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Article ; Online: Cryo-electron microscopy for GPCR research and drug discovery in endocrinology and metabolism.

Duan, Jia / He, Xin-Heng / Li, Shu-Jie / Xu, H Eric

2024 Volume 20, Issue 6, Page(s) 349–365

Abstract: G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, with many GPCRs having crucial roles in endocrinology and metabolism. Cryogenic electron microscopy (cryo-EM) has revolutionized the field of structural biology, ... ...

Abstract	G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, with many GPCRs having crucial roles in endocrinology and metabolism. Cryogenic electron microscopy (cryo-EM) has revolutionized the field of structural biology, particularly regarding GPCRs, over the past decade. Since the first pair of GPCR structures resolved by cryo-EM were published in 2017, the number of GPCR structures resolved by cryo-EM has surpassed the number resolved by X-ray crystallography by 30%, reaching >650, and the number has doubled every ~0.63 years for the past 6 years. At this pace, it is predicted that the structure of 90% of all human GPCRs will be completed within the next 5-7 years. This Review highlights the general structural features and principles that guide GPCR ligand recognition, receptor activation, G protein coupling, arrestin recruitment and regulation by GPCR kinases. The Review also highlights the diversity of GPCR allosteric binding sites and how allosteric ligands could dictate biased signalling that is selective for a G protein pathway or an arrestin pathway. Finally, the authors use the examples of glycoprotein hormone receptors and glucagon-like peptide 1 receptor to illustrate the effect of cryo-EM on understanding GPCR biology in endocrinology and metabolism, as well as on GPCR-related endocrine diseases and drug discovery.
MeSH term(s)	Cryoelectron Microscopy/methods ; Humans ; Receptors, G-Protein-Coupled/metabolism ; Receptors, G-Protein-Coupled/chemistry ; Drug Discovery/methods ; Endocrinology/methods ; Animals ; Signal Transduction ; Ligands
Chemical Substances	Receptors, G-Protein-Coupled ; Ligands
Language	English
Publishing date	2024-02-29
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2489381-X
ISSN	1759-5037 ; 1759-5029
ISSN (online)	1759-5037
ISSN	1759-5029
DOI	10.1038/s41574-024-00957-1
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Zs.A 6336: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular ab Jg. 2022: Lesesaal (EG)
Zs.MG 93: Show issues

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Article: Computational characterization of transducer recognition of β2 adrenergic receptor

Zhao, Lifen / He, Xinheng / Jiang, Hualiang / Cheng, Xi

Biochemical and biophysical research communications. 2022 Feb. 12, v. 592

2022

Abstract: As an important drug target, β2 adrenergic receptor (B2AR) regulates many physiological processes, including cardiac function, airway tone and metabolic functions. The selective coupling between B2AR and specific transducers is critical for the ... ...

Abstract	As an important drug target, β2 adrenergic receptor (B2AR) regulates many physiological processes, including cardiac function, airway tone and metabolic functions. The selective coupling between B2AR and specific transducers is critical for the physiological action of the receptor. However, the molecular mechanism by which B2AR recognizes different transducers remains elusive. Here, molecular dynamics simulations of B2AR binding to three functionally important transducers (Gs, Gi and β-arrestin 1) unveiled distinct binding modes of the receptor. Involving transmembrane helices TMs 2–7 and intracellular loops ICLs 2–3, different binding interfaces for Gs and β-arrestin 1 were identified in the simulation models and further validated by various assays. The distinct recognition mode of B2AR for Gi was computationally characterized. Insights into receptor-transducer communication not only enhance our understanding of signaling bias, but also offer hints for rational drug design targeting specific signaling pathways of G-protein coupled receptors (GPCRs).
Keywords	G-proteins ; cardiac output ; drug design ; drugs ; molecular dynamics ; research
Language	English
Dates of publication	2022-0212
Size	p. 67-73.
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	205723-2
ISSN	0006-291X ; 0006-291X
ISSN (online)	0006-291X
ISSN	0006-291X
DOI	10.1016/j.bbrc.2022.01.012
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Computational characterization of transducer recognition of β2 adrenergic receptor.

Zhao, Lifen / He, Xinheng / Jiang, Hualiang / Cheng, Xi

Biochemical and biophysical research communications

2022 Volume 592, Page(s) 67–73

Abstract	As an important drug target, β2 adrenergic receptor (B2AR) regulates many physiological processes, including cardiac function, airway tone and metabolic functions. The selective coupling between B2AR and specific transducers is critical for the physiological action of the receptor. However, the molecular mechanism by which B2AR recognizes different transducers remains elusive. Here, molecular dynamics simulations of B2AR binding to three functionally important transducers (Gs, Gi and β-arrestin 1) unveiled distinct binding modes of the receptor. Involving transmembrane helices TMs 2-7 and intracellular loops ICLs 2-3, different binding interfaces for Gs and β-arrestin 1 were identified in the simulation models and further validated by various assays. The distinct recognition mode of B2AR for Gi was computationally characterized. Insights into receptor-transducer communication not only enhance our understanding of signaling bias, but also offer hints for rational drug design targeting specific signaling pathways of G-protein coupled receptors (GPCRs).
MeSH term(s)	Computer Simulation ; GTP-Binding Proteins/chemistry ; GTP-Binding Proteins/metabolism ; HEK293 Cells ; Humans ; Molecular Dynamics Simulation ; Protein Binding ; Receptors, Adrenergic, beta-2/chemistry ; Receptors, Adrenergic, beta-2/metabolism ; Signal Transduction ; beta-Arrestin 1/metabolism
Chemical Substances	Receptors, Adrenergic, beta-2 ; beta-Arrestin 1 ; GTP-Binding Proteins (EC 3.6.1.-)
Language	English
Publishing date	2022-01-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	205723-2
ISSN	1090-2104 ; 0006-291X ; 0006-291X
ISSN (online)	1090-2104 ; 0006-291X
ISSN	0006-291X
DOI	10.1016/j.bbrc.2022.01.012
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Article ; Online: Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.

Wang, Yusong / Wang, Tong / Li, Shaoning / He, Xinheng / Li, Mingyu / Wang, Zun / Zheng, Nanning / Shao, Bin / Liu, Tie-Yan

Nature communications

2024 Volume 15, Issue 1, Page(s) 313

Abstract: Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and ... ...

Abstract	Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.
Language	English
Publishing date	2024-01-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-43720-2
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Article ; Online: AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics.

Wang, Tong / He, Xinheng / Li, Mingyu / Shao, Bin / Liu, Tie-Yan

Scientific data

2023 Volume 10, Issue 1, Page(s) 549

Abstract: Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large ... ...

Abstract	Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy.
MeSH term(s)	Benchmarking ; Machine Learning ; Molecular Conformation ; Molecular Dynamics Simulation ; Oligopeptides
Chemical Substances	chignolin ; Oligopeptides
Language	English
Publishing date	2023-08-22
Publishing country	England
Document type	Dataset ; Journal Article
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-023-02465-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Chiral Sensing of Tryptophan Enantiomers Based on the Enzyme Mimics of β-Cyclodextrin-Modified Sulfur Quantum Dots.

Jiang, Weijia / He, Ran / Lv, Han / He, Xinheng / Wang, Li / Wei, Yanli

ACS sensors

2023 Volume 8, Issue 11, Page(s) 4264–4271

Abstract: Chiral recognition of amino acid plays a significant role in pharmaceutical, medical, and food science. This study describes a chiral sensing system of β-cyclodextrin (β-CD)-coated sulfur quantum dots (CD-SQDs) for the selective fluorescence recognition ... ...

Abstract	Chiral recognition of amino acid plays a significant role in pharmaceutical, medical, and food science. This study describes a chiral sensing system of β-cyclodextrin (β-CD)-coated sulfur quantum dots (CD-SQDs) for the selective fluorescence recognition of tryptophan (Trp) enantiomers. CD-SQDs were prepared by a facile assembly fission method and could selectively recognize
MeSH term(s)	Tryptophan ; Quantum Dots/chemistry ; beta-Cyclodextrins/chemistry ; Stereoisomerism
Chemical Substances	Tryptophan (8DUH1N11BX) ; beta-Cyclodextrins
Language	English
Publishing date	2023-11-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2379-3694
ISSN (online)	2379-3694
DOI	10.1021/acssensors.3c01616
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Article ; Online: AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors.

He, Xin-Heng / You, Chong-Zhao / Jiang, Hua-Liang / Jiang, Yi / Xu, H Eric / Cheng, Xi

Acta pharmacologica Sinica

2022 Volume 44, Issue 1, Page(s) 1–7

Abstract: As important drug targets, G protein-coupled receptors (GPCRs) play pivotal roles in a wide range of physiological processes. Extensive efforts of structural biology have been made on the study of GPCRs. However, a large portion of GPCR structures remain ...

Abstract	As important drug targets, G protein-coupled receptors (GPCRs) play pivotal roles in a wide range of physiological processes. Extensive efforts of structural biology have been made on the study of GPCRs. However, a large portion of GPCR structures remain unsolved due to structural instability. Recently, AlphaFold2 has been developed to predict structure models of many functionally important proteins including all members of the GPCR family. Herein we evaluated the accuracy of GPCR structure models predicted by AlphaFold2. We revealed that AlphaFold2 could capture the overall backbone features of the receptors. However, the predicted models and experimental structures were different in many aspects including the assembly of the extracellular and transmembrane domains, the shape of the ligand-binding pockets, and the conformation of the transducer-binding interfaces. These differences impeded the use of predicted structure models in the functional study and structure-based drug design of GPCRs, which required reliable high-resolution structural information.
MeSH term(s)	Models, Molecular ; Receptors, G-Protein-Coupled/metabolism ; Molecular Conformation ; Ligands ; Protein Conformation
Chemical Substances	Receptors, G-Protein-Coupled ; Ligands
Language	English
Publishing date	2022-07-01
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1360774-1
ISSN	1745-7254 ; 0253-9756 ; 1671-4083
ISSN (online)	1745-7254
ISSN	0253-9756 ; 1671-4083
DOI	10.1038/s41401-022-00938-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Loops Mediate Agonist-Induced Activation of the Stimulator of Interferon Genes Protein.

Li, Rui / Chen, Lin / He, Xinheng / Cao, Duanhua / Zhang, Zehong / Jiang, Hualiang / Chen, Kaixian / Cheng, Xi

Journal of chemical information and modeling

2023 Volume 63, Issue 23, Page(s) 7373–7381

Abstract: The stimulator of interferon genes (STING) is an important therapeutic target for cancer diseases. The activated STING recruits downstream tank-binding kinase 1 (TBK1) to trigger several important immune responses. However, the molecular mechanism of how ...

Abstract	The stimulator of interferon genes (STING) is an important therapeutic target for cancer diseases. The activated STING recruits downstream tank-binding kinase 1 (TBK1) to trigger several important immune responses. However, the molecular mechanism of how agonist molecules mediate the STING-TBK1 interactions remains elusive. Here, we performed molecular dynamics simulations to capture the conformational changes of STING and TBK1 upon agonist binding. Our simulations revealed that multiple helices (α5-α7) and especially three loops (loop 6, loop 8, and C-terminal tail) of STING participated in the allosteric mediation of the STING-TBK1 interactions. Consistent results were also observed in the simulations of the constitutive activating mutant of STING (R284S). We further identified α5 as a key region in this agonist-induced activation mechanism of STING. Free-energy perturbation calculations of multiple STING agonists demonstrated that an alkynyl group targeting α5 is a determinant for agonist activities. These results not only offer deeper insights into the agonist-induced allosteric mediation of STING-TKB1 interactions but also provide a guidance for future drug development of this important therapeutic target.
MeSH term(s)	Interferons ; Membrane Proteins/metabolism ; Molecular Dynamics Simulation
Chemical Substances	Interferons (9008-11-1) ; Membrane Proteins ; STING1 protein, human
Language	English
Publishing date	2023-10-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.3c00984
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Article ; Online: Identification of a carbohydrate recognition motif of purinergic receptors.

Zhao, Lifen / Wei, Fangyu / He, Xinheng / Dai, Antao / Yang, Dehua / Jiang, Hualiang / Wen, Liuqing / Cheng, Xi

eLife

2023 Volume 12

Abstract: As a major class of biomolecules, carbohydrates play indispensable roles in various biological processes. However, it remains largely unknown how carbohydrates directly modulate important drug targets, such as G-protein coupled receptors (GPCRs). Here, ... ...

Abstract	As a major class of biomolecules, carbohydrates play indispensable roles in various biological processes. However, it remains largely unknown how carbohydrates directly modulate important drug targets, such as G-protein coupled receptors (GPCRs). Here, we employed P2Y purinoceptor 14 (P2Y14), a drug target for inflammation and immune responses, to uncover the sugar nucleotide activation of GPCRs. Integrating molecular dynamics simulation with functional study, we identified the uridine diphosphate (UDP)-sugar-binding site on P2Y14, and revealed that a UDP-glucose might activate the receptor by bridging the transmembrane (TM) helices 2 and 7. Between TM2 and TM7 of P2Y14, a conserved salt bridging chain (K
MeSH term(s)	Receptors, Purinergic ; Nucleotides ; Uridine Diphosphate Glucose ; Sugars ; Receptors, Purinergic P2Y
Chemical Substances	Receptors, Purinergic ; Nucleotides ; Uridine Diphosphate Glucose (V50K1D7P4Y) ; Sugars ; Receptors, Purinergic P2Y
Language	English
Publishing date	2023-11-13
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.85449
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics.

Wang, Zun / Wu, Hongfei / Sun, Lixin / He, Xinheng / Liu, Zhirong / Shao, Bin / Wang, Tong / Liu, Tie-Yan

The Journal of chemical physics

2023 Volume 159, Issue 3

Abstract: Machine learning force fields (MLFFs) have gained popularity in recent years as they provide a cost-effective alternative to ab initio molecular dynamics (MD) simulations. Despite a small error on the test set, MLFFs inherently suffer from generalization ...

Abstract	Machine learning force fields (MLFFs) have gained popularity in recent years as they provide a cost-effective alternative to ab initio molecular dynamics (MD) simulations. Despite a small error on the test set, MLFFs inherently suffer from generalization and robustness issues during MD simulations. To alleviate these issues, we propose global force metrics and fine-grained metrics from element and conformation aspects to systematically measure MLFFs for every atom and every conformation of molecules. We selected three state-of-the-art MLFFs (ET, NequIP, and ViSNet) and comprehensively evaluated on aspirin, Ac-Ala3-NHMe, and Chignolin MD datasets with the number of atoms ranging from 21 to 166. Driven by the trained MLFFs on these molecules, we performed MD simulations from different initial conformations, analyzed the relationship between the force metrics and the stability of simulation trajectories, and investigated the reason for collapsed simulations. Finally, the performance of MLFFs and the stability of MD simulations can be further improved guided by the proposed force metrics for model training, specifically training MLFF models with these force metrics as loss functions, fine-tuning by reweighting samples in the original dataset, and continued training by recruiting additional unexplored data.
Language	English
Publishing date	2023-07-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	3113-6
ISSN	1089-7690 ; 0021-9606
ISSN (online)	1089-7690
ISSN	0021-9606
DOI	10.1063/5.0147023
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