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Jiang C, He X, Wang Y, Chen CJ, Othman Y, Hao Y, Yuan J, Xie XQ, Feng Z. Molecular Modeling Study of a Receptor-Orthosteric Ligand-Allosteric Modulator Signaling Complex. ACS Chem Neurosci 2023; 14:418-434. [PMID: 36692197 PMCID: PMC10032570 DOI: 10.1021/acschemneuro.2c00554] [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: 09/12/2022] [Accepted: 01/09/2023] [Indexed: 01/25/2023] Open
Abstract
Allosteric modulators (AMs) are considered as a perpetual hotspot in research for their higher selectivity and various effects on orthosteric ligands (OL). They are classified in terms of their functionalities as positive, negative, or silent allosteric modulators (PAM, NAM, or SAM, respectively). In the present work, 11 pairs of three-dimensional (3D) structures of receptor-orthosteric ligand and receptor-orthosteric ligand-allosteric modulator complexes have been collected for the studies, including three different systems: GPCR, enzyme, and ion channel. Molecular dynamics (MD) simulations are applied to quantify the dynamic interactions in both the orthosteric and allosteric binding pockets and the structural fluctuation of the involved proteins. Our results showed that MD simulations of moderately large molecules or peptides undergo insignificant changes compared to crystal structure results. Furthermore, we also studied the conformational changes of receptors that bound with PAM and NAM, as well as the different allosteric binding sites in a receptor. There should be no preference for the position of the allosteric binding pocket after comparing the allosteric binding pockets of these three systems. Finally, we aligned four distinct β2 adrenoceptor structures and three N-methyl-d-aspartate receptor (NMDAR) structures to investigate conformational changes. In the β2 adrenoceptor systems, the aligned results revealed that transmembrane (TM) helices 1, 5, and 6 gradually increased outward movement from an enhanced inactive state to an improved active state. TM6 endured the most significant conformational changes (around 11 Å). For NMDAR, the bottom section of NMDAR's ligand-binding domain (LBD) experienced an upward and outward shift during the gradually activating process. In conclusion, our research provides insight into receptor-orthosteric ligand-allosteric modulator studies and the design and development of allosteric modulator drugs using MD simulation.
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Affiliation(s)
- Chen Jiang
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Xibing He
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Yuanqiang Wang
- School
of Pharmacy and Bioengineering, Chongqing
University of Technology, Chongqing400054, China
- Chongqing
Key Laboratory of Medicinal Chemistry and Molecular Pharmacology, Chongqing400054, China
- Chongqing
Key Laboratory of Target Based Drug Screening and Effect Evaluation, Chongqing400054, China
| | - Chih-Jung Chen
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Yasmin Othman
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Yixuan Hao
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Jiayi Yuan
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Xiang-Qun Xie
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
| | - Zhiwei Feng
- Department
of Pharmaceutical Sciences and Computational Chemical Genomics Screening
Center, Pharmacometrics & System Pharmacology (PSP) PharmacoAnalytics,
School of Pharmacy; National Center of Excellence for Computational
Drug Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania15261, United States
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2
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Cheng J, Hao Y, Shi Q, Hou G, Wang Y, Wang Y, Xiao W, Othman J, Qi J, Wang Y, Chen Y, Yu G. Discovery of Novel Chinese Medicine Compounds Targeting 3CL Protease by Virtual Screening and Molecular Dynamics Simulation. Molecules 2023; 28:molecules28030937. [PMID: 36770604 PMCID: PMC9921503 DOI: 10.3390/molecules28030937] [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: 10/31/2022] [Revised: 12/23/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
The transmission and infectivity of COVID-19 have caused a pandemic that has lasted for several years. This is due to the constantly changing variants and subvariants that have evolved rapidly from SARS-CoV-2. To discover drugs with therapeutic potential for COVID-19, we focused on the 3CL protease (3CLpro) of SARS-CoV-2, which has been proven to be an important target for COVID-19 infection. Computational prediction techniques are quick and accurate enough to facilitate the discovery of drugs against the 3CLpro of SARS-CoV-2. In this paper, we used both ligand-based virtual screening and structure-based virtual screening to screen the traditional Chinese medicine small molecules that have the potential to target the 3CLpro of SARS-CoV-2. MD simulations were used to confirm these results for future in vitro testing. MCCS was then used to calculate the normalized free energy of each ligand and the residue energy contribution. As a result, we found ZINC15676170, ZINC09033700, and ZINC12530139 to be the most promising antiviral therapies against the 3CLpro of SARS-CoV-2.
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Affiliation(s)
- Jin Cheng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng 224005, China
| | - Yixuan Hao
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Qin Shi
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng 224005, China
| | - Guanyu Hou
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yanan Wang
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng 224005, China
| | - Yong Wang
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng 224005, China
| | - Wen Xiao
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng 224005, China
| | - Joseph Othman
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Junnan Qi
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yuanqiang Wang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing 400054, China
- Correspondence: (Y.W.); (Y.C.); (G.Y.); Tel.: +86-2362563190 (Y.W.); +86-57188813483 (Y.C.); +86-13401772896 (G.Y.)
| | - Yan Chen
- College of Pharmacology Sciences, Zhejiang University of Technology, Hangzhou 310014, China
- Correspondence: (Y.W.); (Y.C.); (G.Y.); Tel.: +86-2362563190 (Y.W.); +86-57188813483 (Y.C.); +86-13401772896 (G.Y.)
| | - Guanghua Yu
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng 224005, China
- Correspondence: (Y.W.); (Y.C.); (G.Y.); Tel.: +86-2362563190 (Y.W.); +86-57188813483 (Y.C.); +86-13401772896 (G.Y.)
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3
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Hao Y, Chen M, Othman Y, Xie XQ, Feng Z. Virus-CKB 2.0: Viral-Associated Disease-Specific Chemogenomics Knowledgebase. ACS OMEGA 2022; 7:37476-37484. [PMID: 36312370 PMCID: PMC9609052 DOI: 10.1021/acsomega.2c04258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Transmissible and infectious viruses can cause large-scale epidemics around the world. This is because the virus can constantly mutate and produce different variants and subvariants to counter existing treatments. Therefore, a variety of treatments are urgently needed to keep up with the mutation of the viruses. To facilitate the research of such treatment, we updated our Virus-CKB 1.0 to Virus-CKB 2.0, which contains 10 kinds of viruses, including enterovirus, dengue virus, hepatitis C virus, Zika virus, herpes simplex virus, Andes orthohantavirus, human immunodeficiency virus, Ebola virus, Lassa virus, influenza virus, coronavirus, and norovirus. To date, Virus-CKB 2.0 archived at least 65 antiviral drugs (such as remdesivir, telaprevir, acyclovir, boceprevir, and nelfinavir) in the market, 178 viral-related targets with 292 available 3D crystal or cryo-EM structures, and 3766 chemical agents reported for these target proteins. Virus-CKB 2.0 is integrated with established tools for target prediction and result visualization; these include HTDocking, TargetHunter, blood-brain barrier (BBB) predictor, Spider Plot, etc. The Virus-CKB 2.0 server is accessible at https://www.cbligand.org/g/virus-ckb. By using the established chemogenomic tools and algorithms and newly developed tools, we can screen FDA-approved drugs and chemical compounds that may bind to these proteins involved in viral-associated disease regulation. If the virus strain mutates and the vaccine loses its effect, we can still screen drugs that can be used to treat the mutated virus in a fleeting time. In some cases, we can even repurpose FDA-approved drugs through Virus-CKB 2.0.
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Affiliation(s)
| | | | - Yasmin Othman
- Department of Pharmaceutical
Sciences and Computational Chemical Genomics Screening Center, School
of Pharmacy; National Center of Excellence for Computational Drug
Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical
Sciences and Computational Chemical Genomics Screening Center, School
of Pharmacy; National Center of Excellence for Computational Drug
Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhiwei Feng
- Department of Pharmaceutical
Sciences and Computational Chemical Genomics Screening Center, School
of Pharmacy; National Center of Excellence for Computational Drug
Abuse Research; Drug Discovery Institute; Departments of Computational
Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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4
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Elucidation of Binding Features and Dissociation Pathways of Inhibitors and Modulators in SARS-CoV-2 Main Protease by Multiple Molecular Dynamics Simulations. Molecules 2022; 27:molecules27206823. [PMID: 36296416 PMCID: PMC9609290 DOI: 10.3390/molecules27206823] [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: 09/07/2022] [Revised: 10/03/2022] [Accepted: 10/10/2022] [Indexed: 01/18/2023] Open
Abstract
COVID-19 can cause different neurological symptoms in some people, including smell, inability to taste, dizziness, confusion, delirium, seizures, stroke, etc. Owing to the issue of vaccine effectiveness, update and coverage, we still need one or more diversified strategies as the backstop to manage illness. Characterizing the structural basis of ligand recognition in the main protease (Mpro) of SARS-CoV-2 will facilitate its rational design and development of potential drug candidates with high affinity and selectivity against COVID-19. Up to date, covalent-, non-covalent inhibitors and allosteric modulators have been reported to bind to different active sites of Mpro. In the present work, we applied the molecular dynamics (MD) simulations to systematically characterize the potential binding features of catalytic active site and allosteric binding sites in Mpro using a dataset of 163 3D structures of Mpro-inhibitor complexes, in which our results are consistent with the current studies. In addition, umbrella sampling (US) simulations were used to explore the dissociation processes of substrate pathway and allosteric pathway. All the information provided new insights into the protein features of Mpro and will facilitate its rational drug design for COVID-19.
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Chen CJ, Jiang C, Yuan J, Chen M, Cuyler J, Xie XQ, Feng Z. How Do Modulators Affect the Orthosteric and Allosteric Binding Pockets? ACS Chem Neurosci 2022; 13:959-977. [PMID: 35298129 PMCID: PMC10496248 DOI: 10.1021/acschemneuro.1c00749] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Allosteric modulators (AMs) that bind allosteric sites can exhibit greater selectivity than the orthosteric ligands and can either enhance agonist-induced receptor activity (termed positive allosteric modulator or PAM), inhibit agonist-induced activity (negative AM or NAM), or have no effect on activity (silent AM or SAM). Until now, it is not clear what the exact effects of AMs are on the orthosteric active site or the allosteric binding pocket(s). In the present work, we collected both the three-dimensional (3D) structures of receptor-orthosteric ligand and receptor-orthosteric ligand-AM complexes of a specific target protein. Using our novel algorithm toolset, molecular complex characterizing system (MCCS), we were able to quantify the key residues in both the orthosteric and allosteric binding sites along with potential changes of the binding pockets. After analyzing 21 pairs of 3D crystal or cryo-electron microscopy (cryo-EM) complexes, including 4 pairs of GPCRs, 5 pairs of ion channels, 11 pairs of enzymes, and 1 pair of transcription factors, we found that the binding of AMs had little impact on both the orthosteric and allosteric binding pockets. In return, given the accurately predicted allosteric binding pocket(s) of a drug target of medicinal interest, we can confidently conduct the virtual screening or lead optimization without concern that the huge conformational change of the pocket could lead to the low accuracy of virtual screening.
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Affiliation(s)
- Chih-Jung Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Chen Jiang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Jiayi Yuan
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Maozi Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Jacob Cuyler
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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Wahiduzzaman M, Liu Y, Huang T, Wei W, Li Y. Cell-cell communication analysis for single-cell RNA sequencing and its applications in carcinogenesis and COVID-19. BIOSAFETY AND HEALTH 2022. [DOI: 10.1016/j.bsheal.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Yuan J, Jiang C, Wang J, Chen CJ, Hao Y, Zhao G, Feng Z, Xie XQ. In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators. Molecules 2022; 27:molecules27020453. [PMID: 35056767 PMCID: PMC8781014 DOI: 10.3390/molecules27020453] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/01/2022] [Accepted: 01/03/2022] [Indexed: 11/16/2022] Open
Abstract
Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-β-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future.
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Affiliation(s)
- Jiayi Yuan
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Chen Jiang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Chih-Jung Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yixuan Hao
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Guangyi Zhao
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Correspondence: (Z.F.); (X.-Q.X.)
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (J.Y.); (C.J.); (J.W.); (C.-J.C.); (Y.H.); (G.Z.)
- Department of Pharmaceutical Sciences and National Center of Excellence for Computational Drug Abuse Research, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Correspondence: (Z.F.); (X.-Q.X.)
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Cheng J, Chen M, Wang S, Liang T, Chen H, Chen CJ, Feng Z, Xie XQ. Binding Characterization of Agonists and Antagonists by MCCS: A Case Study from Adenosine A 2A Receptor. ACS Chem Neurosci 2021; 12:1606-1620. [PMID: 33856784 DOI: 10.1021/acschemneuro.1c00082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Characterizing the structural basis of ligand recognition of adenosine A2A receptor (AA2AR) will facilitate its rational design and development of small molecules with high affinity and selectivity, as well as optimal therapeutic effects for pain, cancers, drug abuse disorders, etc. In the present work, we applied our reported algorithm, molecular complex characterizing system (MCCS), to characterize the binding features of AA2AR based on its reported 3D structures of protein-ligand complexes. First, we compared the binding score to the reported experimental binding affinities of each compound. Then, we calculated an output example of residue energy contribution using MCCS and compared the results with data obtained from MM/GBSA. The consistency in results indicated that MCCS is a powerful, fast, and accurate method. Sequentially, using a receptor-ligand data set of 57 crystallized structures of AA2ARs, we characterized the binding features of the binding pockets in AA2AR, summarized the key residues that distinguish antagonist from agonist, produced heatmaps of residue energy contribution for clustering various statuses of AA2ARs, explored the selectivity between AA2AR and AA1AR, etc. All the information provided new insights into the protein features of AA2AR and will facilitate its rational drug design.
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Affiliation(s)
- Jin Cheng
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.,Department of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, Jiangsu 224005, China
| | - Maozi Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Siyi Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Tianjian Liang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Hui Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Chih-Jung Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, and National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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