Structural basis for signal recognition and transduction by platelet-activating-factor receptor

Cao, Chuanai; Tan, Qiuxiang; Xu, Chanjuan; He, Lingli; Yang, Linlin; Zhou, Ye; Zhou, Yiwei; Qiao, Anna; Lu, Minmin; Yi, Cuiying; Han, Gye Won; Wang, Xianping; Li, Xuemei; Yang, Huaiyu; Rao, Zihe; Jiang, Hualiang; Zhao, Yongfang; Liu, Jianfeng; Stevens, Raymond C.; Zhao, Qiang; Zhang, Xuejun C.; Wu, Beili

doi:10.1038/s41594-018-0068-y

Cited by 63 publications

(30 citation statements)

References 46 publications

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“…Specific binding of Zn 21 has also been observed in the of PAF1 platelet activating receptor receptor extracellular loops tightly coordinated by three His and one Glu side chains with distances as low as 2.0-2.2 Å (Cao et al, 2018). This can explain Zn 21 induced inhibition of platelet activating factor binding to the receptor and physiologic reduction information of platelets (Nunez et al, 1989).…”

Section: Nonconserved Ion Binding Sites In G-protein-coupled Recepmentioning

confidence: 86%

Harnessing Ion-Binding Sites for GPCR Pharmacology

et al. 2019

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Section: Nonconserved Ion Binding Sites In G-protein-coupled Recepmentioning

confidence: 86%

Harnessing Ion-Binding Sites for GPCR Pharmacology

et al. 2019

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“…Blind predictions were carried out on C5aR1 (PDB ID 6C1R [37]), Y1R (PDB ID 5ZBQ [38]), PTAFR (PDB ID 5ZKQ [39]), and D2R (PDB ID 6CM4 [40]). 100 models were generated for each target with RosettaGPCR (black) and the best scoring model was used for analysis.…”

Section: Figure 6: Results Of Novel Structure Prediction From Variousmentioning

confidence: 99%

“…We identified four human GPCR structures that were released following the conclusion of method development. The four structures were C5aR1, Y1R, PTAFR, and D2R (PDB IDs 6C1R [37], 5ZBQ [38], 5ZKQ [39], and 6CM4 [40], respectively). Of note, GPCR-ModSim, which is the only on-demand server we tested, had been updated to include the structures of PTAFR and D2R.…”

Section: Rosettagpcr Outperforms Other Gpcr Modeling Serversmentioning

confidence: 99%

RosettaGPCR: Multiple Template Homology Modeling of GPCRs with Rosetta

Bender

Marlow

Meiler

2019

Preprint

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G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies. However, to date, only about 10% of druggable GPCRs have had their structures characterized at atomic resolution. Further, because of the flexibility of GPCRs, alternative conformations remain to be modeled, even after an experimental structure is available. Thus, computational modeling of GPCRs is a crucial component for understanding biological function and to aid development of new therapeutics. Previous single-and multi-template homology modeling protocols in Rosetta often generated non-native-like conformations of transmembrane α-helices and/or extracellular loops. Here we present a new Rosetta protocol for modeling GPCRs that is improved in two critical ways: Firstly, it uses a blended sequence-and structurebased alignment that now accounts for structure conservation in extracellular loops. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target GPCR can be used expanding the conformational space sampled in a meaningful way. This new method allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs.A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Author SummaryStructure-based drug discovery is among the new technologies driving the development of next generation therapeutics. Inherent to this process is the availability of a protein structure for virtual screening. The most heavily drugged protein family, G-protein coupled receptors (GPCRs), however suffers from a lack of experimental structures that could hinder drug development. Technical challenges prevent the determination of every protein structure, so we turn to computational modeling to predict the structures of the remaining proteins. Again, traditional techniques fail due to the high divergence of this family. Here, we build on available methods specifically for the challenge of modeling GPCRs. This new method outperforms other methods and allows for the ability to accurately model nearly 90% of the entire GPCR family.We therefore generate a model database of all GPCRs (www.rosettagpcr.org) for use in future drug development.

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“…Bahan-bahan yang digunakan pada penelitian ini adalah struktur kristal reseptor PAF manusia (PAF-r) [21] yang didapatkan dari Protein Data Bank dengan kode struktur 5zkp, bisa diunduh pada laman https://www.rcsb.org/structure/5zkp, struktur ini nantinya dijadikan sebagai target virtual. Ligan yang digunakan sebagai senyawa penuntun adalah senyawa Y-24180 (Gambar 2) [9] , strukturnya didapatkan dari laman https://pubchem.ncbi.nlm.nih.gov/compound/I srapafant.…”

Section: Bahan Kimiaunclassified

“…Cao et al [21] baru-baru ini memperkenalkan struktur kristal reseptor PAF manusia (PAF-r), penemuan struktur ini membuka jalan untuk menjelaskan kapasitas PAF-r dalam mengikat berbagai ligan, memungkinkan untuk pengembangan strategi terapi yang efektif terhadap jalur reseptor PAF [22] , ketika perannya dalam berbagai penyakit menjadi jelas, penemuan dan perancangan molekul penghambat reseptor PAF dapat ditingkatkan [20] .…”

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Pembuatan Protokol Penapisan Virtual Berbasis Stuktur (pvbs) untuk Identifikasi Ligan Inhibitor Reseptor Platelet-Activating Factor (PAF-r) sebagai Target Terapeutik Asma menggunakan YASARA

Nugraha¹,

Istyastono

2020

J.Ris.Kim.

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Platelet-activating factor receptors (PAF-r) is known as one of the receptors that affect asthma, while the Y-21480 ligand is reported as an effective, specific, and active PAF-r antagonist for asthma patients. Research in building structure-based virtual screening protocol (SBVS) for identification of PAF-r ligand inhibitors has been performed, the receptor crystal structure was obtained from the Protein Data Bank (PDB ID: 5zkp), while the ligand used as a leading compound is Y-24180, obtained from U.S. National Library of Medicine. Interactions between ligands and receptors are observed through molecular dynamics simulations using the YASARA program at intervals up to 20 nanoseconds, ligand-receptor binding stability occurs after a time interval of 2 nanoseconds, the lowest ligand-receptor binding energy occurs at a time interval of 1,401 picoseconds. Internal validation by re-docking 1,000 times the ligand to receptor resulted in a value of Root Mean Square Deviation (RMSD) of 0.6037 Å, confirmed that SBVS protocol was accurately able to reproduce the Y-24180 ligand pose on the 5zkp crystal structure, the protocol can be used as a new approach for investigation or design of compounds that have therapeutic potential as anti-asthma.

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Structural basis for signal recognition and transduction by platelet-activating-factor receptor

Cited by 63 publications

References 46 publications

Harnessing Ion-Binding Sites for GPCR Pharmacology

Harnessing Ion-Binding Sites for GPCR Pharmacology

RosettaGPCR: Multiple Template Homology Modeling of GPCRs with Rosetta

Pembuatan Protokol Penapisan Virtual Berbasis Stuktur (pvbs) untuk Identifikasi Ligan Inhibitor Reseptor Platelet-Activating Factor (PAF-r) sebagai Target Terapeutik Asma menggunakan YASARA

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