Shu‐ou Shan

6.5k total citations
110 papers, 5.1k citations indexed

About

Shu‐ou Shan is a scholar working on Molecular Biology, Genetics and Cell Biology. According to data from OpenAlex, Shu‐ou Shan has authored 110 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 103 papers in Molecular Biology, 51 papers in Genetics and 25 papers in Cell Biology. Recurrent topics in Shu‐ou Shan's work include RNA and protein synthesis mechanisms (63 papers), Bacterial Genetics and Biotechnology (50 papers) and Protein Structure and Dynamics (20 papers). Shu‐ou Shan is often cited by papers focused on RNA and protein synthesis mechanisms (63 papers), Bacterial Genetics and Biotechnology (50 papers) and Protein Structure and Dynamics (20 papers). Shu‐ou Shan collaborates with scholars based in United States, Switzerland and Germany. Shu‐ou Shan's co-authors include Daniel Herschlag, Peter Walter, Kuang Shen, Xin Zhang, David Akopian, Ishu Saraogi, Nenad Ban, Sowmya Chandrasekar, Stewart N. Loh and Joseph A. Piccirilli and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Shu‐ou Shan

108 papers receiving 5.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shu‐ou Shan United States	43	4.4k	1.5k	883	648	422	110	5.1k
A.R. Ferré-D′Amaré United States	58	9.7k 2.2×	1.3k 0.8×	475 0.5×	671 1.0×	649 1.5×	144	10.5k
Michael Brenowitz United States	44	4.8k 1.1×	894 0.6×	392 0.4×	486 0.8×	490 1.2×	135	5.8k
Zoya Ignatova Germany	40	4.6k 1.0×	926 0.6×	347 0.4×	372 0.6×	401 1.0×	123	5.2k
Guillermo Montoya Spain	44	5.0k 1.1×	865 0.6×	574 0.7×	251 0.4×	318 0.8×	134	5.8k
L.S. Beese United States	39	6.1k 1.4×	1.2k 0.8×	436 0.5×	487 0.8×	588 1.4×	73	7.1k
Dietrich Suck Germany	40	5.0k 1.1×	853 0.6×	945 1.1×	707 1.1×	793 1.9×	96	6.6k
Matthias Bochtler Poland	32	4.7k 1.1×	937 0.6×	999 1.1×	329 0.5×	546 1.3×	106	5.5k
T. Kigawa Japan	46	6.1k 1.4×	717 0.5×	784 0.9×	382 0.6×	637 1.5×	181	7.5k
François Stricher Spain	25	4.0k 0.9×	746 0.5×	355 0.4×	187 0.3×	600 1.4×	32	4.9k
Karen G. Fleming United States	40	3.7k 0.8×	902 0.6×	496 0.6×	236 0.4×	378 0.9×	92	4.4k

Countries citing papers authored by Shu‐ou Shan

Since Specialization

Citations

This map shows the geographic impact of Shu‐ou Shan'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 Shu‐ou Shan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shu‐ou Shan more than expected).

Fields of papers citing papers by Shu‐ou Shan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shu‐ou Shan. 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 Shu‐ou Shan. The network helps show where Shu‐ou Shan may publish in the future.

Co-authorship network of co-authors of Shu‐ou Shan

This figure shows the co-authorship network connecting the top 25 collaborators of Shu‐ou Shan. A scholar is included among the top collaborators of Shu‐ou Shan 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 Shu‐ou Shan. Shu‐ou Shan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Yudin, Denis, Sowmya Chandrasekar, Alain Scaiola, et al.. (2025). Mechanism of cotranslational modification of histones H2A and H4 by MetAP1 and NatD. Science Advances. 11(51). eaeb1017–eaeb1017.

Yudin, Denis, Martin Gamerdinger, Sowmya Chandrasekar, et al.. (2024). NAC guides a ribosomal multienzyme complex for nascent protein processing. Nature. 633(8030). 718–724. 14 indexed citations

Cho, Hyun-Ju, et al.. (2024). Dynamic stability of Sgt2 enables selective and privileged client handover in a chaperone triad. Nature Communications. 15(1). 134–134. 5 indexed citations

Chou, Tsui‐Fen, et al.. (2023). An ankyrin repeat chaperone targets toxic oligomers during amyloidogenesis. Protein Science. 32(8). e4728–e4728. 2 indexed citations

Wang, Shuai, et al.. (2022). Ribosome profiling reveals multiple roles of SecA in cotranslational protein export. Nature Communications. 13(1). 3393–3393. 12 indexed citations

Jomaa, Ahmad, Martin Gamerdinger, Viswanathan Chandrasekaran, et al.. (2022). Mechanism of signal sequence handover from NAC to SRP on ribosomes during ER-protein targeting. Science. 375(6583). 839–844. 56 indexed citations

Chio, Un Seng, Yumeng Liu, Sangyoon Chung, et al.. (2021). Subunit cooperation in the Get1/2 receptor promotes tail-anchored membrane protein insertion. The Journal of Cell Biology. 220(11). 4 indexed citations

Cho, Hyun-Ju, et al.. (2021). J-domain proteins promote client relay from Hsp70 during tail-anchored membrane protein targeting. Journal of Biological Chemistry. 296. 100546–100546. 16 indexed citations

Lee, Jae Ho, Ahmad Jomaa, Sangyoon Chung, et al.. (2021). Receptor compaction and GTPase rearrangement drive SRP-mediated cotranslational protein translocation into the ER. Science Advances. 7(21). 21 indexed citations

10.

Siegel, Alex, Quang Vinh Lam, Gerard Kroon, et al.. (2020). A Disorder-to-Order Transition Activates an ATP-Independent Membrane Protein Chaperone. Journal of Molecular Biology. 432(24). 166708–166708. 5 indexed citations

11.

Wang, Shuai, et al.. (2019). The molecular mechanism of cotranslational membrane protein recognition and targeting by SecA. Nature Structural & Molecular Biology. 26(10). 919–929. 25 indexed citations

12.

Kobayashi, Kan, Ahmad Jomaa, Jae Ho Lee, et al.. (2018). Structure of a prehandover mammalian ribosomal SRP·SRP receptor targeting complex. Science. 360(6386). 323–327. 50 indexed citations

13.

Cho, Hyun-Ju & Shu‐ou Shan. (2018). Substrate relay in an Hsp70‐cochaperone cascade safeguards tail‐anchored membrane protein targeting. The EMBO Journal. 37(16). 42 indexed citations

14.

Wang, Shuai, et al.. (2017). SecA mediates cotranslational targeting and translocation of an inner membrane protein. The Journal of Cell Biology. 216(11). 3639–3653. 33 indexed citations

15.

Rao, Meera, Voytek Okreglak, Un Seng Chio, et al.. (2016). Multiple selection filters ensure accurate tail-anchored membrane protein targeting. eLife. 5. 66 indexed citations

16.

Gristick, Harry B., Meera Rao, Justin W. Chartron, et al.. (2014). Crystal structure of ATP-bound Get3–Get4–Get5 complex reveals regulation of Get3 by Get4. Nature Structural & Molecular Biology. 21(5). 437–442. 49 indexed citations

17.

Pierce, Nathan W., J. Eugene Lee, Xing Liu, et al.. (2013). Cand1 Promotes Assembly of New SCF Complexes through Dynamic Exchange of F Box Proteins. Cell. 153(1). 206–215. 205 indexed citations

18.

Liu, Ming, Roberto Lara‐Lemus, Shu‐ou Shan, et al.. (2012). Impaired Cleavage of Preproinsulin Signal Peptide Linked to Autosomal-Dominant Diabetes. Diabetes. 61(4). 828–837. 58 indexed citations

19.

Shan, Shu‐ou, Sowmya Chandrasekar, & Peter Walter. (2007). Conformational changes in the GTPase modules of the signal reception particle and its receptor drive initiation of protein translocation. The Journal of Cell Biology. 178(4). 611–620. 62 indexed citations

20.

Shan, Shu‐ou & Peter Walter. (2003). Induced nucleotide specificity in a GTPase. Proceedings of the National Academy of Sciences. 100(8). 4480–4485. 45 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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