Jonathan F. Fay

1.6k total citations
30 papers, 913 citations indexed

About

Jonathan F. Fay is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Surgery. According to data from OpenAlex, Jonathan F. Fay has authored 30 papers receiving a total of 913 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 15 papers in Cellular and Molecular Neuroscience and 4 papers in Surgery. Recurrent topics in Jonathan F. Fay's work include Receptor Mechanisms and Signaling (20 papers), Neuropeptides and Animal Physiology (6 papers) and Neuroscience and Neuropharmacology Research (5 papers). Jonathan F. Fay is often cited by papers focused on Receptor Mechanisms and Signaling (20 papers), Neuropeptides and Animal Physiology (6 papers) and Neuroscience and Neuropharmacology Research (5 papers). Jonathan F. Fay collaborates with scholars based in United States, Denmark and South Korea. Jonathan F. Fay's co-authors include David Farrens, Jay M. Janz, Bryan L. Roth, Ryan H. Gumpper, B. Krumm, Zhe Chen, Gregory M. Martin, Shicheng Zhang, Emily A. Rex and Matthew R. Whorton and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Jonathan F. Fay

29 papers receiving 909 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jonathan F. Fay United States 18 679 454 128 100 61 30 913
Hye Jin Kang South Korea 19 414 0.6× 264 0.6× 141 1.1× 105 1.1× 110 1.8× 53 1.1k
Ulrich Gergs Germany 23 1.0k 1.5× 299 0.7× 51 0.4× 72 0.7× 51 0.8× 125 1.5k
Eva Marina Schmidt Hungary 17 467 0.7× 243 0.5× 93 0.7× 43 0.4× 57 0.9× 32 1.1k
Aaron M. D’Antona United States 12 634 0.9× 441 1.0× 218 1.7× 67 0.7× 20 0.3× 25 892
Uwe Kirchhefer Germany 26 1.4k 2.0× 272 0.6× 22 0.2× 58 0.6× 113 1.9× 88 1.8k
Youwen Zhuang China 12 661 1.0× 323 0.7× 129 1.0× 44 0.4× 8 0.1× 18 896
Aaron S. Goetz United States 18 1.1k 1.6× 398 0.9× 64 0.5× 335 3.4× 31 0.5× 26 1.8k
Paul M. Sweetnam United States 21 616 0.9× 569 1.3× 42 0.3× 69 0.7× 16 0.3× 30 1.2k
H. Ongun Onaran Türkiye 17 916 1.3× 493 1.1× 45 0.4× 41 0.4× 14 0.2× 40 1.1k
Thomas J. Mangano United States 18 879 1.3× 510 1.1× 45 0.4× 39 0.4× 28 0.5× 27 1.2k

Countries citing papers authored by Jonathan F. Fay

Since Specialization
Citations

This map shows the geographic impact of Jonathan F. Fay's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jonathan F. Fay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan F. Fay more than expected).

Fields of papers citing papers by Jonathan F. Fay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan F. Fay. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jonathan F. Fay. The network helps show where Jonathan F. Fay may publish in the future.

Co-authorship network of co-authors of Jonathan F. Fay

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan F. Fay. A scholar is included among the top collaborators of Jonathan F. Fay based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jonathan F. Fay. Jonathan F. Fay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Limjunyawong, Nathachit, Can Cao, James Meixiong, et al.. (2024). MRGPRX4 mediates phospho-drug–associated pruritus in a humanized mouse model. Science Translational Medicine. 16(746). eadk8198–eadk8198. 5 indexed citations
2.
Krumm, B., Nicholas J. Kapolka, W.H. Ludlam, et al.. (2024). A neurodevelopmental disorder mutation locks G proteins in the transitory pre-activated state. Nature Communications. 15(1). 6643–6643. 5 indexed citations
3.
Kang, Hye Jin, B. Krumm, Matan Geron, et al.. (2024). Structure-guided design of a peripherally restricted chemogenetic system. Cell. 187(26). 7433–7449.e20. 5 indexed citations
4.
Wang, Chunyu, Yongfeng Liu, Marion Lanier, et al.. (2024). High-affinity agonists reveal recognition motifs for the MRGPRD GPCR. Cell Reports. 43(12). 114942–114942. 1 indexed citations
5.
Krumm, B., Jeffrey F. DiBerto, Reid H. J. Olsen, et al.. (2023). Neurotensin Receptor Allosterism Revealed in Complex with a Biased Allosteric Modulator. Biochemistry. 62(7). 1233–1248. 36 indexed citations
6.
Han, Jianming, Jingying Zhang, Sarah M. Bernhard, et al.. (2023). Ligand and G-protein selectivity in the κ-opioid receptor. Nature. 617(7960). 417–425. 43 indexed citations
7.
Zhang, Shicheng, He Chen, Chengwei Zhang, et al.. (2022). Inactive and active state structures template selective tools for the human 5-HT5A receptor. Nature Structural & Molecular Biology. 29(7). 677–687. 18 indexed citations
8.
Zhang, Shicheng, Ryan H. Gumpper, Xi‐Ping Huang, et al.. (2022). Molecular basis for selective activation of DREADD-based chemogenetics. Nature. 612(7939). 354–362. 49 indexed citations
9.
Liu, Yongfeng, Can Cao, Xi‐Ping Huang, et al.. (2022). Ligand recognition and allosteric modulation of the human MRGPRX1 receptor. Nature Chemical Biology. 19(4). 416–422. 26 indexed citations
10.
Gumpper, Ryan H., Jonathan F. Fay, & Bryan L. Roth. (2022). Molecular insights into the regulation of constitutive activity by RNA editing of 5HT2C serotonin receptors. Cell Reports. 40(7). 111211–111211. 35 indexed citations
11.
Cao, Can, Ximena Barros-Álvarez, Shicheng Zhang, et al.. (2022). Signaling snapshots of a serotonin receptor activated by the prototypical psychedelic LSD. Neuron. 110(19). 3154–3167.e7. 99 indexed citations
12.
Szundi, István, et al.. (2021). Functional integrity of membrane protein rhodopsin solubilized by styrene-maleic acid copolymer. Biophysical Journal. 120(16). 3508–3515. 7 indexed citations
13.
Fay, Jonathan F., Luba A. Aleksandrov, Timothy J. Jensen, et al.. (2018). Cryo-EM Visualization of an Active High Open Probability CFTR Anion Channel. Biochemistry. 57(43). 6234–6246. 38 indexed citations
14.
15.
Mathiasen, Signe, Sune M. Christensen, Jonathan F. Fay, et al.. (2018). Single Proteoliposome High-Content Analysis Reveals Differences in the Homo-Oligomerization of GPCRs. Biophysical Journal. 115(2). 300–312. 17 indexed citations
16.
Fay, Jonathan F. & David Farrens. (2017). Purification of Functional CB1 and Analysis by Site-Directed Fluorescence Labeling Methods. Methods in enzymology on CD-ROM/Methods in enzymology. 593. 343–370. 7 indexed citations
17.
Mathiasen, Signe, Sune M. Christensen, Juan José Fung, et al.. (2014). Nanoscale high-content analysis using compositional heterogeneities of single proteoliposomes. Nature Methods. 11(9). 931–934. 58 indexed citations
18.
19.
Janz, Jay M., Jonathan F. Fay, & David Farrens. (2003). Stability of Dark State Rhodopsin Is Mediated by a Conserved Ion Pair in Intradiscal Loop E-2. Journal of Biological Chemistry. 278(19). 16982–16991. 76 indexed citations
20.
Farrens, David, et al.. (2002). Design, expression, and characterization of a synthetic human cannabinoid receptor and cannabinoid receptor/ G‐protein fusion protein. Journal of Peptide Research. 60(6). 336–347. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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