1
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Zhao L, Wang J, Yang W, Zhao K, Sun Q, Chen J. Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning. Molecules 2024; 29:2681. [PMID: 38893554 PMCID: PMC11174096 DOI: 10.3390/molecules29112681] [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: 05/08/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024] Open
Abstract
CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy landscapes (FELs). DL finds that the binding pocket as well as the T-loop binding to the Vcyclin protein are involved in obvious differences of conformation contacts. This result suggests that the binding pocket of inhibitors (LQQ and AP9) and the binding interface of CDK6 to the Vcyclin protein play a key role in the function of CDK6. The analyses of FELs reveal that the binding pocket and the T-loop of CDK6 have disordered states. The results from principal component analysis (PCA) indicate that the binding of the Vcyclin protein affects the fluctuation behavior of the T-loop in CDK6. Our QM/MM-GBSA calculations suggest that the binding ability of LQQ to CDK6 is stronger than AP9 with or without the binding of the Vcyclin protein. Interaction networks of inhibitors with CDK6 were analyzed and the results reveal that LQQ contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. In addition, the binding pocket endures flexibility changes from opening to closing states and the Vcyclin protein plays an important role in the stabilizing conformation of the T-loop. We anticipate that this work could provide useful information for further understanding the function of CDK6 and developing new promising inhibitors targeting CDK6.
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Affiliation(s)
- Lu Zhao
- School of Science, Shandong Jiaotong University, Jinan 250357, China; (J.W.); (W.Y.); (K.Z.); (Q.S.)
| | | | | | | | | | - Jianzhong Chen
- School of Science, Shandong Jiaotong University, Jinan 250357, China; (J.W.); (W.Y.); (K.Z.); (Q.S.)
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2
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Do HN, Wang J, Miao Y. Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors. JACS AU 2023; 3:3165-3180. [PMID: 38034960 PMCID: PMC10685416 DOI: 10.1021/jacsau.3c00503] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 12/02/2023]
Abstract
G-protein-coupled receptors (GPCRs) make up the largest superfamily of human membrane proteins and represent primary targets of ∼1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon the binding of positive and negative allosteric modulators (PAMs and NAMs). The mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have systematically mapped dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators using the Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy prOfiling Workflow (GLOW). GaMD simulations were performed for a total of 66 μs on 44 GPCR systems in the presence and absence of the modulator. DL and free energy calculations revealed significantly reduced dynamic fluctuations and conformational space of GPCRs upon modulator binding. While the modulator-free GPCRs often sampled multiple low-energy conformational states, the NAMs and PAMs confined the inactive and active agonist-G-protein-bound GPCRs, respectively, to mostly only one specific conformation for signaling. Such cooperative effects were significantly reduced for binding of the selective modulators to "non-cognate" receptor subtypes. Therefore, GPCR allostery exhibits a dynamic "conformational selection" mechanism. In the absence of available modulator-bound structures as for most current GPCRs, it is critical to use a structural ensemble of representative GPCR conformations rather than a single structure for compound docking ("ensemble docking"), which will potentially improve structure-based design of novel allosteric drugs of GPCRs.
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Affiliation(s)
| | - Jinan Wang
- Computational Biology Program
and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
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3
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Wang J, Do HN, Koirala K, Miao Y. Predicting Biomolecular Binding Kinetics: A Review. J Chem Theory Comput 2023; 19:2135-2148. [PMID: 36989090 DOI: 10.1021/acs.jctc.2c01085] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Hung N Do
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Kushal Koirala
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
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4
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Do H, Wang J, Miao Y. Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors. RESEARCH SQUARE 2023:rs.3.rs-2543463. [PMID: 36865316 PMCID: PMC9980202 DOI: 10.21203/rs.3.rs-2543463/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
G-protein-coupled receptors (GPCRs) are the largest superfamily of human membrane proteins and represent primary targets of ~ 1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon binding of positive and negative allosteric modulators (PAMs and NAMs). Mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have systematically mapped dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators using the Gaussian accelerated molecular dynamics (GaMD), Deep Learning (DL) and free energy prOfiling Workflow (GLOW). A total of 18 available high-resolution experimental structures of allosteric modulator-bound class A and B GPCRs were collected for simulations. A number of 8 computational models were generated to examine selectivity of the modulators by changing their target receptors to different subtypes. All-atom GaMD simulations were performed for a total of 66 μs on 44 GPCR systems in the presence/absence of the modulator. DL and free energy calculations revealed significantly reduced conformational space of GPCRs upon modulator binding. While the modulator-free GPCRs often sampled multiple low-energy conformational states, the NAMs and PAMs confined the inactive and active agonist-G protein-bound GPCRs, respectively, to mostly only one specific conformation for signaling. Such cooperative effects were significantly reduced for binding of the selective modulators to "non-cognate" receptor subtypes in the computational models. Therefore, comprehensive DL of extensive GaMD simulations has revealed a general dynamic mechanism of GPCR allostery, which will greatly facilitate rational design of selective allosteric drugs of GPCRs.
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5
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Do HN, Wang J, Miao Y. Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.15.524128. [PMID: 36711515 PMCID: PMC9882226 DOI: 10.1101/2023.01.15.524128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
G-protein-coupled receptors (GPCRs) are the largest superfamily of human membrane proteins and represent primary targets of ~1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon binding of positive and negative allosteric modulators (PAMs and NAMs). Mechanism of dynamic allosteric modulation in GPCRs remains unclear. In this work, we have systematically mapped dynamic changes in free energy landscapes of GPCRs upon binding of allosteric modulators using the Gaussian accelerated molecular dynamics (GaMD), Deep Learning (DL) and free energy prOfiling Workflow (GLOW). A total of 18 available high-resolution experimental structures of allosteric modulator-bound class A and B GPCRs were collected for simulations. A number of 8 computational models were generated to examine selectivity of the modulators by changing their target receptors to different subtypes. All-atom GaMD simulations were performed for a total of 66 μs on 44 GPCR systems in the presence/absence of the modulator. DL and free energy calculations revealed significantly reduced conformational space of GPCRs upon modulator binding. While the modulator-free GPCRs often sampled multiple low-energy conformational states, the NAMs and PAMs confined the inactive and active agonist-G protein-bound GPCRs, respectively, to mostly only one specific conformation for signaling. Such cooperative effects were significantly reduced for binding of the selective modulators to "non-cognate" receptor subtypes in the computational models. Therefore, comprehensive DL of extensive GaMD simulations has revealed a general dynamic mechanism of GPCR allostery, which will greatly facilitate rational design of selective allosteric drugs of GPCRs.
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Affiliation(s)
- Hung N. Do
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
| | - Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047
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6
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Yasuda T, Morita R, Shigeta Y, Harada R. Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace. J Chem Inf Model 2022; 62:6217-6227. [PMID: 36449380 DOI: 10.1021/acs.jcim.2c01173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Since proteins perform biological functions through their dynamic properties, molecular dynamics (MD) simulation is a sophisticated strategy for investigating their functions. Analyses of trajectories provide statistical information about a specific protein as a free-energy landscape (FEL). However, the timescale of normal MD is shorter than that of biological functions, resulting in statistically insufficient conformational sampling, finally leading to unreliable FEL calculation. To search for a broad configurational subspace, an external bias is imposed on a target protein as biased sampling. However, its regulation is challenging because the optimal strength of the perturbation is unknown. Furthermore, a physically irrelevant configurational subspace was searched when imposing an inappropriate external bias. To address this issue, we newly proposed an external biased regulation scheme known as the G-factor external bias limiter (GERBIL). In GERBIL, protein configurations generated by external bias are structurally validated by an indicator (G-factor), enabling the search for a physically relevant subspace. In addition to biased sampling, nonbiased sampling might search for a physically irrelevant configurational subspace because repeating multiple MD simulations from several initial structures tends to search for an overly broad configurational subspace. For this issue, the structural qualities of configurations generated by nonbiased sampling have not been investigated. Therefore, we confirmed whether the G-factor screened the collapsed (low-quality) configurations generated by nonbiased sampling. To address this issue, the outlier flooding method (OFLOOD) was adopted in GERBIL as a nonbiased sampling method, which is referred to as OFLOOD-GERBIL. OFLOOD rapidly expands a configurational subspace by resampling the rarely occurring states of a given protein and tends to search an overly broad subspace. Thus, we considered that GERBIL might improve the excessive conformational search of OFLOOD for a physically irrelevant configurational subspace. As a demonstration, OFLOOD and OFLOOD-GERBIL were applied to a globular protein (T4 lysozyme) and their conformational search qualities were assessed. Based on our assessment, normal OFLOOD without the outlier validation frequently sampled low-quality configurations, whereas OFLOOD-GERBIL with the outlier validation intensively sampled high-quality configurations. In conclusion, OFLOOD-GERBIL derives a smart conformational search in a physically relevant configurational subspace, indicating that protein structure validation works in both nonbiased and biased sampling methods.
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Affiliation(s)
- Takunori Yasuda
- College of Biological Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-0821, Japan
| | - Rikuri Morita
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki305-8577, Japan
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7
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Wang YT, Liao JM, Lin WW, Li CC, Huang BC, Cheng TL, Chen TC. Structural insights into Nirmatrelvir (PF-07321332)-3C-like SARS-CoV-2 protease complexation: a ligand Gaussian accelerated molecular dynamics study. Phys Chem Chem Phys 2022; 24:22898-22904. [PMID: 36124909 DOI: 10.1039/d2cp02882d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Coronavirus 3C-like protease (3CLpro) is found in SARS-CoV-2 virus, which causes COVID-19. 3CLpro controls virus replication and is a major target for target-based antiviral discovery. As reported by Pfizer, Nirmatrelvir (PF-07321332) is a competitive protein inhibitor and a clinical candidate for orally delivered medication. However, the binding mechanisms between Nirmatrelvir and 3CLpro complex structures remain unknown. This study incorporated ligand Gaussian accelerated molecular dynamics, the one-dimensional and two-dimensional potential of mean force, normal molecular dynamics, and Kramers' rate theory to determine the binding and dissociation rate constants (koff and kon) associated with the binding of the 3CLpro protein to the Nirmatrelvir inhibitor. The proposed approach addresses the challenges in designing small-molecule antiviral drugs.
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Affiliation(s)
- Yeng-Tseng Wang
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Taiwan. .,Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan
| | - Jun-Min Liao
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Wen-Wei Lin
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Taiwan. .,Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chia-Ching Li
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Bo-Cheng Huang
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Tian-Lu Cheng
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Tun-Chieh Chen
- Department of Internal Medicine, College of Medicine, Kaohsiung Medical University, Taiwan
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8
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Copeland M, Do HN, Votapka L, Joshi K, Wang J, Amaro RE, Miao Y. Gaussian Accelerated Molecular Dynamics in OpenMM. J Phys Chem B 2022; 126:5810-5820. [PMID: 35895977 PMCID: PMC9773147 DOI: 10.1021/acs.jpcb.2c03765] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations. In addition, GaMD simulations captured the folding pathways of three hyperstable RNA tetraloops (UUCG, GCAA, and CUUG) and binding of the rbt203 ligand to the HIV-1 Tar RNA, both of which involved critical electrostatic interactions such as hydrogen bonding and base stacking. Together with previous implementations, GaMD in OpenMM will allow for wider applications in simulations of proteins, RNA, and other biomolecules.
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Affiliation(s)
- Matthew Copeland
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047
| | - Hung N. Do
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047
| | - Lane Votapka
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093
| | - Keya Joshi
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047
| | - Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047
| | - Rommie E. Amaro
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047,To whom correspondence should be addressed:
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9
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Yasuda T, Morita R, Shigeta Y, Harada R. Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins. J Chem Inf Model 2022; 62:3442-3452. [PMID: 35786886 DOI: 10.1021/acs.jcim.2c00573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.
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Affiliation(s)
- Takunori Yasuda
- College of Biological Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-0821, Japan
| | - Rikuri Morita
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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10
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Wang J, Bhattarai A, Do HN, Akhter S, Miao Y. Molecular Simulations and Drug Discovery of Adenosine Receptors. Molecules 2022; 27:2054. [PMID: 35408454 PMCID: PMC9000248 DOI: 10.3390/molecules27072054] [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: 01/31/2022] [Revised: 03/18/2022] [Accepted: 03/20/2022] [Indexed: 02/02/2023] Open
Abstract
G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.
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Affiliation(s)
| | | | | | | | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA; (J.W.); (A.B.); (H.N.D.); (S.A.)
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11
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Pawnikar S, Miao Y. Mechanism of Peptide Agonist Binding in CXCR4 Chemokine Receptor. Front Mol Biosci 2022; 9:821055. [PMID: 35359589 PMCID: PMC8963245 DOI: 10.3389/fmolb.2022.821055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/14/2022] [Indexed: 01/07/2023] Open
Abstract
Chemokine receptors are key G-protein-coupled receptors (GPCRs) that control cell migration in immune system responses, development of cardiovascular and central nervous systems, and numerous diseases. In particular, the CXCR4 chemokine receptor promotes metastasis, tumor growth and angiogenesis in cancers. CXCR4 is also used as one of the two co-receptors for T-tropic HIV-1 entry into host cells. Therefore, CXCR4 serves as an important therapeutic target for treating cancers and HIV infection. Apart from the CXCL12 endogenous peptide agonist, previous studies suggested that the first 17 amino acids of CXCL12 are sufficient to activate CXCR4. Two 17-residue peptides with positions 1-4 mutated to RSVM and ASLW functioned as super and partial agonists of CXCR4, respectively. However, the mechanism of peptide agonist binding in CXCR4 remains unclear. Here, we have investigated this mechanism through all-atom simulations using a novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) method. The Pep-GaMD simulations have allowed us to explore representative binding conformations of each peptide and identify critical low-energy states of CXCR4 activated by the super versus partial peptide agonists. Our simulations have provided important mechanistic insights into peptide agonist binding in CXCR4, which are expected to facilitate rational design of new peptide modulators of CXCR4 and other chemokine receptors.
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12
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Do HN, Wang J, Bhattarai A, Miao Y. GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling. J Chem Theory Comput 2022; 18:1423-1436. [PMID: 35200019 PMCID: PMC9773012 DOI: 10.1021/acs.jctc.1c01055] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A1 receptor (A1AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A1AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.
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Affiliation(s)
- Hung N. Do
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047
| | - Jinan Wang
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047
| | - Apurba Bhattarai
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047
| | - Yinglong Miao
- The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047,Corresponding author:
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13
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Pawnikar S, Bhattarai A, Wang J, Miao Y. Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives. Adv Appl Bioinform Chem 2022; 15:1-19. [PMID: 35023931 PMCID: PMC8747661 DOI: 10.2147/aabc.s247950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/20/2021] [Indexed: 12/12/2022] Open
Abstract
Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.
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Affiliation(s)
- Shristi Pawnikar
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
| | - Apurba Bhattarai
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
| | - Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
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14
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Challenges and frontiers of computational modelling of biomolecular recognition. QRB DISCOVERY 2022. [DOI: 10.1017/qrd.2022.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Abstract
Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.
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15
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Abrol R, Serrano E, Santiago LJ. Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 128:325-359. [PMID: 35034722 DOI: 10.1016/bs.apcsb.2021.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, β arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.
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Affiliation(s)
- Ravinder Abrol
- Department of Chemistry and Biochemistry, California State University, Northridge, CA, United States.
| | - Erik Serrano
- Department of Chemistry and Biochemistry, California State University, Northridge, CA, United States
| | - Luis Jaimes Santiago
- Department of Chemistry and Biochemistry, California State University, Northridge, CA, United States
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16
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Wang J, Arantes PR, Bhattarai A, Hsu RV, Pawnikar S, Huang YMM, Palermo G, Miao Y. Gaussian accelerated molecular dynamics (GaMD): principles and applications. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2021; 11:e1521. [PMID: 34899998 PMCID: PMC8658739 DOI: 10.1002/wcms.1521] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 01/28/2021] [Indexed: 12/20/2022]
Abstract
Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, KS, 66047, United States
| | - Pablo R Arantes
- Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA 92512, United States
| | - Apurba Bhattarai
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr, Lawrence, KS, 66047, United States
| | - Rohaine V Hsu
- Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA 92512, United States
| | - Shristi Pawnikar
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, KS, 66047, United States
| | - Yu-Ming M Huang
- Department of Physics & Astronomy, Wayne State University, 666 W Hancock St, Detroit, MI 48207, USA
| | - Giulia Palermo
- Department of Bioengineering and Department of Chemistry, University of California Riverside, 900 University Avenue, Riverside, CA 92512, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, Kansas 66047, United States
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17
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Zhou B, Wu Y, Su Z. Computational Simulation of Holin S105 in Membrane Bilayer and Its Dimerization Through a Helix-Turn-Helix Motif. J Membr Biol 2021; 254:397-407. [PMID: 34189599 PMCID: PMC10811654 DOI: 10.1007/s00232-021-00187-w] [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: 01/03/2021] [Accepted: 05/15/2021] [Indexed: 11/30/2022]
Abstract
During the final step of the bacteriophage infection cycle, the cytoplasmic membrane of host cells is disrupted by small membrane proteins called holins. The function of holins in cell lysis is carried out by forming a highly ordered structure called lethal lesion, in which the accumulation of holins in the cytoplasmic membrane leads to the sudden opening of a hole in the middle of this oligomer. Previous studies showed that dimerization of holins is a necessary step to induce their higher order assembly. However, the molecular mechanism underlying the holin-mediated lesion formation is not well understood. In order to elucidate the functions of holin, we first computationally constructed a structural model for our testing system: the holin S105 from bacteriophage lambda. All atom molecular dynamic simulations were further applied to refine its structure and study its dynamics as well as interaction in lipid bilayer. Additional simulations on association between two holins provide supportive evidence to the argument that the C-terminal region of holin plays a critical role in regulating the dimerization. In detail, we found that the adhesion of specific nonpolar residues in transmembrane domain 3 (TMD3) in a polar environment serves as the driven force of dimerization. Our study therefore brings insights to the design of binding interfaces between holins, which can be potentially used to modulate the dynamics of lesion formation.
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Affiliation(s)
- Brian Zhou
- Edgemont Jr.\Sr. High School, 200 White Oak Ln, Scarsdale, NY, 10583, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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18
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Hong ST, Su YC, Wang YJ, Cheng TL, Wang YT. Anti-TNF Alpha Antibody Humira with pH-dependent Binding Characteristics: A constant-pH Molecular Dynamics, Gaussian Accelerated Molecular Dynamics, and In Vitro Study. Biomolecules 2021; 11:334. [PMID: 33672169 PMCID: PMC7926962 DOI: 10.3390/biom11020334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/18/2021] [Accepted: 02/20/2021] [Indexed: 12/17/2022] Open
Abstract
Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira-TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing the Humira-TNF alpha complex structures to dissociate to Humira and soluble TNF alpha in the early endosome to enable Humira recycling is crucial. We used the cytoplasmic pH (7.4), the early endosomal pH (6.0), and pKa of histidine side chains (6.0-6.4) to mutate the residues of complementarity-determining regions with histidine. Our engineered Humira (W1-Humira) can bind to TNF alpha in plasma at neutral pH and dissociate from the TNF alpha in the endosome at acidic pH. We used the constant-pH molecular dynamics, Gaussian accelerated molecular dynamics, two-dimensional potential mean force profiles, and in vitro methods to investigate the characteristics of W1-Humira. Our results revealed that the proposed Humira can bind TNF alpha with pH-dependent affinity in vitro. The W1-Humira was weaker than wild-type Humira at neutral pH in vitro, and our prediction results were close to the in vitro results. Furthermore, our approach displayed a high accuracy in antibody pH-dependent binding characteristics prediction, which may facilitate antibody drug design. Advancements in computational methods and computing power may further aid in addressing the challenges in antibody drug design.
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Affiliation(s)
- Shih-Ting Hong
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
| | - Yu-Cheng Su
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan;
| | - Yu-Jen Wang
- Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan;
| | - Tian-Lu Cheng
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yeng-Tseng Wang
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
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19
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Wang E, Weng G, Sun H, Du H, Zhu F, Chen F, Wang Z, Hou T. Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions. Phys Chem Chem Phys 2019; 21:18958-18969. [PMID: 31453590 DOI: 10.1039/c9cp04096j] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (rp = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (rp = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.
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Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Gaoqi Weng
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Hongyan Du
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Feng Zhu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Fu Chen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China. and State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
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20
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Samanta PN, Kar S, Leszczynski J. Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors. Curr Pharm Des 2019; 25:750-773. [DOI: 10.2174/1381612825666190304123545] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 02/26/2019] [Indexed: 11/22/2022]
Abstract
The rapid advancement of computer architectures and development of mathematical algorithms offer a
unique opportunity to leverage the simulation of macromolecular systems at physiologically relevant timescales.
Herein, we discuss the impact of diverse structure-based and ligand-based molecular modeling techniques in
designing potent and selective antagonists against each adenosine receptor (AR) subtype that constitutes multitude
of drug targets. The efficiency and robustness of high-throughput empirical scoring function-based approaches
for hit discovery and lead optimization in the AR family are assessed with the help of illustrative examples
that have led to nanomolar to sub-micromolar inhibition activities. Recent progress in computer-aided drug
discovery through homology modeling, quantitative structure-activity relation, pharmacophore models, and molecular
docking coupled with more accurate free energy calculation methods are reported and critically analyzed
within the framework of structure-based virtual screening of AR antagonists. Later, the potency and applicability
of integrated molecular dynamics (MD) methods are addressed in the context of diligent inspection of intricated
AR-antagonist binding processes. MD simulations are exposed to be competent for studying the role of the membrane
as well as the receptor flexibility toward the precise evaluation of the biological activities of antagonistbound
AR complexes such as ligand binding modes, inhibition affinity, and associated thermodynamic and kinetic
parameters.
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Affiliation(s)
- Pabitra Narayan Samanta
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, United States
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, United States
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, United States
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21
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Mostofian B, Zuckerman DM. Statistical Uncertainty Analysis for Small-Sample, High Log-Variance Data: Cautions for Bootstrapping and Bayesian Bootstrapping. J Chem Theory Comput 2019; 15:3499-3509. [PMID: 31002504 PMCID: PMC6754704 DOI: 10.1021/acs.jctc.9b00015] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Recent advances in molecular simulations allow the evaluation of previously unattainable observables, such as rate constants for protein folding. However, these calculations are usually computationally expensive, and even significant computing resources may result in a small number of independent estimates spread over many orders of magnitude. Such small-sample, high "log-variance" data are not readily amenable to analysis using the standard uncertainty (i.e., "standard error of the mean") because unphysical negative limits of confidence intervals result. Bootstrapping, a natural alternative guaranteed to yield a confidence interval within the minimum and maximum values, also exhibits a striking systematic bias of the lower confidence limit in log space. As we show, bootstrapping artifactually assigns high probability to improbably low mean values. A second alternative, the Bayesian bootstrap strategy, does not suffer from the same deficit and is more logically consistent with the type of confidence interval desired. The Bayesian bootstrap provides uncertainty intervals that are more reliable than those from the standard bootstrap method but must be used with caution nevertheless. Neither standard nor Bayesian bootstrapping can overcome the intrinsic challenge of underestimating the mean from small-size, high log-variance samples. Our conclusions are based on extensive analysis of model distributions and reanalysis of multiple independent atomistic simulations. Although we only analyze rate constants, similar considerations will apply to related calculations, potentially including highly nonlinear averages like the Jarzynski relation.
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Affiliation(s)
- Barmak Mostofian
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, Oregon
| | - Daniel M. Zuckerman
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, Oregon
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22
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An X, Bai Q, Bing Z, Zhou S, Shi D, Liu H, Yao X. How Does Agonist and Antagonist Binding Lead to Different Conformational Ensemble Equilibria of the κ-Opioid Receptor: Insight from Long-Time Gaussian Accelerated Molecular Dynamics Simulation. ACS Chem Neurosci 2019; 10:1575-1584. [PMID: 30372027 DOI: 10.1021/acschemneuro.8b00535] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The opioid receptors belong to the class A seven transmembrane-spanning (7TM) G protein-coupled receptors (GPCRs). The κ-opioid receptor (KOR) is a subfamily of four opioid receptors. The endogenous peptide and a variety of selective agonists and antagonists of KOR have been developed. The structurally similar ligands at the same site cause completely opposite biological functions and induce different conformational changes. To shed light on the conformation ensembles and conformational dynamics in activation and deactivation processes of KOR, we performed all-atom, long-time Gaussian accelerated molecular dynamics simulation (GaMD) on KOR binding with agonist epoxymorphinan MP1104 and antagonist JDTic, respectively. Our results revealed different conformation ensembles of KOR binding with agonist and with antagonist. Agonist binding stabilizes the active state of key motifs including DYYNM motif and CWxP motif, and biases the conformation equilibria toward the active state. Antagonist binding will not destroy inactive conformation equilibria, by keeping the stable inactive state of these crucial motifs. We found that the inactive apo form of KOR is the most stable state, while the active apo form relaxes readily to inactive state. Our results also revealed a stable intermediate (I), which is attributed to the hydrophobic interactions between Tyr2465.58 and TM6, as well as the steric hindrance of them. Our results not only show the conformation equilibria bias of KOR by binding with agonist and antagonist, but also provide the structural information for the design and discovery of potential ligands with different functions.
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Affiliation(s)
- Xiaoli An
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou 730000, China
| | - Qifeng Bai
- School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China
| | - Zhitong Bing
- School of Basic Medical Science, Lanzhou University, Lanzhou 730000, China
- Institute of Modern Physics of Chinese Academy of Sciences, Gansu Province, Lanzhou 730000, China
| | - Shuangyan Zhou
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou 730000, China
- School of Pharmacy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Danfeng Shi
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou 730000, China
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou 730000, P. R. China
| | - Xiaojun Yao
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou University, Lanzhou 730000, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, Macau China
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Angladon MA, Fossépré M, Leherte L, Vercauteren DP. Interaction of POPC, DPPC, and POPE with the μ opioid receptor: A coarse-grained molecular dynamics study. PLoS One 2019; 14:e0213646. [PMID: 30870466 PMCID: PMC6417715 DOI: 10.1371/journal.pone.0213646] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 02/26/2019] [Indexed: 11/18/2022] Open
Abstract
The μ opioid receptor (μOR), which is part of the G protein-coupled receptors family, is a membrane protein that is modulated by its lipid environment. In the present work, we model μOR in three different membrane systems: POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine), POPE (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine), and DPPC (1, 2-dipalmitoyl-sn-glycero-3-phosphocholine) through 45 μs molecular dynamics (MD) simulations at the coarse-grained level. Our theoretical studies provide new insights to the lipid-induced modulation of the receptor. Particularly, to characterize how μOR interacts with each lipid, we analyze the tilt of the protein, the number of contacts occurring between the lipids and each amino acid of the receptor, and the μOR-lipid interface described as a network graph. We also analyze the variations in the number and the nature of the protein contacts that are induced by the lipid structure. We show that POPC interacts preferentially with helix 1 (H1) and helices H5-H6, POPE, with H5-H6 and H6-H7, and DPPC, with H4 and H6. We demonstrate how each of the three lipids shape the structure of the μOR.
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Affiliation(s)
- Marie-Ange Angladon
- Laboratoire de Physico-Chimie Informatique, Unité de Chimie Physique Théorique et Structurale, Namur Medecine and Drug Innovation Center (NAMEDIC), Namur Research Institute for Life Sciences (NARILIS), University of Namur (UNamur), Namur, Belgium
- * E-mail:
| | - Mathieu Fossépré
- Laboratoire de Physico-Chimie Informatique, Unité de Chimie Physique Théorique et Structurale, Namur Medecine and Drug Innovation Center (NAMEDIC), Namur Research Institute for Life Sciences (NARILIS), University of Namur (UNamur), Namur, Belgium
| | - Laurence Leherte
- Laboratoire de Physico-Chimie Informatique, Unité de Chimie Physique Théorique et Structurale, Namur Medecine and Drug Innovation Center (NAMEDIC), Namur Research Institute for Life Sciences (NARILIS), University of Namur (UNamur), Namur, Belgium
| | - Daniel P. Vercauteren
- Laboratoire de Physico-Chimie Informatique, Unité de Chimie Physique Théorique et Structurale, Namur Medecine and Drug Innovation Center (NAMEDIC), Namur Research Institute for Life Sciences (NARILIS), University of Namur (UNamur), Namur, Belgium
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24
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Wang J, Miao Y. Recent advances in computational studies of GPCR-G protein interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2019; 116:397-419. [PMID: 31036298 PMCID: PMC6986689 DOI: 10.1016/bs.apcsb.2018.11.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Protein-protein interactions are key in cellular signaling. G protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are able to transduce extracellular signals (e.g., hormones and neurotransmitters) to intracellular proteins, in particular the G proteins. Since GPCRs serve as primary targets of ~1/3 of currently marketed drugs, it is important to understand mechanisms of GPCR signaling in order to design selective and potent drug molecules. This chapter focuses on recent advances in computational studies of the GPCR-G protein interactions using bioinformatics, protein-protein docking and molecular dynamics simulation approaches.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States.
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25
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A molecular dynamics simulation study decodes the Zika virus NS5 methyltransferase bound to SAH and RNA analogue. Sci Rep 2018; 8:6336. [PMID: 29679079 PMCID: PMC5910437 DOI: 10.1038/s41598-018-24775-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 04/05/2018] [Indexed: 12/16/2022] Open
Abstract
Since 2015, widespread Zika virus outbreaks in Central and South America have caused increases in microcephaly cases, and this acute problem requires urgent attention. We employed molecular dynamics and Gaussian accelerated molecular dynamics techniques to investigate the structure of Zika NS5 protein with S-adenosyl-L-homocysteine (SAH) and an RNA analogue, namely 7-methylguanosine 5'-triphosphate (m7GTP). For the binding motif of Zika virus NS5 protein and SAH, we suggest that the four Zika NS5 substructures (residue orders: 101-112, 54-86, 127-136 and 146-161) and the residues (Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148) might be responsible for the selectivity of the new Zika virus drugs. For the binding motif of Zika NS5 protein and m7GTP, we suggest that the three Zika NS5 substructures (residue orders: 11-31, 146-161 and 207-218) and the residues (Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215) might be responsible for the selectivity of the new Zika virus drugs.
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26
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Huang YMM, McCammon JA, Miao Y. Replica Exchange Gaussian Accelerated Molecular Dynamics: Improved Enhanced Sampling and Free Energy Calculation. J Chem Theory Comput 2018; 14:1853-1864. [PMID: 29489349 PMCID: PMC6747702 DOI: 10.1021/acs.jctc.7b01226] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Through adding a harmonic boost potential to smooth the system potential energy surface, Gaussian accelerated molecular dynamics (GaMD) provides enhanced sampling and free energy calculation of biomolecules without the need of predefined reaction coordinates. This work continues to improve the acceleration power and energy reweighting of the GaMD by combining the GaMD with replica exchange algorithms. Two versions of replica exchange GaMD (rex-GaMD) are presented: force constant rex-GaMD and threshold energy rex-GaMD. During simulations of force constant rex-GaMD, the boost potential can be exchanged between replicas of different harmonic force constants with fixed threshold energy. However, the algorithm of threshold energy rex-GaMD tends to switch the threshold energy between lower and upper bounds for generating different levels of boost potential. Testing simulations on three model systems, including the alanine dipeptide, chignolin, and HIV protease, demonstrate that through continuous exchanges of the boost potential, the rex-GaMD simulations not only enhance the conformational transitions of the systems but also narrow down the distribution width of the applied boost potential for accurate energetic reweighting to recover biomolecular free energy profiles.
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Affiliation(s)
- Yu-ming M. Huang
- Department of Pharmacology and University of California, San Diego, La Jolla, CA 92093
| | - J. Andrew McCammon
- Department of Pharmacology and University of California, San Diego, La Jolla, CA 92093
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093
| | - Yinglong Miao
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093
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Mechanism of the G-protein mimetic nanobody binding to a muscarinic G-protein-coupled receptor. Proc Natl Acad Sci U S A 2018; 115:3036-3041. [PMID: 29507218 DOI: 10.1073/pnas.1800756115] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Protein-protein binding is key in cellular signaling processes. Molecular dynamics (MD) simulations of protein-protein binding, however, are challenging due to limited timescales. In particular, binding of the medically important G-protein-coupled receptors (GPCRs) with intracellular signaling proteins has not been simulated with MD to date. Here, we report a successful simulation of the binding of a G-protein mimetic nanobody to the M2 muscarinic GPCR using the robust Gaussian accelerated MD (GaMD) method. Through long-timescale GaMD simulations over 4,500 ns, the nanobody was observed to bind the receptor intracellular G-protein-coupling site, with a minimum rmsd of 2.48 Å in the nanobody core domain compared with the X-ray structure. Binding of the nanobody allosterically closed the orthosteric ligand-binding pocket, being consistent with the recent experimental finding. In the absence of nanobody binding, the receptor orthosteric pocket sampled open and fully open conformations. The GaMD simulations revealed two low-energy intermediate states during nanobody binding to the M2 receptor. The flexible receptor intracellular loops contribute remarkable electrostatic, polar, and hydrophobic residue interactions in recognition and binding of the nanobody. These simulations provided important insights into the mechanism of GPCR-nanobody binding and demonstrated the applicability of GaMD in modeling dynamic protein-protein interactions.
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Liao JM, Wang YT. In silico studies of conformational dynamics of Mu opioid receptor performed using gaussian accelerated molecular dynamics. J Biomol Struct Dyn 2018; 37:166-177. [DOI: 10.1080/07391102.2017.1422025] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Jun-Min Liao
- Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yeng-Tseng Wang
- Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Biochemistry, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
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Abstract
Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.
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Affiliation(s)
- Naushad Velgy
- Department of Biochemistry, Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - George Hedger
- Department of Biochemistry, Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Philip C Biggin
- Department of Biochemistry, Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
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Miao Y, McCammon JA. Gaussian Accelerated Molecular Dynamics: Theory, Implementation, and Applications. ANNUAL REPORTS IN COMPUTATIONAL CHEMISTRY 2017; 13:231-278. [PMID: 29720925 PMCID: PMC5927394 DOI: 10.1016/bs.arcc.2017.06.005] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A novel Gaussian Accelerated Molecular Dynamics (GaMD) method has been developed for simultaneous unconstrained enhanced sampling and free energy calculation of biomolecules. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of the biomolecules. Furthermore, by constructing a boost potential that follows a Gaussian distribution, accurate reweighting of GaMD simulations is achieved via cumulant expansion to the second order. The free energy profiles obtained from GaMD simulations allow us to identify distinct low energy states of the biomolecules and characterize biomolecular structural dynamics quantitatively. In this chapter, we present the theory of GaMD, its implementation in the widely used molecular dynamics software packages (AMBER and NAMD), and applications to the alanine dipeptide biomolecular model system, protein folding, biomolecular large-scale conformational transitions and biomolecular recognition.
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Affiliation(s)
- Yinglong Miao
- Howard Hughes Medical Institute, University of California at San Diego, La Jolla, CA 92093
- Department of Pharmacology, University of California at San Diego, La Jolla, CA 92093
| | - J Andrew McCammon
- Howard Hughes Medical Institute, University of California at San Diego, La Jolla, CA 92093
- Department of Pharmacology, University of California at San Diego, La Jolla, CA 92093
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093
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31
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Wang YT, Chan YH. Understanding the molecular basis of agonist/antagonist mechanism of human mu opioid receptor through gaussian accelerated molecular dynamics method. Sci Rep 2017; 7:7828. [PMID: 28798303 PMCID: PMC5552784 DOI: 10.1038/s41598-017-08224-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 07/10/2017] [Indexed: 01/01/2023] Open
Abstract
The most powerful analgesic and addictive properties of opiate alkaloids are mediated by the μ opioid receptor (MOR). The MOR has been extensively investigated as a drug target in the twentieth century, with numerous compounds of varying efficacy being identified. We employed molecular dynamics and Gaussian accelerated molecular dynamics techniques to identify the binding mechanisms of MORs to BU72 (agonist) and β-funaltrexamine (antagonist). Our approach theoretically suggests that the 34 residues (Lys209–Phe221 and Ile301–Cys321) of the MORs were the key regions enabling the two compounds to bind to the active site of the MORs. When the MORs were in the holo form, the key region was in the open conformation. When the MORs were in the apo form, the key region was in the closed conformation. The key region might be responsible for the selectivity of new MOR agonists and antagonists.
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Affiliation(s)
- Yeng-Tseng Wang
- Department of Biochemistry, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. .,Center for Biomarkers and Biotech Drugs, Kaohsiung Medical University, Kaohsiung, Taiwan. .,Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. .,Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
| | - Yang-Hsiang Chan
- Department of Chemistry, National Sun Yat-sen University, 70 Lien Hai Road, Kaohsiung, Taiwan
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Bartuzi D, Kaczor AA, Targowska-Duda KM, Matosiuk D. Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors. Molecules 2017; 22:molecules22020340. [PMID: 28241450 PMCID: PMC6155844 DOI: 10.3390/molecules22020340] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 01/27/2017] [Accepted: 02/15/2017] [Indexed: 12/16/2022] Open
Abstract
The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.
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Affiliation(s)
- Damian Bartuzi
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
- School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211 Kuopio, Finland.
| | | | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
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Periole X. Interplay of G Protein-Coupled Receptors with the Membrane: Insights from Supra-Atomic Coarse Grain Molecular Dynamics Simulations. Chem Rev 2016; 117:156-185. [PMID: 28073248 DOI: 10.1021/acs.chemrev.6b00344] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
G protein-coupled receptors (GPCRs) are central to many fundamental cellular signaling pathways. They transduce signals from the outside to the inside of cells in physiological processes ranging from vision to immune response. It is extremely challenging to look at them individually using conventional experimental techniques. Recently, a pseudo atomistic molecular model has emerged as a valuable tool to access information on GPCRs, more specifically on their interactions with their environment in their native cell membrane and the consequences on their supramolecular organization. This approach uses the Martini coarse grain (CG) model to describe the receptors, lipids, and solvent in molecular dynamics (MD) simulations and in enough detail to allow conserving the chemical specificity of the different molecules. The elimination of unnecessary degrees of freedom has opened up large-scale simulations of the lipid-mediated supramolecular organization of GPCRs. Here, after introducing the Martini CGMD method, we review these studies carried out on various members of the GPCR family, including rhodopsin (visual receptor), opioid receptors, adrenergic receptors, adenosine receptors, dopamine receptor, and sphingosine 1-phosphate receptor. These studies have brought to light an interesting set of novel biophysical principles. The insights range from revealing localized and heterogeneous deformations of the membrane bilayer at the surface of the protein, specific interactions of lipid molecules with individual GPCRs, to the effect of the membrane matrix on global GPCR self-assembly. The review ends with an overview of the lessons learned from the use of the CGMD method, the biophysical-chemical findings on lipid-protein interplay.
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Affiliation(s)
- Xavier Periole
- Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen , Nijenborgh 7, 9747AG Groningen, The Netherlands
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34
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McRobb FM, Negri A, Beuming T, Sherman W. Molecular dynamics techniques for modeling G protein-coupled receptors. Curr Opin Pharmacol 2016; 30:69-75. [DOI: 10.1016/j.coph.2016.07.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 06/28/2016] [Accepted: 07/03/2016] [Indexed: 11/17/2022]
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Arnatt CK, Falls BA, Yuan Y, Raborg TJ, Masvekar RR, El-Hage N, Selley DE, Nicola AV, Knapp PE, Hauser KF, Zhang Y. Exploration of bivalent ligands targeting putative mu opioid receptor and chemokine receptor CCR5 dimerization. Bioorg Med Chem 2016; 24:5969-5987. [PMID: 27720326 DOI: 10.1016/j.bmc.2016.09.059] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Revised: 09/20/2016] [Accepted: 09/23/2016] [Indexed: 12/19/2022]
Abstract
Modern antiretroviral therapies have provided HIV-1 infected patients longer lifespans and better quality of life. However, several neurological complications are now being seen in these patients due to HIV-1 associated injury of neurons by infected microglia and astrocytes. In addition, these effects can be further exacerbated with opiate use and abuse. One possible mechanism for such potentiation effects of opiates is the interaction of the mu opioid receptor (MOR) with the chemokine receptor CCR5 (CCR5), a known HIV-1 co-receptor, to form MOR-CCR5 heterodimer. In an attempt to understand this putative interaction and its relevance to neuroAIDS, we designed and synthesized a series of bivalent ligands targeting the putative CCR5-MOR heterodimer. To understand how these bivalent ligands may interact with the heterodimer, biological studies including calcium mobilization inhibition, binding affinity, HIV-1 invasion, and cell fusion assays were applied. In particular, HIV-1 infection assays using human peripheral blood mononuclear cells, macrophages, and astrocytes revealed a notable synergy in activity for one particular bivalent ligand. Further, a molecular model of the putative CCR5-MOR heterodimer was constructed, docked with the bivalent ligand, and molecular dynamics simulations of the complex was performed in a membrane-water system to help understand the biological observation.
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Affiliation(s)
- Christopher K Arnatt
- Department of Medicinal Chemistry, Virginia Commonwealth University, 800 East Leigh Street, Richmond, VA 23298, USA
| | - Bethany A Falls
- Department of Medicinal Chemistry, Virginia Commonwealth University, 800 East Leigh Street, Richmond, VA 23298, USA
| | - Yunyun Yuan
- Department of Medicinal Chemistry, Virginia Commonwealth University, 800 East Leigh Street, Richmond, VA 23298, USA
| | - Thomas J Raborg
- Department of Medicinal Chemistry, Virginia Commonwealth University, 800 East Leigh Street, Richmond, VA 23298, USA
| | - Ruturaj R Masvekar
- Department of Anatomy & Neurobiology, Virginia Commonwealth University, 1217 East Marshall Street, Richmond, VA 23298, USA
| | - Nazira El-Hage
- Department of Pharmacology and Toxicology, Virginia Commonwealth University, 410 North 12th Street, Richmond, VA 23298, USA
| | - Dana E Selley
- Department of Pharmacology and Toxicology, Virginia Commonwealth University, 410 North 12th Street, Richmond, VA 23298, USA
| | - Anthony V Nicola
- Veterinary Microbiology and Pathology, Washington State University, Pullman, WA 99164, USA
| | - Pamela E Knapp
- Department of Pharmacology and Toxicology, Virginia Commonwealth University, 410 North 12th Street, Richmond, VA 23298, USA; Department of Anatomy & Neurobiology, Virginia Commonwealth University, 1217 East Marshall Street, Richmond, VA 23298, USA
| | - Kurt F Hauser
- Department of Pharmacology and Toxicology, Virginia Commonwealth University, 410 North 12th Street, Richmond, VA 23298, USA; Department of Anatomy & Neurobiology, Virginia Commonwealth University, 1217 East Marshall Street, Richmond, VA 23298, USA
| | - Yan Zhang
- Department of Medicinal Chemistry, Virginia Commonwealth University, 800 East Leigh Street, Richmond, VA 23298, USA.
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Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor. Proc Natl Acad Sci U S A 2016; 113:E5675-84. [PMID: 27601651 DOI: 10.1073/pnas.1612353113] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Design of ligands that provide receptor selectivity has emerged as a new paradigm for drug discovery of G protein-coupled receptors, and may, for certain families of receptors, only be achieved via identification of chemically diverse allosteric modulators. Here, the extracellular vestibule of the M2 muscarinic acetylcholine receptor (mAChR) is targeted for structure-based design of allosteric modulators. Accelerated molecular dynamics (aMD) simulations were performed to construct structural ensembles that account for the receptor flexibility. Compounds obtained from the National Cancer Institute (NCI) were docked to the receptor ensembles. Retrospective docking of known ligands showed that combining aMD simulations with Glide induced fit docking (IFD) provided much-improved enrichment factors, compared with the Glide virtual screening workflow. Glide IFD was thus applied in receptor ensemble docking, and 38 top-ranked NCI compounds were selected for experimental testing. In [(3)H]N-methylscopolamine radioligand dissociation assays, approximately half of the 38 lead compounds altered the radioligand dissociation rate, a hallmark of allosteric behavior. In further competition binding experiments, we identified 12 compounds with affinity of ≤30 μM. With final functional experiments on six selected compounds, we confirmed four of them as new negative allosteric modulators (NAMs) and one as positive allosteric modulator of agonist-mediated response at the M2 mAChR. Two of the NAMs showed subtype selectivity without significant effect at the M1 and M3 mAChRs. This study demonstrates an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies.
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37
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Multiscale design of coarse-grained elastic network-based potentials for the μ opioid receptor. J Mol Model 2016; 22:227. [DOI: 10.1007/s00894-016-3092-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 08/08/2016] [Indexed: 01/10/2023]
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38
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Kalli AC, Rog T, Vattulainen I, Campbell ID, Sansom MSP. The Integrin Receptor in Biologically Relevant Bilayers: Insights from Molecular Dynamics Simulations. J Membr Biol 2016; 250:337-351. [PMID: 27465729 PMCID: PMC5579164 DOI: 10.1007/s00232-016-9908-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/25/2016] [Indexed: 11/27/2022]
Abstract
Integrins are heterodimeric (αβ) cell surface receptors that are potential therapeutic targets for a number of diseases. Despite the existence of structural data for all parts of integrins, the structure of the complete integrin receptor is still not available. We have used available structural data to construct a model of the complete integrin receptor in complex with talin F2-F3 domain. It has been shown that the interactions of integrins with their lipid environment are crucial for their function but details of the integrin/lipid interactions remain elusive. In this study an integrin/talin complex was inserted in biologically relevant bilayers that resemble the cell plasma membrane containing zwitterionic and charged phospholipids, cholesterol and sphingolipids to study the dynamics of the integrin receptor and its effect on bilayer structure and dynamics. The results of this study demonstrate the dynamic nature of the integrin receptor and suggest that the presence of the integrin receptor alters the lipid organization between the two leaflets of the bilayer. In particular, our results suggest elevated density of cholesterol and of phosphatidylserine lipids around the integrin/talin complex and a slowing down of lipids in an annulus of ~30 Å around the protein due to interactions between the lipids and the integrin/talin F2-F3 complex. This may in part regulate the interactions of integrins with other related proteins or integrin clustering thus facilitating signal transduction across cell membranes.
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Affiliation(s)
- Antreas C Kalli
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Tomasz Rog
- Department of Physics, Tampere University of Technology, P.O. Box 692, 33101, Tampere, Finland
| | - Ilpo Vattulainen
- Department of Physics, Tampere University of Technology, P.O. Box 692, 33101, Tampere, Finland
- MEMPHYS - Center for Biomembrane Physics, University of Southern Denmark, 5230, Odense M, Denmark
| | - Iain D Campbell
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Mark S P Sansom
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
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Miao Y, McCammon JA. Unconstrained Enhanced Sampling for Free Energy Calculations of Biomolecules: A Review. MOLECULAR SIMULATION 2016; 42:1046-1055. [PMID: 27453631 PMCID: PMC4955644 DOI: 10.1080/08927022.2015.1121541] [Citation(s) in RCA: 111] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Free energy calculations are central to understanding the structure, dynamics and function of biomolecules. Yet insufficient sampling of biomolecular configurations is often regarded as one of the main sources of error. Many enhanced sampling techniques have been developed to address this issue. Notably, enhanced sampling methods based on biasing collective variables (CVs), including the widely used umbrella sampling, adaptive biasing force and metadynamics, have been discussed in a recent excellent review (Abrams and Bussi, Entropy, 2014). Here, we aim to review enhanced sampling methods that do not require predefined system-dependent CVs for biomolecular simulations and as such do not suffer from the hidden energy barrier problem as encountered in the CV-biasing methods. These methods include, but are not limited to, replica exchange/parallel tempering, self-guided molecular/Langevin dynamics, essential energy space random walk and accelerated molecular dynamics. While it is overwhelming to describe all details of each method, we provide a summary of the methods along with the applications and offer our perspectives. We conclude with challenges and prospects of the unconstrained enhanced sampling methods for accurate biomolecular free energy calculations.
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Affiliation(s)
- Yinglong Miao
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92093
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92093
| | - J. Andrew McCammon
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92093
- Department of Pharmacology, University of California San Diego, La Jolla, CA 92093
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093
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40
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G-protein coupled receptors: advances in simulation and drug discovery. Curr Opin Struct Biol 2016; 41:83-89. [PMID: 27344006 DOI: 10.1016/j.sbi.2016.06.008] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2016] [Accepted: 06/07/2016] [Indexed: 11/21/2022]
Abstract
G-protein coupled receptors (GPCRs), the largest family of human membrane proteins, mediate cellular signaling and represent primary targets of about one third of currently marketed drugs. GPCRs undergo highly dynamic structural transitions during signal transduction, from binding of extracellular ligands to coupling with intracellular effector proteins. Molecular dynamics (MD) simulations have been utilized to investigate GPCR signaling mechanisms (such as pathways of ligand binding and receptor activation/deactivation) and to design novel small-molecule drug candidates. Future research directions point towards modeling cooperative binding of multiple orthosteric and allosteric ligands to GPCRs, GPCR oligomerization and interactions of GPCRs with different intracellular signaling proteins. Through methodological and supercomputing advances, MD simulations will continue to provide important insights into GPCR signaling mechanisms and further facilitate structure-based drug design.
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41
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Böckmann M, Doltsinis NL, Marx D. Adaptive switching of interaction potentials in the time domain: an extended Lagrangian approach tailored to transmute force field to QM/MM simulations and back. J Chem Theory Comput 2016; 11:2429-39. [PMID: 26575543 DOI: 10.1021/acs.jctc.5b00142] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
An extended Lagrangian formalism that allows for a smooth transition between two different descriptions of interactions during a molecular dynamics simulation is presented. This time-adaptive method is particularly useful in the context of multiscale simulation as it provides a sound recipe to switch on demand between different hierarchical levels of theory, for instance between ab initio ("QM") and force field ("MM") descriptions of a given (sub)system in the course of a molecular dynamics simulation. The equations of motion can be integrated straightforwardly using the usual propagators, such as the Verlet algorithm. First test cases include a bath of harmonic oscillators, of which a subset is switched to a different force constant and/or equilibrium position, as well as an all-MM to QM/MM transition in a hydrogen-bonded water dimer. The method is then applied to a smectic 8AB8 liquid crystal and is shown to be able to switch dynamically a preselected 8AB8 molecule from an all-MM to a QM/MM description which involves partition boundaries through covalent bonds. These examples show that the extended Lagrangian approach is not only easy to implement into existing code but that it is also efficient and robust. The technique moreover provides easy access to a conserved energy quantity, also in cases when Nosé-Hoover chain thermostatting is used throughout dynamical switching. A simple quadratic driving potential proves to be sufficient to guarantee a smooth transition whose time scale can be easily tuned by varying the fictitious mass parameter associated with the auxiliary variable used to extend the Lagrangian. The method is general and can be applied to time-adaptive switching on demand between two different levels of theory within the framework of hybrid scale-bridging simulations.
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Affiliation(s)
- Marcus Böckmann
- Institut für Festkörpertheorie and Center for Multiscale Theory & Computation, Westfälische Wilhelms-Universität Münster , Wilhelm-Klemm-Str. 10, 48149 Münster, Germany
| | - Nikos L Doltsinis
- Institut für Festkörpertheorie and Center for Multiscale Theory & Computation, Westfälische Wilhelms-Universität Münster , Wilhelm-Klemm-Str. 10, 48149 Münster, Germany
| | - Dominik Marx
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum , 44780 Bochum, Germany
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Sengupta D, Joshi M, Athale CA, Chattopadhyay A. What can simulations tell us about GPCRs. Methods Cell Biol 2016; 132:429-52. [DOI: 10.1016/bs.mcb.2015.11.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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43
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44
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Nishizawa M, Nishizawa K. Free energy of helical transmembrane peptide dimerization in OPLS-AA/Berger force field simulations: inaccuracy and implications for partner-specific Lennard-Jones parameters between peptides and lipids. MOLECULAR SIMULATION 2015. [DOI: 10.1080/08927022.2015.1112006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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45
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Nishizawa M, Nishizawa K. Potential of mean force analysis of the self-association of leucine-rich transmembrane α-helices: difference between atomistic and coarse-grained simulations. J Chem Phys 2015; 141:075101. [PMID: 25149815 DOI: 10.1063/1.4891932] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Interaction of transmembrane (TM) proteins is important in many biological processes. Large-scale computational studies using coarse-grained (CG) simulations are becoming popular. However, most CG model parameters have not fully been calibrated with respect to lateral interactions of TM peptide segments. Here, we compare the potential of mean forces (PMFs) of dimerization of TM helices obtained using a MARTINI CG model and an atomistic (AT) Berger lipids-OPLS/AA model (AT(OPLS)). For helical, tryptophan-flanked, leucine-rich peptides (WL15 and WALP15) embedded in a parallel configuration in an octane slab, the AT(OPLS) PMF profiles showed a shallow minimum (with a depth of approximately 3 kJ/mol; i.e., a weak tendency to dimerize). A similar analysis using the CHARMM36 all-atom model (AT(CHARMM)) showed comparable results. In contrast, the CG analysis generally showed steep PMF curves with depths of approximately 16-22 kJ/mol, suggesting a stronger tendency to dimerize compared to the AT model. This CG > AT discrepancy in the propensity for dimerization was also seen for dilauroylphosphatidylcholine (DLPC)-embedded peptides. For a WL15 (and WALP15)/DLPC bilayer system, AT(OPLS) PMF showed a repulsive mean force for a wide range of interhelical distances, in contrast to the attractive forces observed in the octane system. The change from the octane slab to the DLPC bilayer also mitigated the dimerization propensity in the CG system. The dimerization energies of CG (AALALAA)3 peptides in DLPC and dioleoylphosphatidylcholine bilayers were in good agreement with previous experimental data. The lipid headgroup, but not the length of the lipid tails, was a key causative factor contributing to the differences between octane and DLPC. Furthermore, the CG model, but not the AT model, showed high sensitivity to changes in amino acid residues located near the lipid-water interface and hydrophobic mismatch between the peptides and membrane. These findings may help interpret CG and AT simulation results on membrane proteins.
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Affiliation(s)
- Manami Nishizawa
- Teikyo University School of Medical Technology, Itabashi, Tokyo, Japan
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46
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Rossetti G, Dibenedetto D, Calandrini V, Giorgetti A, Carloni P. Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins. Arch Biochem Biophys 2015; 582:91-100. [DOI: 10.1016/j.abb.2015.03.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Revised: 03/11/2015] [Accepted: 03/12/2015] [Indexed: 01/05/2023]
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47
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Miao Y, Feher VA, McCammon JA. Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation. J Chem Theory Comput 2015; 11:3584-3595. [PMID: 26300708 PMCID: PMC4535365 DOI: 10.1021/acs.jctc.5b00436] [Citation(s) in RCA: 474] [Impact Index Per Article: 52.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Indexed: 12/20/2022]
Abstract
A Gaussian accelerated molecular dynamics (GaMD) approach for simultaneous enhanced sampling and free energy calculation of biomolecules is presented. By constructing a boost potential that follows Gaussian distribution, accurate reweighting of the GaMD simulations is achieved using cumulant expansion to the second order. Here, GaMD is demonstrated on three biomolecular model systems: alanine dipeptide, chignolin folding, and ligand binding to the T4-lysozyme. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of these biomolecules. Furthermore, the free energy profiles obtained from reweighting of the GaMD simulations allow us to identify distinct low-energy states of the biomolecules and characterize the protein-folding and ligand-binding pathways quantitatively.
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Affiliation(s)
- Yinglong Miao
- Howard Hughes Medical Institute, Department of Chemistry and Biochemistry, and Department of Pharmacology, University of California at San Diego , La Jolla, California 92093, United States
| | - Victoria A Feher
- Howard Hughes Medical Institute, Department of Chemistry and Biochemistry, and Department of Pharmacology, University of California at San Diego , La Jolla, California 92093, United States
| | - J Andrew McCammon
- Howard Hughes Medical Institute, Department of Chemistry and Biochemistry, and Department of Pharmacology, University of California at San Diego , La Jolla, California 92093, United States
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48
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Jatana N, Thukral L, Latha N. Structure and dynamics of DRD4 bound to an agonist and an antagonist using in silico
approaches. Proteins 2015; 83:867-80. [DOI: 10.1002/prot.24716] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2014] [Revised: 09/15/2014] [Accepted: 09/27/2014] [Indexed: 01/11/2023]
Affiliation(s)
- Nidhi Jatana
- Bioinformatics Infrastructure Facility; Sri Venkateswara College (University of Delhi); Benito Juarez Road Dhaula Kuan New Delhi 110 021 India
| | - Lipi Thukral
- CSIR-Institute of Genomics and Integrative Biology; Mall Road New Delhi 110 007 India
| | - N. Latha
- Bioinformatics Infrastructure Facility; Sri Venkateswara College (University of Delhi); Benito Juarez Road Dhaula Kuan New Delhi 110 021 India
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49
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Xiao X, Zeng X, Yuan Y, Gao N, Guo Y, Pu X, Li M. Understanding the conformation transition in the activation pathway of β2 adrenergic receptor via a targeted molecular dynamics simulation. Phys Chem Chem Phys 2015; 17:2512-22. [DOI: 10.1039/c4cp04528a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The conformation transition in the activation pathway of β2 adrenergic receptor was explored mainly using a target molecular dynamics simulation.
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Affiliation(s)
- Xiuchan Xiao
- Faculty of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Xiaojun Zeng
- Faculty of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Yuan Yuan
- College of Management
- Southwest University for Nationalities
- Chengdu
- People's Republic of China
| | - Nan Gao
- Faculty of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Yanzhi Guo
- Faculty of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Xuemei Pu
- Faculty of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
| | - Menglong Li
- Faculty of Chemistry
- Sichuan University
- Chengdu
- People's Republic of China
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50
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On the modularity of the intrinsic flexibility of the µ opioid receptor: a computational study. PLoS One 2014; 9:e115856. [PMID: 25549261 PMCID: PMC4280117 DOI: 10.1371/journal.pone.0115856] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 12/01/2014] [Indexed: 11/19/2022] Open
Abstract
The µ opioid receptor (µOR), the principal target to control pain, belongs to the G protein-coupled receptors (GPCRs) family, one of the most highlighted protein families due to their importance as therapeutic targets. The conformational flexibility of GPCRs is one of their essential characteristics as they take part in ligand recognition and subsequent activation or inactivation mechanisms. It is assessed that the intrinsic mechanical properties of the µOR, more specifically its particular flexibility behavior, would facilitate the accomplishment of specific biological functions, at least in their first steps, even in the absence of a ligand or any chemical species usually present in its biological environment. The study of the mechanical properties of the µOR would thus bring some indications regarding the highly efficient ability of the µOR to transduce cellular message. We therefore investigate the intrinsic flexibility of the µOR in its apo-form using all-atom Molecular Dynamics simulations at the sub-microsecond time scale. We particularly consider the µOR embedded in a simplified membrane model without specific ions, particular lipids, such as cholesterol moieties, or any other chemical species that could affect the flexibility of the µOR. Our analyses highlighted an important local effect due to the various bendability of the helices resulting in a diversity of shape and volume sizes adopted by the µOR binding site. Such property explains why the µOR can interact with ligands presenting highly diverse structural geometry. By investigating the topology of the µOR binding site, a conformational global effect is depicted: the correlation between the motional modes of the extra- and intracellular parts of µOR on one hand, along with a clear rigidity of the central µOR domain on the other hand. Our results show how the modularity of the µOR flexibility is related to its pre-ability to activate and to present a basal activity.
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