.1021/acs.jcim.4c00398] [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: 06/07/2024]
Abstract
Lysine-specific demethylase 1 (LSD1), a highly sophisticated epigenetic regulator, orchestrates a range of critical cellular processes, holding promising therapeutic potential for treating diverse diseases. However, the clinical research progress targeting LSD1 is very slow. After 20 years of research, only one small-molecule drug, BEA-17, targeting the degradation of LSD1 and CoREST has been approved by the U.S. Food and Drug Administration. The primary reason for this may be the lack of abundant structural data regarding its intricate functions. To gain a deeper understanding of its conformational dynamics and guide the drug design process, we conducted molecular dynamics simulations to explore the conformational states of LSD1 in the apo state and under the influence of cofactors of flavin adenine dinucleotide (FAD) and CoREST. Our results showed that, across all states, the substrate binding pocket exhibited high flexibility, whereas the FAD binding pocket remained more stable. These distinct dynamical properties are essential for LSD1's ability to bind various substrates while maintaining efficient demethylation activity. Both pockets can be enlarged by merging with adjacent pockets, although only the substrate binding pocket can shrink into smaller pockets. These new pocket shapes can inform inhibitor design, particularly for selectively FAD-competitive inhibitors of LSD1, given the presence of numerous FAD-dependent enzymes in the human body. More interestingly, in the absence of FAD binding, the united substrate and FAD binding pocket are partitioned by the conserved residue of Tyr761, offering valuable insights for the design of inhibitors that disrupt the crucial steric role of Tyr761 and the redox role of FAD. Additionally, we identified pockets that positively or negatively correlate with the substrate and FAD binding pockets, which can be exploited for the design of allosteric or concurrent inhibitors. Our results reveal the intricate dynamical properties of LSD1 as well as multiple novel conformational states, which deepen our understanding of its sophisticated functions and aid in the rational design of new inhibitors.
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Affiliation(s)
- Kecheng Yang
- National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Hongmin Liu
- Key Lab of Advanced Drug Preparation Technologies, Ministry of Education of China, State Key Laboratory of Esophageal Cancer Prevention & Treatment, Key Laboratory of Henan Province for Drug Quality and Evaluation, Institute of Drug Discovery and Development, School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
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3
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Lv N, Cao Z. Subpocket-Based Analysis Approach for the Protein Pocket Dynamics. J Chem Theory Comput 2024; 20:4909-4920. [PMID: 38772734 DOI: 10.1021/acs.jctc.4c00476] [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: 05/23/2024]
Abstract
Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.
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Affiliation(s)
- Nan Lv
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 360015, People's Republic of China
| | - Zexing Cao
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 360015, People's Republic of China
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4
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Wang R, Zhou Z, Wu X, Jiang X, Zhuo L, Liu M, Li H, Fu X, Yao X. An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy. J Chem Inf Model 2024; 64:2798-2806. [PMID: 37643082 DOI: 10.1021/acs.jcim.3c00868] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reliable and accurate, but they require expensive equipment and a lot of time. The method of machine learning speeds up the prediction process of SSPs, but the instability of feature extraction will also lead to further limitations of this type of method. Therefore, this paper proposes a new feature-correction-based model for SSP recognition in plants, abbreviated as SE-SSP. The model mainly includes the following three advantages: First, the use of transformer encoders can better reveal implicit features. Second, design a feature correction module suitable for sequences, named 2-D SENET, to adaptively adjust the features to obtain a more robust feature representation. Third, stack multiple linear modules to further dig out the deep information on the sample. At the same time, the training based on a contrastive learning strategy can alleviate the problem of sparse samples. We construct experiments on publicly available data sets, and the results verify that our model shows an excellent performance. The proposed model can be used as a convenient and effective SSP prediction tool in the future. Our data and code are publicly available at https://github.com/wrab12/SE-SSP/.
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Affiliation(s)
- Rui Wang
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Zhecheng Zhou
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xiaonan Wu
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xin Jiang
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Linlin Zhuo
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Mingzhe Liu
- Wenzhou University of Technology, 325000 Wenzhou, China
| | - Hao Li
- Central South University, 410083 Changsha, China
| | - Xiangzheng Fu
- Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao
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Chen J, Gou Q, Chen X, Song Y, Zhang F, Pu X. Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor. Phys Chem Chem Phys 2024; 26:10698-10710. [PMID: 38512140 DOI: 10.1039/d3cp05050e] [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: 03/22/2024]
Abstract
Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive μOR and two different biased agonists (G-protein-biased agonist TRV130 and β-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the β-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for μOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.
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Affiliation(s)
- Jianfang Chen
- College of Chemistry, Sichuan University, Chengdu 610064, China.
| | - Qiaoling Gou
- College of Chemistry, Sichuan University, Chengdu 610064, China.
| | - Xin Chen
- College of Chemistry, Sichuan University, Chengdu 610064, China.
| | - Yuanpeng Song
- College of Chemistry, Sichuan University, Chengdu 610064, China.
| | - Fuhui Zhang
- Graduate School, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China.
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6
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Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [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: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
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Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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7
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Bai L, Deng Z, Xu M, Zhang Z, Guo G, Xue X, Wang S, Yang J, Xia Z. CETSA-MS-based target profiling of anti-aging natural compound quercetin. Eur J Med Chem 2024; 267:116203. [PMID: 38342014 DOI: 10.1016/j.ejmech.2024.116203] [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: 10/18/2023] [Revised: 01/16/2024] [Accepted: 01/31/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND Quercetin is widely distributed in nature and abundant in the human diet, which exhibits diverse biological activities and potential medical benefits. However, there remains a lack of comprehensive understanding about its cellular targets, impeding its in-depth mechanistic studies and clinical applications. PURPOSE This study aimed to profile protein targets of quercetin at the proteome level. METHODS A label-free CETSA-MS proteomics technique was employed for target enrichment and identification. The R package Inflect was used for melting curve fitting and target selection. D3Pocket and LiBiSco tools were used for binding pocket prediction and binding pocket analysis. Western blotting, molecular docking, site-directed mutagenesis and pull-down assays were used for target verification and validation. RESULTS We curated a library of direct binding targets of quercetin in cells. This library comprises 37 proteins that show increased thermal stability upon quercetin binding and 33 proteins that display decreased thermal stability. Through Western blotting, molecular docking, site-directed mutagenesis and pull-down assays, we validated CBR1 and GSK3A from the stabilized protein group and MAPK1 from the destabilized group as direct binding targets of quercetin. Moreover, we characterized the shared chemical properties of the binding pockets of quercetin with targets. CONCLUSION Our findings deepen our understanding of the proteins pivotal to the bioactivity of quercetin and lay the groundwork for further exploration into its mechanisms of action and potential clinical applications.
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Affiliation(s)
- Lin Bai
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zhifen Deng
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Mengfei Xu
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zhehao Zhang
- Department of Biochemistry, Faculty of Life Science, Faculty of Natural Science, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Guangyu Guo
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China; Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xinli Xue
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Shaochi Wang
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Jinghua Yang
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zongping Xia
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China; Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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8
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Wang J, Xu Y, Wang X, Li J, Hua Z. Mechanism of Mutation-Induced Effects on the Catalytic Function of TEV Protease: A Molecular Dynamics Study. Molecules 2024; 29:1071. [PMID: 38474583 DOI: 10.3390/molecules29051071] [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: 01/27/2024] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Tobacco etch virus protease (TEVp) is wildly exploited for various biotechnological applications. These applications take advantage of TEVp's ability to cleave specific substrate sequences to study protein function and interactions. A major limitation of this enzyme is its relatively slow catalytic rate. In this study, MD simulations were conducted on TEV enzymes and known highly active mutants (eTEV and uTEV3) to explore the relationship between mutation, conformation, and catalytic function. The results suggest that mutations distant from the active site can influence the substrate-binding pocket through interaction networks. MD analysis of eTEV demonstrates that, by stabilizing the orientation of the substrate at the catalytic site, mutations that appropriately enlarge the substrate-binding pocket will be beneficial for Kcat, enhancing the catalytic efficiency of the enzyme. On the contrary, mutations in uTEV3 reduced the flexibility of the active pocket and increased the hydrogen bonding between the substrate and enzyme, resulting in higher affinity. At the same time, the MD simulation demonstrates that mutations outside of the active site residues could affect the dynamic movement of the binding pocket by altering residue networks and communication pathways, thereby having a profound impact on reactivity. These findings not only provide a molecular mechanistic explanation for the excellent mutants, but also serve as a guiding framework for rational computational design.
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Affiliation(s)
- Jingyao Wang
- School of Biopharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Yicong Xu
- School of Biopharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Xujian Wang
- School of Biopharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Jiahuang Li
- School of Biopharmacy, China Pharmaceutical University, Nanjing 211198, China
- Changzhou High-Tech Research Institute, Nanjing University, Changzhou 213164, China
| | - Zichun Hua
- School of Biopharmacy, China Pharmaceutical University, Nanjing 211198, China
- Changzhou High-Tech Research Institute, Nanjing University, Changzhou 213164, China
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Science, Nanjing 210023, China
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9
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Li M, Lan X, Lu X, Zhang J. A Structure-Based Allosteric Modulator Design Paradigm. HEALTH DATA SCIENCE 2023; 3:0094. [PMID: 38487194 PMCID: PMC10904074 DOI: 10.34133/hds.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 10/11/2023] [Indexed: 03/17/2024]
Abstract
Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.
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Affiliation(s)
- Mingyu Li
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobin Lan
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xun Lu
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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10
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Lu X, Lan X, Lu S, Zhang J. Progressive computational approaches to facilitate decryption of allosteric mechanism and drug discovery. Curr Opin Struct Biol 2023; 83:102701. [PMID: 37716092 DOI: 10.1016/j.sbi.2023.102701] [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: 07/12/2023] [Revised: 08/17/2023] [Accepted: 08/21/2023] [Indexed: 09/18/2023]
Abstract
Allostery is a ubiquitous biological phenomenon where perturbation at topologically distal areas of a protein serves as a trigger to fine-tune the orthosteric site and thus regulate protein function. The investigation of allosteric regulation greatly enhances our understanding of human diseases and broadens avenue for drug discovery. For decades, owing to the difficulty in allostery characterization through serendipitous experimental screening, researchers have developed several innovative computational approaches, which proves to accelerate the elucidation of allostery. Herein, we review the state-of-the-art advance of computational methodologies for allostery study, with particular emphasis on promising trends emerging over the past two years. We expect this review will outline the latest landscape of allostery study and inspire researchers to further facilitate this field.
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Affiliation(s)
- Xun Lu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobing Lan
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Shaoyong Lu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
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11
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López-Correa JM, König C, Vellido A. GPCR molecular dynamics forecasting using recurrent neural networks. Sci Rep 2023; 13:20995. [PMID: 38017062 PMCID: PMC10684758 DOI: 10.1038/s41598-023-48346-4] [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: 04/13/2023] [Accepted: 11/25/2023] [Indexed: 11/30/2023] Open
Abstract
G protein-coupled receptors (GPCRs) are a large superfamily of cell membrane proteins that play an important physiological role as transmitters of extracellular signals. Signal transmission through the cell membrane depends on conformational changes in the transmembrane region of the receptor, which makes the investigation of the dynamics in these regions particularly relevant. Molecular dynamics (MD) simulations provide a wealth of data about the structure, dynamics, and physiological function of biological macromolecules by modelling the interactions between their atomic constituents. In this study, a Recurrent and Convolutional Neural Network (RNN) model, namely Long Short-Term Memory (LSTM), is used to predict the dynamics of two GPCR states and three specific simulations of each one, through their activation path and focussing on specific receptor regions. Active and inactive states of the GPCRs are analysed in six scenarios involving APO, Full Agonist (BI 167107) and Partial Inverse Agonist (carazolol) of the receptor. Four Machine Learning models with increasing complexity in terms of neural network architecture are evaluated, and their results discussed. The best method achieves an overall RMSD lower than 0.139 Å and the transmembrane helices are the regions showing the minimum prediction errors and minimum relative movements of the protein.
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Affiliation(s)
| | - Caroline König
- Universitat Politècnica de Catalunya, Barcelona, Spain
- IDEAI-UPC - Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Alfredo Vellido
- Universitat Politècnica de Catalunya, Barcelona, Spain.
- IDEAI-UPC - Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain.
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12
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Liu K, Hao Z, Zheng H, Wang H, Zhang L, Yan M, Tuerhong R, Zhou Y, Wang Y, Pang T, Shi L. Repurposing of rilpivirine for preventing platelet β3 integrin-dependent thrombosis by targeting c-Src active autophosphorylation. Thromb Res 2023; 229:53-68. [PMID: 37413892 DOI: 10.1016/j.thromres.2023.06.031] [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: 04/17/2023] [Revised: 06/19/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND HIV-infected individuals are known to be at higher risk for thrombotic cardiovascular disease (CVD), which may also be differentially affected by components of anti-HIV drugs. To identify the effects of a series of FDA-approved anti-HIV drugs on platelet aggregation in humans, focusing on the novel pharmacological effects of rilpivirine (RPV), a reverse transcriptase inhibitor, on platelet function both in vitro and in vivo and the mechanisms involved. METHODS AND RESULTS In vitro studies showed that RPV was the only anti-HIV reagent that consistently and efficiently inhibited aggregation elicited by different agonists, exocytosis, morphological extension on fibrinogen, and clot retraction. Treatment of mice with RPV significantly prevented thrombus formation in FeCl3-injured mesenteric vessels, postcava with stenosis surgery, and ADP -induced pulmonary embolism models without defects in platelet viability, tail bleeding, and coagulation activities. RPV also improved cardiac performance in mice with post-ischemic reperfusion. A mechanistic study revealed that RPV preferentially attenuated fibrinogen-stimulated Tyr773 phosphorylation of β3-integrin by inhibiting Tyr419 autophosphorylation of c-Src. Molecular docking and surface plasmon resonance analyses showed that RPV can bind directly to c-Src. Further mutational analysis showed that the Phe427 residue of c-Src is critical for RPV interaction, suggesting a novel interaction site for targeting c-Src to block β3-integrin outside-in signaling. CONCLUSION These results demonstrated that RPV was able to prevent the progression of thrombotic CVDs by interrupting β3-integrin-mediated outside-in signaling via inhibiting c-Src activation without hemorrhagic side effects, highlighting RPV as a promising reagent for the prevention and therapy of thrombotic CVDs.
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Affiliation(s)
- Kui Liu
- Xiamen Key Laboratory of Cardiovascular Disease, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, 2999 Jinshan Road, Xiamen 361000, China; State Key Laboratory of Natural Medicines, New Drug Screening Center, Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing 210009, China
| | - Zhen Hao
- Xiamen Key Laboratory of Cardiovascular Disease, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, 2999 Jinshan Road, Xiamen 361000, China; College of Basic Medical Sciences, Dalian Medical University, No. 9 West Section, South Lv shun Road, Dalian 116044, China
| | - Hao Zheng
- College of Basic Medical Sciences, Dalian Medical University, No. 9 West Section, South Lv shun Road, Dalian 116044, China
| | - Haojie Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Luying Zhang
- College of Basic Medical Sciences, Dalian Medical University, No. 9 West Section, South Lv shun Road, Dalian 116044, China
| | - Minghui Yan
- College of Basic Medical Sciences, Dalian Medical University, No. 9 West Section, South Lv shun Road, Dalian 116044, China
| | - Reyisha Tuerhong
- College of Basic Medical Sciences, Dalian Medical University, No. 9 West Section, South Lv shun Road, Dalian 116044, China
| | - Yuling Zhou
- Xiamen Key Laboratory of Cardiovascular Disease, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, 2999 Jinshan Road, Xiamen 361000, China
| | - Yan Wang
- Xiamen Key Laboratory of Cardiovascular Disease, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, 2999 Jinshan Road, Xiamen 361000, China.
| | - Tao Pang
- State Key Laboratory of Natural Medicines, New Drug Screening Center, Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing 210009, China.
| | - Lei Shi
- Xiamen Key Laboratory of Cardiovascular Disease, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, 2999 Jinshan Road, Xiamen 361000, China; College of Basic Medical Sciences, Dalian Medical University, No. 9 West Section, South Lv shun Road, Dalian 116044, China.
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Zou R, Guo Y, Wang Y, Lu X, Ma Z, Shou L, Liu Y, Zhu G, Guo Y. Insights into the Binding Profile of Anti-chlorpyrifos Recombinant Antibodies: From Computational Simulation to Immunoassay Validation. Anal Chem 2023; 95:11287-11295. [PMID: 37459591 DOI: 10.1021/acs.analchem.3c01355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
A novel virtual screening strategy was proposed for the profiling and discovery of active variable regions (VRs) that encode hapten-specific recombinant antibodies (rAbs). Chlorpyrifos, a hazardous organophosphorus pesticide, was selected as the target. First, a VR model-14G4 from anti-chlorpyrifos hybridoma was built via homology modeling. Its binding pattern toward seven organophosphorus analogues was assessed through virtual screening by performing molecular docking. Based on energy scoring, visual examination, and molecular interaction analysis, chlorpyrifos-methyl was also inferred as the high-affinity target for model-14G4 and was then confirmed via a non-competitive surface plasmon resonance (SPR) assay. Subsequently, we attempted to discover hapten-specific VRs by creating a collection of VR models for anonymous testing. Chlorpyrifos and model-14G4 were employed as the known hit and active VRs, respectively. After molecular docking, a novel anti-chlorpyrifos VR (model-1) was identified due to its satisfactory energy scoring and a similar binding pattern to the reference model-14G4. Expressed by HEK293(F) mammalian cells, the newly prepared full-length rAb-model-1 and rAb-14G4 exhibited high sensitivities for detecting chlorpyrifos by the indirect competitive enzyme-linked immunosorbent assay (ic-ELISA), with IC50 of 3.01 ng/mL and 42.82 ng/mL, respectively. They recognized chlorpyrifos-methyl with a cross-reactivity (CR) of 2.5-17.3%. Moreover, the binding properties of rAb-model-1 for recognizing chlorpyrifos and chlorpyrifos-methyl were confirmed via a non-competitive microscale thermophoresis (MST) method. Thus, the experimental results showed good agreement with computational outputs on antibody profiling. Furthermore, the recognition diversity of rAb-model-1 for chlorpyrifos and chlorpyrifos-methyl was studied via molecular dynamics simulation. Overall, the proposed study provides a versatile and economical strategy for antibody characterization and promotes the in vitro production of rAbs for pesticide monitoring.
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Affiliation(s)
- Rubing Zou
- Institute of Pesticide and Environmental Toxicology, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
| | - Yuanhao Guo
- Institute of Pesticide and Environmental Toxicology, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
- Quality and Safety Engineering Institute of Food and Drug, Zhejiang Gongshang University, Hangzhou 310018, China
| | - Yan Wang
- Institute of Pesticide and Environmental Toxicology, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
| | - Xinying Lu
- Institute of Pesticide and Environmental Toxicology, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
| | - Zhongjie Ma
- Institute of Pesticide and Environmental Toxicology, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
| | - Linfei Shou
- Zhejiang Provincial Plant Protection Quarantine and Pesticide Management Institute, Hangzhou 310004, China
| | - Yihua Liu
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China
| | - Guonian Zhu
- Institute of Pesticide and Environmental Toxicology, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
| | - Yirong Guo
- Institute of Pesticide and Environmental Toxicology, Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
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Li Z, Chen Y, Li Y, Zeng Y, Li W, Ma X, Huang L, Shen Y. Whole-Genome Resequencing Reveals the Diversity of Patchouli Germplasm. Int J Mol Sci 2023; 24:10970. [PMID: 37446145 DOI: 10.3390/ijms241310970] [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: 05/17/2023] [Revised: 06/27/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
As an important medicinal and aromatic plant, patchouli is distributed throughout most of Asia. However, current research on patchouli's genetic diversity is limited and lacks genome-wide studies. Here, we have collected seven representative patchouli accessions from different localities and performed whole-genome resequencing on them. In total, 402,650 single nucleotide polymorphisms (SNPs) and 153,233 insertions/deletions (INDELs) were detected. Based on these abundant genetic variants, patchouli accessions were primarily classified into the Chinese group and the Southeast Asian group. However, the accession SP (Shipai) collected from China formed a distinct subgroup within the Southeast Asian group. As SP has been used as a genuine herb in traditional Chinese medicine, its unique molecular markers have been subsequently screened and verified. For 26,144 specific SNPs and 16,289 specific INDELs in SP, 10 of them were validated using Polymerase Chain Reaction (PCR) following three different approaches. Further, we analyzed the effects of total genetic variants on genes involved in the sesquiterpene synthesis pathway, which produce the primary phytochemical compounds found in patchouli. Eight genes were ultimately investigated and a gene encoding nerolidol synthetase (PatNES) was chosen and confirmed through biochemical assay. In accession YN, genetic variants in PatNES led to a loss of synthetase activity. Our results provide valuable information for understanding the diversity of patchouli germplasm resources.
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Affiliation(s)
- Zhipeng Li
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Yiqiong Chen
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Yangyan Li
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Ying Zeng
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Wanying Li
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Xiaona Ma
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Lili Huang
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Yanting Shen
- Institute of Medicinal Plant Physiology and Ecology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100000, China
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15
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Wang Y, Yang F, Yan D, Zeng Y, Wei B, Chen J, He W. Identification Mechanism of BACE1 on Inhibitors Probed by Using Multiple Separate Molecular Dynamics Simulations and Comparative Calculations of Binding Free Energies. Molecules 2023; 28:4773. [PMID: 37375328 DOI: 10.3390/molecules28124773] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward the treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively determine the identification mechanism of BACE1 for three inhibitors, 60W, 954 and 60X. The analyses of MD trajectories indicated that the presence of three inhibitors influences the structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward the treatment of AD.
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Affiliation(s)
- Yiwen Wang
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
- School of Aeronautics, Shandong Jiaotong University, Jinan 250357, China
| | - Fen Yang
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
| | - Dongliang Yan
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
- School of Science, Shandong Jiaotong University, Jinan 250357, China
| | - Yalin Zeng
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Jianzhong Chen
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
- School of Science, Shandong Jiaotong University, Jinan 250357, China
| | - Weikai He
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
- School of Aeronautics, Shandong Jiaotong University, Jinan 250357, China
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Song B, Wang J, Ren Y, Su Y, Geng X, Yang F, Wang H, Zhang J. Butein inhibits cancer cell growth by rescuing the wild-type thermal stability of mutant p53. Biomed Pharmacother 2023; 163:114773. [PMID: 37156116 DOI: 10.1016/j.biopha.2023.114773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/15/2023] [Accepted: 04/22/2023] [Indexed: 05/10/2023] Open
Abstract
p53 is a transcription factor that activates the expression of various genes involved in the maintenance of genomic stability, and more than 50% of cancers harbor inactivating p53 mutations, which are indicative of highly aggressive cancer and poor prognosis. Pharmacological targeting of mutant p53 to restore the wild-type p53 tumor-suppressing function is a promising strategy for cancer therapy. In this study, we identified a small molecule, Butein, that reactivates mutant p53 activity in tumor cells harboring the R175H or R273H mutation. Butein restored wild-type-like conformation and DNA-binding ability in HT29 and SK-BR-3 cells harboring mutant p53-R175H and mutant p53-R273H, respectively. Moreover, Butein enabled the transactivation of p53 target genes and decreased the interactions of Hsp90 with mutant p53-R175H and mutant p53-R273H proteins, while Hsp90 overexpression reversed targeted p53 gene activation. In addition, Butein induced thermal stabilization of wild-type p53, mutant p53-R273H and mutant p53-R175H, as determined via CETSA. From docking study, we further proved that Butein binding to p53 stabilized the DNA-binding loop-sheet-helix motif of mutant p53-R175H and regulated its DNA-binding activity via an allosteric mechanism, conferring wild-type-like the DNA-binding activity of mutant p53. Collectively, the data suggest that Butein is a potential antitumor agent that restores p53 function in cancers harboring mutant p53-R273H or mutant p53-R175H. SIGNIFICANCE: Butein restores the ability of mutant p53 to bind DNA by reversing its transition to the Loop3 (L3) state, endows p53 mutants with thermal stability and re-establishes their transcriptional activity to induce cancer cell death.
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Affiliation(s)
- Bin Song
- Lab of Molecular Pharmacology, Medical School, Kunming University of Science and Technology, Kunming 650500, China; Laboratory of Radiation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Jiajian Wang
- Lab of Molecular Pharmacology, Medical School, Kunming University of Science and Technology, Kunming 650500, China
| | - Yixin Ren
- School of Pharmacy, Minzu University of China, Beijing 100081, China
| | - Yongnan Su
- Lab of Molecular Pharmacology, Medical School, Kunming University of Science and Technology, Kunming 650500, China
| | - Xueye Geng
- Lab of Molecular Pharmacology, Medical School, Kunming University of Science and Technology, Kunming 650500, China
| | - Fan Yang
- Lab of Molecular Pharmacology, Medical School, Kunming University of Science and Technology, Kunming 650500, China
| | - Hao Wang
- School of Pharmacy, Minzu University of China, Beijing 100081, China
| | - Jihong Zhang
- Lab of Molecular Pharmacology, Medical School, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Province Clinical Research Center for Hematologic Disease, Kunming 650032, China.
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17
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Wang J, Zhao H, Qu Y, Yang P, Huang J. The binding pocket properties were fundamental to functional diversification of the GDSL-type esterases/lipases gene family in cotton. FRONTIERS IN PLANT SCIENCE 2023; 13:1099673. [PMID: 36743561 PMCID: PMC9889996 DOI: 10.3389/fpls.2022.1099673] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
Cotton is one of the most important crops in the world. GDSL-type esterases/lipases (GELPs) are widely present in all kingdoms and play an essential role in regulating plant growth, development, and responses to abiotic and biotic stresses. However, the molecular mechanisms underlying this functional diversity remain unclear. Here, based on the identification of the GELP gene family, we applied genetic evolution and molecular simulation techniques to explore molecular mechanisms in cotton species. A total of 1502 GELP genes were identified in 10 cotton species. Segmental duplication and differences in evolutionary rates are the leading causes of the increase in the number and diversity of GELP genes during evolution for ecological adaptation. Structural analysis revealed that the GELP family has high structural diversity. Moreover, molecular simulation studies have demonstrated significant differences in the properties of the binding pockets among cotton GELPs. In the process of adapting to the environment, GELPs not only have segmental duplication but also have different evolutionary rates, resulting in gene diversity. This diversity leads to significant differences in the 3D structure and binding pocket properties and, finally, to functional diversity. These findings provide a reference for further functional analyses of plant GELPs.
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Affiliation(s)
- Jianshe Wang
- College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, Henan, China
| | - Haiyan Zhao
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, Henan, China
| | - Yunfang Qu
- College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China
| | - Peng Yang
- College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China
| | - Jinling Huang
- College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China
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Gutiérrez-Mondragón MA, König C, Vellido A. Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of the β2- Adrenergic GPCR Receptor. Int J Mol Sci 2023; 24:ijms24021155. [PMID: 36674669 PMCID: PMC9865744 DOI: 10.3390/ijms24021155] [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: 11/15/2022] [Revised: 12/22/2022] [Accepted: 12/30/2022] [Indexed: 01/11/2023] Open
Abstract
G-protein-coupled receptors (GPCRs) are cell membrane proteins of relevance as therapeutic targets, and are associated to the development of treatments for illnesses such as diabetes, Alzheimer's, or even cancer. Therefore, comprehending the underlying mechanisms of the receptor functional properties is of particular interest in pharmacoproteomics and in disease therapy at large. Their interaction with ligands elicits multiple molecular rearrangements all along their structure, inducing activation pathways that distinctly influence the cell response. In this work, we studied GPCR signaling pathways from molecular dynamics simulations as they provide rich information about the dynamic nature of the receptors. We focused on studying the molecular properties of the receptors using deep-learning-based methods. In particular, we designed and trained a one-dimensional convolution neural network and illustrated its use in a classification of conformational states: active, intermediate, or inactive, of the β2-adrenergic receptor when bound to the full agonist BI-167107. Through a novel explainability-oriented investigation of the prediction results, we were able to identify and assess the contribution of individual motifs (residues) influencing a particular activation pathway. Consequently, we contribute a methodology that assists in the elucidation of the underlying mechanisms of receptor activation-deactivation.
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Affiliation(s)
- Mario A. Gutiérrez-Mondragón
- Computer Science Department, Universitat Politècnica de Catalunya—UPC BarcelonaTech, 08034 Barcelona, Spain
- Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Universitat Politècnica de Catalunya—UPC BarcelonaTech, 08034 Barcelona, Spain
| | - Caroline König
- Computer Science Department, Universitat Politècnica de Catalunya—UPC BarcelonaTech, 08034 Barcelona, Spain
- Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Universitat Politècnica de Catalunya—UPC BarcelonaTech, 08034 Barcelona, Spain
- Correspondence:
| | - Alfredo Vellido
- Computer Science Department, Universitat Politècnica de Catalunya—UPC BarcelonaTech, 08034 Barcelona, Spain
- Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, Universitat Politècnica de Catalunya—UPC BarcelonaTech, 08034 Barcelona, Spain
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Chang J, Jiang Z, Ma T, Li J, Chen J, Ye P, Feng L. Integrating transcriptomics and network analysis-based multiplexed drug repurposing to screen drug candidates for M2 macrophage-associated castration-resistant prostate cancer bone metastases. Front Immunol 2022; 13:989972. [PMID: 36389722 PMCID: PMC9643318 DOI: 10.3389/fimmu.2022.989972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022] Open
Abstract
Metastatic castration-resistant prostate cancer (CRPC) has long been considered to be associated with patient mortality. Among metastatic organs, bone is the most common metastatic site, with more than 90% of advanced patients developing bone metastases (BMs) before 24 months of death. Although patients were recommended to use bone-targeted drugs represented by bisphosphonates to treat BMs of CRPC, there was no significant improvement in patient survival. In addition, the use of immunotherapy and androgen deprivation therapy is limited due to the immunosuppressed state and resistance to antiandrogen agents in patients with bone metastases. Therefore, it is still essential to develop a safe and effective therapeutic schedule for CRPC patients with BMs. To this end, we propose a multiplex drug repurposing scheme targeting differences in patient immune cell composition. The identified drug candidates were ranked from the perspective of M2 macrophages by integrating transcriptome and network-based analysis. Meanwhile, computational chemistry and clinical trials were used to generate a comprehensive drug candidate list for the BMs of CRPC by drug redundancy structure filtering. In addition to docetaxel, which has been approved for clinical trials, the list includes norethindrone, testosterone, menthol and foretinib. This study provides a new scheme for BMs of CRPC from the perspective of M2 macrophages. It is undeniable that this multiplex drug repurposing scheme specifically for immune cell-related bone metastases can be used for drug screening of any immune-related disease, helping clinicians find promising therapeutic schedules more quickly, and providing reference information for drug R&D and clinical trials.
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Garg P, Vanamamalai VK, Jali I, Sharma S. In silico prediction of the animal susceptibility and virtual screening of natural compounds against SARS-CoV-2: Molecular dynamics simulation based analysis. Front Genet 2022; 13:906955. [PMID: 36110222 PMCID: PMC9468858 DOI: 10.3389/fgene.2022.906955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. It has six open reading frames (orf1ab, orf3a, orf6, orf7a, orf8, and orf10), a spike protein, a membrane protein, an envelope small membrane protein, and a nucleocapsid protein, out of which, orf1ab is the largest ORF coding different important non-structural proteins. In this study, an effort was made to evaluate the susceptibility of different animals against SARS-CoV-2 by analyzing the interactions of Spike and ACE2 proteins of the animals and propose a list of potential natural compounds binding to orf1ab of SARS-CoV-2. Here, we analyzed structural interactions between spike proteins of SARS-CoV-2 and the ACE2 receptor of 16 different hosts. A simulation for 50 ns was performed on these complexes. Based on post-simulation analysis, Chelonia mydas was found to have a more stable complex, while Bubalus bubalis, Aquila chrysaetos chrysaetos, Crocodylus porosus, and Loxodonta africana were found to have the least stable complexes with more fluctuations than all other organisms. Apart from that, we performed domain assignment of orf1ab of SARS-CoV-2 and identified 14 distinct domains. Out of these, Domain 3 (DNA/RNA polymerases) was selected as a target, as it showed no similarities with host proteomes and was validated in silico. Then, the top 10 molecules were selected from the virtual screening of ∼1.8 lakh molecules from the ZINC database, based on binding energy, and validated for ADME and toxicological properties. Three molecules were selected and analyzed further. The structural analysis showed that these molecules were residing within the pocket of the receptor. Finally, a simulation for 200 ns was performed on complexes with three selected molecules. Based on post-simulation analysis (RMSD, RMSF, Rg, SASA, and energies), the molecule ZINC000103666966 was found as the most suitable inhibitory compound against Domain 3. As this is an in silico prediction, further experimental studies could unravel the potential of the proposed molecule against SARS-CoV-2.
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Liu XH, Cheng T, Liu BY, Chi J, Shu T, Wang T. Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development. Front Pharmacol 2022; 13:955648. [PMID: 36016554 PMCID: PMC9395726 DOI: 10.3389/fphar.2022.955648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.
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Li M, Wang Y, Fan J, Zhuang H, Liu Y, Ji D, Lu S. Mechanistic Insights into the Long-range Allosteric Regulation of KRAS Via Neurofibromatosis Type 1 (NF1) Scaffold Upon SPRED1 Loading. J Mol Biol 2022; 434:167730. [PMID: 35872068 DOI: 10.1016/j.jmb.2022.167730] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/22/2022] [Accepted: 07/08/2022] [Indexed: 01/17/2023]
Abstract
Allosteric regulation is the most direct and efficient way of regulating protein function, wherein proteins transmit the perturbations at one site to another distinct functional site. Deciphering the mechanism of allosteric regulation is of vital importance for the comprehension of both physiological and pathological events in vivo as well as the rational allosteric drug design. However, it remains challenging to elucidate dominant allosteric signal transduction pathways, especially for large and multi-component protein machineries where long-range allosteric regulation exits. One of the quintessential examples having long-range allosteric regulation is the ternary complex, SPRED1-RAS-neurofibromin type 1 (NF1, a RAS GTPase-activating protein), in which SPRED1 facilitates RAS-GTP hydrolysis by interacting with NF1 at a distal, allosteric site from the RAS binding site. To address the underlying mechanism, we performed extensive Gaussian accelerated molecular dynamics simulations and Markov state model analysis of KRAS-NF1 complex in the presence and absence of SPRED1. Our findings suggested that SPRED1 loading allosterically enhanced KRAS-NF1 binding, but hindered conformational transformation of the NF1 catalytic center for RAS hydrolysis. Moreover, we unveiled the possible allosteric pathways upon SPRED1 binding through difference contact network analysis. This study not only provided an in-depth mechanistic insight into the allosteric regulation of KRAS by SPRED1, but also shed light on the investigation of long-range allosteric regulation among complex macromolecular systems.
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Affiliation(s)
- Minyu Li
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Yuanhao Wang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jigang Fan
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Haiming Zhuang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Dong Ji
- Department of Anesthesiology, Changhai Hospital, Navy Medical University, Shanghai 200433, China.
| | - Shaoyong Lu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.
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23
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Rial R, González-Durruthy M, Liu Z, Ruso JM. Conformational binding mechanism of lysozyme induced by interactions with penicillin antibiotic drugs. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.119081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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24
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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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25
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Ma M, Yang Y, Wu L, Zhou L, Shi Y, Han J, Xu Z, Zhu W. Conserved protein targets for developing pan-coronavirus drugs based on sequence and 3D structure similarity analyses. Comput Biol Med 2022; 145:105455. [PMID: 35364304 PMCID: PMC8957316 DOI: 10.1016/j.compbiomed.2022.105455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 11/23/2022]
Abstract
There are 7 known human pathogenic coronaviruses, which are HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-HKU1, MERS-CoV, SARS-CoV and SARS-CoV-2. While SARS-CoV-2 is currently caused a severe epidemic, experts believe that new pathogenic coronavirus would emerge in the future. Therefore, developing broad-spectrum anti-coronavirus drugs is of great significance. In this study, we performed protein sequence and three-dimensional structure analyses for all the 20 virus-encoded proteins across all the 7 coronaviruses, with the purpose to identify highly conserved proteins and binding sites for developing pan-coronavirus drugs. We found that nsp5, nsp10, nsp12, nsp13, nsp14, and nsp16 are highly conserved both in protein sequences (with average identity percentage higher than 52%, average amino acid conservation scores higher than 5.2) and binding pockets (with average amino acid conservation scores higher than 5.8). We also performed the similarity comparison between these 6 proteins and all the human proteins, and found that all the 6 proteins have similarity less than 25%, indicating that the drugs targeting the 6 proteins should have little interference of human protein function. Accordingly, we suggest that nsp5, nsp10, nsp12, nsp13, nsp14, and nsp16 are potential targets for pan-coronavirus drug development.
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Affiliation(s)
- Minfei Ma
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanqing Yang
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leyun Wu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liping Zhou
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yulong Shi
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaxin Han
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, 210046, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China.
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26
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Yang Y, Zhou D, Zhang X, Shi Y, Han J, Zhou L, Wu L, Ma M, Li J, Peng S, Xu Z, Zhu W. D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19. Brief Bioinform 2022; 23:6571526. [PMID: 35443040 PMCID: PMC9310271 DOI: 10.1093/bib/bbac147] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/13/2022] [Accepted: 03/31/2022] [Indexed: 11/25/2022] Open
Abstract
Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
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Affiliation(s)
- Yanqing Yang
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Deshan Zhou
- Department of Computer Science, Hunan University, Changsha, 410082, China
| | - Xinben Zhang
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Yulong Shi
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Jiaxin Han
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, 210046, China
| | - Liping Zhou
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Leyun Wu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Minfei Ma
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Jintian Li
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Shaoliang Peng
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
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27
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Li C, Liu J, Chen J, Yuan Y, Yu J, Gou Q, Guo Y, Pu X. An Interpretable Convolutional Neural Network Framework for Analyzing Molecular Dynamics Trajectories: a Case Study on Functional States for G-Protein-Coupled Receptors. J Chem Inf Model 2022; 62:1399-1410. [PMID: 35257580 DOI: 10.1021/acs.jcim.2c00085] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular dynamics (MD) simulations have made great contribution to revealing structural and functional mechanisms for many biomolecular systems. However, how to identify functional states and important residues from vast conformation space generated by MD remains challenging; thus an intelligent navigation is highly desired. Despite intelligent advantages of deep learning exhibited in analyzing MD trajectory, its black-box nature limits its application. To address this problem, we explore an interpretable convolutional neural network (CNN)-based deep learning framework to automatically identify diverse active states from the MD trajectory for G-protein-coupled receptors (GPCRs), named the ICNNMD model. To avoid the information loss in representing the conformation structure, the pixel representation is introduced, and then the CNN module is constructed to efficiently extract features followed by a fully connected neural network to realize the classification task. More importantly, we design a local interpretable model-agnostic explanation interpreter for the classification result by local approximation with a linear model, through which important residues underlying distinct active states can be quickly identified. Our model showcases higher than 99% classification accuracy for three important GPCR systems with diverse active states. Notably, some important residues in regulating different biased activities are successfully identified, which are beneficial to elucidating diverse activation mechanisms for GPCRs. Our model can also serve as a general tool to analyze MD trajectory for other biomolecular systems. All source codes are freely available at https://github.com/Jane-Liu97/ICNNMD for aiding MD studies.
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Affiliation(s)
- Chuan Li
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Jiangting Liu
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Jianfang Chen
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yuan Yuan
- College of Management, Southwest University for Nationalities, Chengdu 610041, China
| | - Jin Yu
- Department of Physics and Astronomy, University of California, Irvine, California 92697, United States
| | - Qiaolin Gou
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
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28
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Ni D, Liu Y, Kong R, Yu Z, Lu S, Zhang J. Computational elucidation of allosteric communication in proteins for allosteric drug design. Drug Discov Today 2022; 27:2226-2234. [DOI: 10.1016/j.drudis.2022.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/22/2022] [Accepted: 03/17/2022] [Indexed: 02/07/2023]
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29
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Durojaye OA, Sedzro DM, Idris MO, Yekeen AA, Fadahunsi AA, Alakanse OS. Identification of a Potential mRNA-based Vaccine Candidate against the SARS-CoV-2 Spike Glycoprotein: A Reverse Vaccinology Approach. ChemistrySelect 2022; 7:e202103903. [PMID: 35601809 PMCID: PMC9111088 DOI: 10.1002/slct.202103903] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/04/2022] [Indexed: 12/11/2022]
Abstract
The emergence of the novel coronavirus (SARS-CoV-2) in December 2019 has generated a devastating global consequence which makes the development of a rapidly deployable, effective and safe vaccine candidate an imminent global health priority. The design of most vaccine candidates has been directed at the induction of antibody responses against the trimeric spike glycoprotein of SARS-CoV-2, a class I fusion protein that aids ACE2 (angiotensin-converting enzyme 2) receptor binding. A variety of formulations and vaccinology approaches are being pursued for targeting the spike glycoprotein, including simian and human replication-defective adenoviral vaccines, subunit protein vaccines, nucleic acid vaccines and whole-inactivated SARS-CoV-2. Here, we directed a reverse vaccinology approach towards the design of a nucleic acid (mRNA-based) vaccine candidate. The "YLQPRTFLL" peptide sequence (position 269-277) which was predicted to be a B cell epitope and likewise a strong binder of the HLA*A-0201 was selected for the design of the vaccine candidate, having satisfied series of antigenicity assessments. Through the codon optimization protocol, the nucleotide sequence for the vaccine candidate design was generated and targeted at the human toll-like receptor 7 (TLR7). Bioinformatics analyses showed that the sequence "UACCUGCAGCCGCGUACCUUCCUGCUG" exhibited a strong affinity and likewise was bound to a stable cavity in the TLR7 pocket. This study is therefore expected to contribute to the research efforts directed at securing definitive preventive measures against the SARS-CoV-2 infection.
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Affiliation(s)
- Olanrewaju Ayodeji Durojaye
- MOE Key Laboratory of Membraneless Organelle and Cellular DynamicsHefei National Laboratory for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefeiAnhui230027China
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
- Department of Chemical SciencesCoal City University, EmeneEnugu StateNigeria
| | - Divine Mensah Sedzro
- MOE Key Laboratory of Membraneless Organelle and Cellular DynamicsHefei National Laboratory for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefeiAnhui230027China
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
| | | | - Abeeb Abiodun Yekeen
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
| | - Adeola Abraham Fadahunsi
- Department of Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiAnhui230027China
| | - Oluwaseun Suleiman Alakanse
- School of Life SciencesUniversity of Science and Technology of ChinaHefeiAnhui230027China
- Department of BiochemistryFaculty of Life SciencesUniversity of IlorinIlorinKwara StateNigeria
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30
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Fan J, Liu Y, Kong R, Ni D, Yu Z, Lu S, Zhang J. Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery. J Med Chem 2021; 64:17728-17743. [PMID: 34878270 DOI: 10.1021/acs.jmedchem.1c01695] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.
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Affiliation(s)
- Jigang Fan
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Zhiyuan Innovative Research Center, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Duan Ni
- The Charles Perkins Centre, University of Sydney, Sydney, New South Wales 2006, Australia
| | | | - Shaoyong Lu
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
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31
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Investigation of glutathione as a natural antioxidant and multitarget inhibitor for Alzheimer’s disease: Insights from molecular simulations. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117960] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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32
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Ni D, Chai Z, Wang Y, Li M, Yu Z, Liu Y, Lu S, Zhang J. Along the allostery stream: Recent advances in computational methods for allosteric drug discovery. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1585] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Duan Ni
- College of Pharmacy Ningxia Medical University Yinchuan China
- The Charles Perkins Centre University of Sydney Sydney New South Wales Australia
| | - Zongtao Chai
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital Second Military Medical University Shanghai China
| | - Ying Wang
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Mingyu Li
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
| | | | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Shaoyong Lu
- College of Pharmacy Ningxia Medical University Yinchuan China
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jian Zhang
- College of Pharmacy Ningxia Medical University Yinchuan China
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
- School of Pharmaceutical Sciences Zhengzhou University Zhengzhou China
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33
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Chen N, Wen J, Wang Z, Wang J. Multiple regulation and targeting effects of borneol in the neurovascular unit in neurodegenerative diseases. Basic Clin Pharmacol Toxicol 2021; 130:5-19. [PMID: 34491621 DOI: 10.1111/bcpt.13656] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 11/27/2022]
Abstract
Efficient delivery of brain-targeted drugs is highly important for the success of therapies in neurodegenerative diseases. Borneol has several biological activities, such as anti-inflammatory and cell penetration enhancing effect, and can regulate processes in the neurovascular unit (NVU), such as protein toxic stress, autophagosome/lysosomal system, oxidative stress, programmed cell death and neuroinflammation. However, the influence of borneol on NVU in neurodegenerative diseases has not been fully explained. This study searched the keywords 'borneol', 'neurovascular unit', 'endothelial cell', 'astrocyte', 'neuron', 'blood-brain barrier', 'neurodegenerative diseases' and 'brain disease', in PubMed, BioMed Central, China National Knowledge Infrastructure (CNKI), and Bing search engines to explore the influence of borneol on NVU. In addition to the principle and mechanism of penetration of borneol in the brain, this study also showed its multiple regulation effects on NVU. Borneol was able to penetrate the blood-brain barrier (BBB), affecting the signal transmission between BBB and the microenvironment of the brain, down-regulating the expression of inflammatory and oxidative stress proteins in NVU, especially in microglia and astrocytes. In summary, borneol is a potential drug delivery agent for drugs against neurodegenerative diseases.
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Affiliation(s)
- Nian Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jing Wen
- Department of Pharmacology, North Sichuan Medical College, Nanchong, China
| | - Zhilei Wang
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jian Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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34
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Evans DJ, Yovanno RA, Rahman S, Cao DW, Beckett MQ, Patel MH, Bandak AF, Lau AY. Finding Druggable Sites in Proteins Using TACTICS. J Chem Inf Model 2021; 61:2897-2910. [PMID: 34096704 DOI: 10.1021/acs.jcim.1c00204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.
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Affiliation(s)
- Daniel J Evans
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Remy A Yovanno
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Sanim Rahman
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - David W Cao
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Morgan Q Beckett
- Department of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Milan H Patel
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Afif F Bandak
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Albert Y Lau
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
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35
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Qiu Y, Yin X, Li X, Wang Y, Fu Q, Huang R, Lu S. Untangling Dual-Targeting Therapeutic Mechanism of Epidermal Growth Factor Receptor (EGFR) Based on Reversed Allosteric Communication. Pharmaceutics 2021; 13:747. [PMID: 34070173 PMCID: PMC8158526 DOI: 10.3390/pharmaceutics13050747] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/12/2021] [Accepted: 04/21/2021] [Indexed: 12/18/2022] Open
Abstract
Dual-targeting therapeutics by coadministration of allosteric and orthosteric drugs is drawing increased attention as a revolutionary strategy for overcoming the drug-resistance problems. It was further observed that the occupation of orthosteric sites by therapeutics agents has the potential to enhance allosteric ligand binding, which leads to improved potency of allosteric drugs. Epidermal growth factor receptor (EGFR), as one of the most critical anti-cancer targets belonging to the receptor tyrosine kinase family, represents a quintessential example. It was revealed that osimertinib, an ATP-competitive covalent EGFR inhibitor, remarkably enhanced the affinity of a recently developed allosteric inhibitor JBJ-04-125-02 for EGFRL858R/T790M. Here, we utilized extensive large-scale molecular dynamics simulations and the reversed allosteric communication to untangle the detailed molecular underpinning, in which occupation of osimertinib at the orthosteric site altered the overall conformational ensemble of EGFR mutant and reshaped the allosteric site via long-distance signaling. A unique intermediate state resembling the active conformation was identified, which was further stabilized by osimertinib loading. Based on the allosteric communication pathway, we predicted a novel allosteric site positioned around K867, E868, H893, and K960 within the intermediate state. Its correlation with the orthosteric site was validated by both structural and energetic analysis, and its low sequence conservation indicated the potential for selective targeting across the human kinome. Together, these findings not only provided a mechanistic basis for future clinical application of the dual-targeting therapeutics, but also explored an innovative perception of allosteric inhibition of tyrosine kinase signaling.
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Affiliation(s)
- Yuran Qiu
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Department of Pathophysiology, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (Y.Q.); (X.L.); (Y.W.)
| | - Xiaolan Yin
- Department of Radiotherapy, Changhai Hospital (Hongkou District), Naval Medical University, Shanghai 200081, China;
| | - Xinyi Li
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Department of Pathophysiology, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (Y.Q.); (X.L.); (Y.W.)
| | - Yuanhao Wang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Department of Pathophysiology, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (Y.Q.); (X.L.); (Y.W.)
| | - Qiang Fu
- Department of Orthopedics, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
| | - Renhua Huang
- Department of Radiation, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Shaoyong Lu
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Department of Pathophysiology, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (Y.Q.); (X.L.); (Y.W.)
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36
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Wang X, Zhang X, Peng C, Shi Y, Li H, Xu Z, Zhu W. D3DistalMutation: a Database to Explore the Effect of Distal Mutations on Enzyme Activity. J Chem Inf Model 2021; 61:2499-2508. [PMID: 33938221 DOI: 10.1021/acs.jcim.1c00318] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Enzyme activity is affected by amino acid mutations, particularly mutations near the active site. Increasing evidence has shown that distal mutations more than 10 Å away from the active site may significantly affect enzyme activity. However, it is difficult to study the enzyme regulation mechanism of distal mutations due to the lack of a systematic collection of three-dimensional (3D) structures, highlighting distal mutation site and the corresponding enzyme activity change. Therefore, we constructed a distal mutation database, namely, D3DistalMutation, which relates the distal mutation to enzyme activity. As a result, we observed that approximately 80% of distal mutations could affect enzyme activity and 72.7% of distal mutations would decrease or abolish enzyme activity in D3DistalMutation. Only 6.6% of distal mutations in D3DistalMutation could increase enzyme activity, which have great potential to the industrial field. Among these mutations, the Y to F, S to D, and T to D mutations are most likely to increase enzyme activity, which sheds some light on industrial catalysis. Distal mutations decreasing enzyme activity in the allosteric pocket play an indispensable role in allosteric drug design. In addition, the pockets in the enzyme structures are provided to explore the enzyme regulation mechanism of distal mutations. D3DistalMutation is accessible free of charge at https://www.d3pharma.com/D3DistalMutation/index.php.
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Affiliation(s)
- Xiaoyu Wang
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.,College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 200090, China
| | - Xinben Zhang
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Cheng Peng
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yulong Shi
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Huiyu Li
- College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 200090, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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37
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Yang Y, Zhu Z, Wang X, Zhang X, Mu K, Shi Y, Peng C, Xu Z, Zhu W. Ligand-based approach for predicting drug targets and for virtual screening against COVID-19. Brief Bioinform 2021; 22:1053-1064. [PMID: 33461215 PMCID: PMC7929377 DOI: 10.1093/bib/bbaa422] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/18/2020] [Accepted: 12/19/2020] [Indexed: 01/18/2023] Open
Abstract
Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.
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Affiliation(s)
- Yanqing Yang
- Shanghai Institute of Materia Medica.,CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China
| | - Zhengdan Zhu
- Shanghai Institute of Materia Medica in 2020. His research interest is halogen bond interaction. His affiliation is with CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China
| | - Xiaoyu Wang
- Shanghai University of Electric Power. Her research interest is database construction. Her affiliation is with College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 200090, China
| | - Xinben Zhang
- East China University of Science and Technology. His research interest is software development. His affiliation is with CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Kaijie Mu
- Nano Science and Technology Institute, University of Science and Technology of China. Her research interest is QM/MM calculations and molecular modeling. Her affiliation is with Nano Science and Technology Institute, University of Science and Technology of China, Suzhou, Jiangsu, 215123, China
| | - Yulong Shi
- Shanghai Institute of Materia Medica. His research interest is molecular docking method development. His affiliation is with CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China
| | - Cheng Peng
- Shanghai Institute of Materia Medica. His research interest is molecular dynamics. His affiliation is with CAS Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; School of Pharmacy, University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China
| | - Zhijian Xu
- Shanghai Institute of Materia Medica in 2012
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38
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González-Durruthy M, Rial R, Cordeiro MND, Liu Z, Ruso JM. Exploring the conformational binding mechanism of fibrinogen induced by interactions with penicillin β-lactam antibiotic drugs. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2020.114667] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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39
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Kangabam R, Sahoo S, Ghosh A, Roy R, Silla Y, Misra N, Suar M. Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery. Comput Biol Med 2021; 128:104158. [PMID: 33301953 PMCID: PMC7705366 DOI: 10.1016/j.compbiomed.2020.104158] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/25/2020] [Accepted: 11/25/2020] [Indexed: 12/14/2022]
Abstract
The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.
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Affiliation(s)
- Rajiv Kangabam
- KIIT-Technology Business Incubator (KIIT-TBI), Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India
| | - Susrita Sahoo
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India
| | - Arpan Ghosh
- KIIT-Technology Business Incubator (KIIT-TBI), Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India; School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India
| | - Riya Roy
- KIIT-Technology Business Incubator (KIIT-TBI), Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India
| | - Yumnam Silla
- Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat, 785006, India
| | - Namrata Misra
- KIIT-Technology Business Incubator (KIIT-TBI), Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India; School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India
| | - Mrutyunjay Suar
- KIIT-Technology Business Incubator (KIIT-TBI), Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India; School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to Be University, Bhubaneswar, 751024, India.
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40
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Renault P, Giraldo J. Dynamical Correlations Reveal Allosteric Sites in G Protein-Coupled Receptors. Int J Mol Sci 2020; 22:ijms22010187. [PMID: 33375427 PMCID: PMC7795036 DOI: 10.3390/ijms22010187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 01/14/2023] Open
Abstract
G protein-coupled Receptors (GPCRs) play a central role in many physiological processes and, consequently, constitute important drug targets. In particular, the search for allosteric drugs has recently drawn attention, since they could be more selective and lead to fewer side effects. Accordingly, computational tools have been used to estimate the druggability of allosteric sites in these receptors. In spite of many successful results, the problem is still challenging, particularly the prediction of hydrophobic sites in the interface between the protein and the membrane. In this work, we propose a complementary approach, based on dynamical correlations. Our basic hypothesis was that allosteric sites are strongly coupled to regions of the receptor that undergo important conformational changes upon activation. Therefore, using ensembles of experimental structures, normal mode analysis and molecular dynamics simulations we calculated correlations between internal fluctuations of different sites and a collective variable describing the activation state of the receptor. Then, we ranked the sites based on the strength of their coupling to the collective dynamics. In the β2 adrenergic (β2AR), glucagon (GCGR) and M2 muscarinic receptors, this procedure allowed us to correctly identify known allosteric sites, suggesting it has predictive value. Our results indicate that this dynamics-based approach can be a complementary tool to the existing toolbox to characterize allosteric sites in GPCRs.
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Affiliation(s)
- Pedro Renault
- Laboratory of Molecular Neuropharmacology and Bioinformatics, Unitat de Bioestadística and Institut de Neurociències, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain;
- Unitat de Neurociència Traslacional, Parc Taulí Hospital Universitari, Institut d’Investigació i Innovació Parc Taulí (I3PT), Institut de Neurociències, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
- Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, 08193 Bellaterra, Spain
| | - Jesús Giraldo
- Laboratory of Molecular Neuropharmacology and Bioinformatics, Unitat de Bioestadística and Institut de Neurociències, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain;
- Unitat de Neurociència Traslacional, Parc Taulí Hospital Universitari, Institut d’Investigació i Innovació Parc Taulí (I3PT), Institut de Neurociències, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
- Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, 08193 Bellaterra, Spain
- Correspondence:
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41
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Peng C, Zhu Z, Shi Y, Wang X, Mu K, Yang Y, Zhang X, Xu Z, Zhu W. Computational Insights into the Conformational Accessibility and Binding Strength of SARS-CoV-2 Spike Protein to Human Angiotensin-Converting Enzyme 2. J Phys Chem Lett 2020; 11:10482-10488. [PMID: 33274945 PMCID: PMC7737396 DOI: 10.1021/acs.jpclett.0c02958] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 11/24/2020] [Indexed: 05/08/2023]
Abstract
The spike protein of SARS-CoV-2 (CoV-2-S) mediates the virus entry into human cells. Experimental studies have shown the stronger binding affinity of the RBD (receptor binding domain) of CoV-2-S to angiotensin-converting enzyme 2 (ACE2) as compared to that of SARS-CoV spike (CoV-S). However, a similar or weaker binding affinity of CoV-2-S compared to that of CoV-S is observed if entire spikes are used in the bioassay. To explore the underlying mechanism, we calculated the binding affinities of the RBDs to ACE2 and simulated the transitions between ACE2-inaccessible and -accessible conformations. We found that the ACE2-accessible angle of CoV-2-S is 52.2° and that the ACE2 binding strength of CoV-2-S RBD is much stronger than that of CoV-S RBD. However, CoV-2-S has much less of an ACE2-accessible conformation and is much more difficult to shift from ACE2-inaccessible to -accessible than CoV-S, making the binding affinity of the entire protein decrease. Further analysis revealed key interactional residues for strong binding and five potential ligand-binding pockets for drug research.
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Affiliation(s)
- Cheng Peng
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- School
of Pharmacy, University of Chinese Academy
of Sciences, No. 19A
Yuquan Road, Beijing 100049, China
| | - Zhengdan Zhu
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- School
of Pharmacy, University of Chinese Academy
of Sciences, No. 19A
Yuquan Road, Beijing 100049, China
| | - Yulong Shi
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- School
of Pharmacy, University of Chinese Academy
of Sciences, No. 19A
Yuquan Road, Beijing 100049, China
| | - Xiaoyu Wang
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- College
of Mathematics and Physics, Shanghai University
of Electric Power, Shanghai 200090, China
| | - Kaijie Mu
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- Nano
Science and Technology Institute, University
of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Yanqing Yang
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- School
of Pharmacy, University of Chinese Academy
of Sciences, No. 19A
Yuquan Road, Beijing 100049, China
| | - Xinben Zhang
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
| | - Zhijian Xu
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- School
of Pharmacy, University of Chinese Academy
of Sciences, No. 19A
Yuquan Road, Beijing 100049, China
| | - Weiliang Zhu
- CAS
Key Laboratory of Receptor Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- School
of Pharmacy, University of Chinese Academy
of Sciences, No. 19A
Yuquan Road, Beijing 100049, China
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Ni D, Wei J, He X, Rehman AU, Li X, Qiu Y, Pu J, Lu S, Zhang J. Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy. Chem Sci 2020; 12:464-476. [PMID: 34163609 PMCID: PMC8178949 DOI: 10.1039/d0sc05131d] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Allostery, which is one of the most direct and efficient methods to fine-tune protein functions, has gained increasing recognition in drug discovery. However, there are several challenges associated with the identification of allosteric sites, which is the fundamental cornerstone of drug design. Previous studies on allosteric site predictions have focused on communication signals propagating from the allosteric sites to the orthosteric sites. However, recent biochemical studies have revealed that allosteric coupling is bidirectional and that orthosteric perturbations can modulate allosteric sites through reversed allosteric communication. Here, we proposed a new framework for the prediction of allosteric sites based on reversed allosteric communication using a combination of computational and experimental strategies (molecular dynamics simulations, Markov state models, and site-directed mutagenesis). The desirable performance of our approach was demonstrated by predicting the known allosteric site of the small molecule MDL-801 in nicotinamide dinucleotide (NAD+)-dependent protein lysine deacetylase sirtuin 6 (Sirt6). A potential novel cryptic allosteric site located around the L116, R119, and S120 residues within the dynamic ensemble of Sirt6 was identified. The allosteric effect of the predicted site was further quantified and validated using both computational and experimental approaches. This study proposed a state-of-the-art computational pipeline for detecting allosteric sites based on reversed allosteric communication. This method enabled the identification of a previously uncharacterized potential cryptic allosteric site on Sirt6, which provides a starting point for allosteric drug design that can aid the identification of candidate pockets in other therapeutic targets. Using reversed allosteric communication, we performed MD simulations, MSMs, and mutagenesis experiments, to discover allosteric sites. It reproduced the known allosteric site for MDL-801 on Sirt6 and uncovered a novel cryptic allosteric Pocket X.![]()
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Affiliation(s)
- Duan Ni
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China .,The Charles Perkins Centre, University of Sydney Sydney NSW 2006 Australia
| | - Jiacheng Wei
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China
| | - Xinheng He
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China
| | - Ashfaq Ur Rehman
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China
| | - Xinyi Li
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China
| | - Yuran Qiu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China
| | - Jun Pu
- Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine Shanghai 200120 China
| | - Shaoyong Lu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China .,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China .,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China.,School of Pharmaceutical Sciences, Zhengzhou University Zhengzhou 450001 China
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SARS-CoV-2 transcriptome analysis and molecular cataloguing of immunodominant epitopes for multi-epitope based vaccine design. Genomics 2020; 112:5044-5054. [PMID: 32920121 PMCID: PMC7500163 DOI: 10.1016/j.ygeno.2020.09.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/18/2020] [Accepted: 09/08/2020] [Indexed: 01/12/2023]
Abstract
Genomics-led researches are engaged in tracing virus expression pattern, and induced immune responses in human to develop effective vaccine against COVID-19. In this study, targeted expression profiling and differential gene expression analysis of major histocompatibility complexes and innate immune system genes were performed through SARS-CoV-2 infected RNA-seq data of human cell line, and virus transcriptome was generated for T-and B-cell epitope prediction. Docking studies of epitopes with MHC and B-cell receptors were performed to identify potential T-and B-cell epitopes. Transcriptome analysis revealed the specific multiple allele expressions in cell line, genes for elicited induce immune response, and virus gene expression. Proposed T- and B-cell epitopes have high potential to elicit equivalent immune responses caused by SARS-CoV-2 infection which can be useful to provide links between elicited immune response and virus gene expression. This study will facilitate in vitro and in vivo vaccine related research studies in disease control. SARS-CoV-2 transcriptome construction from RNA-seq data of infected human cell-lines. T- and B-cell epitopes from SARS-CoV-2 transcriptome Gene expression profiling of MHC alleles and innate immune system genes
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44
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Zheng H, Zheng YC, Cui Y, Zhu JJ, Zhong JY. Study on effects of co-solvents on the structure of DhaA by molecular dynamics simulation. J Biomol Struct Dyn 2020; 39:5999-6007. [PMID: 32696722 DOI: 10.1080/07391102.2020.1796801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
With the increasing application of enzymes in various research fields, the choices of co-solvents in enzymatic preparations which directly related to the catalytic activity have been attracted attention. Thus, researching on the stabilization or destabilization behaviors of enzymes in different solvents is extremely essential. In this study, the structural changes of DhaA in two typical aprotic co-solvents (acetonitrile and tetrahydrofuran) were firstly investigated by molecular dynamics (MD) simulation. The simulation results revealed the strong van der Waals force between co-solvents and DhaA which could induce the structural change of enzyme. Interestingly, the differences of molecular size and the electrostatic force with enzyme of two co-solvents led to quite different influences on DhaA. As for acetonitrile, solvent molecules could penetrate into the catalytic site of DhaA which promoted by the electrostatic interaction. On the contrary, tetrahydrofuran molecules were mainly distributed around the catalytic site due to the relative weak electrostatic interaction and steric resistance effect. It can be concluded that different co-solvent can affect the key domains, substrate pathway and catalytic pocket of DhaA.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- He Zheng
- State Key Laboratory of NBC Protection for Civilian, Beijing, China
| | - Yong-Chao Zheng
- State Key Laboratory of NBC Protection for Civilian, Beijing, China
| | - Yan Cui
- State Key Laboratory of NBC Protection for Civilian, Beijing, China
| | - Jian-Jun Zhu
- State Key Laboratory of NBC Protection for Civilian, Beijing, China
| | - Jin-Yi Zhong
- State Key Laboratory of NBC Protection for Civilian, Beijing, China
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Shi Y, Zhang X, Mu K, Peng C, Zhu Z, Wang X, Yang Y, Xu Z, Zhu W. D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19. Acta Pharm Sin B 2020; 10:1239-1248. [PMID: 32318328 PMCID: PMC7169934 DOI: 10.1016/j.apsb.2020.04.006] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 03/23/2020] [Accepted: 03/25/2020] [Indexed: 01/16/2023] Open
Abstract
A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.
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Affiliation(s)
- Yulong Shi
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinben Zhang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Kaijie Mu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou 215123, China
| | - Cheng Peng
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhengdan Zhu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyu Wang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yanqing Yang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing 100049, China
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Du J, Guo J, Kang D, Li Z, Wang G, Wu J, Zhang Z, Fang H, Hou X, Huang Z, Li G, Lu X, Liu X, Ouyang L, Rao L, Zhan P, Zhang X, Zhang Y. New techniques and strategies in drug discovery. CHINESE CHEM LETT 2020. [DOI: 10.1016/j.cclet.2020.03.028] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Yan F, Gao F. A systematic strategy for the investigation of vaccines and drugs targeting bacteria. Comput Struct Biotechnol J 2020; 18:1525-1538. [PMID: 32637049 PMCID: PMC7327267 DOI: 10.1016/j.csbj.2020.06.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/02/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023] Open
Abstract
Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.
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Affiliation(s)
- Fangfang Yan
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
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