Charles R. Sanders

10.7k total citations
195 papers, 8.6k citations indexed

About

Charles R. Sanders is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cell Biology. According to data from OpenAlex, Charles R. Sanders has authored 195 papers receiving a total of 8.6k indexed citations (citations by other indexed papers that have themselves been cited), including 157 papers in Molecular Biology, 40 papers in Cellular and Molecular Neuroscience and 40 papers in Cell Biology. Recurrent topics in Charles R. Sanders's work include Lipid Membrane Structure and Behavior (53 papers), Protein Structure and Dynamics (49 papers) and Cardiac electrophysiology and arrhythmias (36 papers). Charles R. Sanders is often cited by papers focused on Lipid Membrane Structure and Behavior (53 papers), Protein Structure and Dynamics (49 papers) and Cardiac electrophysiology and arrhythmias (36 papers). Charles R. Sanders collaborates with scholars based in United States, Germany and South Korea. Charles R. Sanders's co-authors include James H. Prestegard, Frank D. Sönnichsen, R. Scott Prosser, Wade D. Van Horn, Yuanli Song, Arina Hadziselimovic, Jeffrey K. Myers, Andrew J. Beel, Carlos G. Vanoye and Paul J. Barrett and has published in prestigious journals such as Science, Chemical Reviews and Proceedings of the National Academy of Sciences.

In The Last Decade

Charles R. Sanders

189 papers receiving 8.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charles R. Sanders United States 48 6.5k 1.6k 1.3k 993 934 195 8.6k
Gianluigi Veglia United States 47 4.9k 0.7× 2.1k 1.4× 540 0.4× 468 0.5× 705 0.8× 216 7.6k
Eduardo Perozo United States 54 8.4k 1.3× 1.0k 0.6× 3.3k 2.5× 369 0.4× 827 0.9× 132 9.8k
Alexander S. Arseniev Russia 50 6.5k 1.0× 1.0k 0.7× 1.0k 0.8× 433 0.4× 315 0.3× 261 8.1k
Vladimı́r Saudek Czechia 39 6.0k 0.9× 996 0.6× 515 0.4× 818 0.8× 937 1.0× 96 8.4k
J. Antoinette Killian Netherlands 65 11.1k 1.7× 1.5k 0.9× 1.1k 0.9× 1.1k 1.1× 1.3k 1.4× 192 13.0k
Juan Llopis Spain 29 5.3k 0.8× 436 0.3× 1.5k 1.1× 1.2k 1.2× 419 0.4× 69 7.9k
Shohei Koide United States 52 7.0k 1.1× 571 0.4× 623 0.5× 532 0.5× 1.4k 1.5× 169 9.7k
Michael Beyermann Germany 58 8.1k 1.2× 555 0.4× 1.3k 1.0× 407 0.4× 1.1k 1.1× 199 11.0k
Christian Altenbach United States 45 6.2k 0.9× 1.3k 0.8× 3.0k 2.3× 601 0.6× 404 0.4× 75 9.0k
Guy Lippens France 50 4.7k 0.7× 923 0.6× 583 0.4× 834 0.8× 1.7k 1.8× 210 7.5k

Countries citing papers authored by Charles R. Sanders

Since Specialization
Citations

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

Fields of papers citing papers by Charles R. Sanders

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles R. Sanders. 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 Charles R. Sanders. The network helps show where Charles R. Sanders may publish in the future.

Co-authorship network of co-authors of Charles R. Sanders

This figure shows the co-authorship network connecting the top 25 collaborators of Charles R. Sanders. A scholar is included among the top collaborators of Charles R. Sanders 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 Charles R. Sanders. Charles R. Sanders 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.
2.
Liu, Xiangdong, Srajan Kapoor, Charles R. Sanders, et al.. (2025). Biophysical basis of tight junction barrier modulation by a pan-claudin-binding molecule. PNAS Nexus. 4(6). pgaf189–pgaf189. 1 indexed citations
3.
4.
Kuenze, Georg, et al.. (2022). Predicting the functional impact of KCNQ1 variants with artificial neural networks. PLoS Computational Biology. 18(4). e1010038–e1010038. 7 indexed citations
5.
Marinko, Justin T., et al.. (2021). Glycosylation limits forward trafficking of the tetraspan membrane protein PMP22. Journal of Biological Chemistry. 296. 100719–100719. 11 indexed citations
6.
Hutchison, J.M.S., et al.. (2021). Recombinant SARS-CoV-2 envelope protein traffics to the trans-Golgi network following amphipol-mediated delivery into human cells. Journal of Biological Chemistry. 297(2). 100940–100940. 4 indexed citations
7.
Marinko, Justin T., Anne K. Kenworthy, & Charles R. Sanders. (2020). Peripheral myelin protein 22 preferentially partitions into ordered phase membrane domains. Proceedings of the National Academy of Sciences. 117(25). 14168–14177. 26 indexed citations
8.
Hutchison, J.M.S., Kuo‐Chih Shih, Holger A. Scheidt, et al.. (2020). Bicelles Rich in both Sphingolipids and Cholesterol and Their Use in Studies of Membrane Proteins. Journal of the American Chemical Society. 142(29). 12715–12729. 31 indexed citations
9.
Kuenze, Georg, Carlos G. Vanoye, Reshma R. Desai, et al.. (2020). Allosteric mechanism for KCNE1 modulation of KCNQ1 potassium channel activation. eLife. 9. 17 indexed citations
10.
Taylor, Keenan C., Po Wei Kang, Panpan Hou, et al.. (2020). Structure and physiological function of the human KCNQ1 channel voltage sensor intermediate state. eLife. 9. 36 indexed citations
11.
Xia, Yan, Georg Kuenze, Amanda M. Duran, et al.. (2019). A unified structural model of the mammalian translocator protein (TSPO). Journal of Biomolecular NMR. 73(6-7). 347–364. 13 indexed citations
12.
Vanoye, Carlos G., Reshma R. Desai, F Potet, et al.. (2018). High-Throughput Functional Evaluation of KCNQ1 Decrypts Variants of Unknown Significance. Circulation Genomic and Precision Medicine. 11(11). e002345–e002345. 71 indexed citations
13.
Huang, Hui, Georg Kuenze, Jarrod A. Smith, et al.. (2018). Mechanisms of KCNQ1 channel dysfunction in long QT syndrome involving voltage sensor domain mutations. Science Advances. 4(3). eaar2631–eaar2631. 65 indexed citations
14.
Barrett, Paul J., Yuanli Song, Wade D. Van Horn, et al.. (2012). The Amyloid Precursor Protein Has a Flexible Transmembrane Domain and Binds Cholesterol. Science. 336(6085). 1168–1171. 387 indexed citations
15.
Lu, Zhenwei, Wade D. Van Horn, Jiang Chen, et al.. (2012). Bicelles at Low Concentrations. Molecular Pharmaceutics. 9(4). 752–761. 47 indexed citations
16.
Hadziselimovic, Arina, et al.. (2010). Look and See if It Is Time To Induce Protein Expression in Escherichia coli Cultures. Biochemistry. 49(26). 5405–5407. 7 indexed citations
17.
Borza, Corina M., Xiwu Chen, S. Mathew, et al.. (2010). Integrin α1β1 Promotes Caveolin-1 Dephosphorylation by Activating T Cell Protein-tyrosine Phosphatase. Journal of Biological Chemistry. 285(51). 40114–40124. 32 indexed citations
18.
Vanacore, Roberto, Amy‐Joan L. Ham, Markus Voehler, et al.. (2009). A Sulfilimine Bond Identified in Collagen IV. Science. 325(5945). 1230–1234. 233 indexed citations
19.
Horn, Wade D. Van, Hak Jun Kim, Charles D. Ellis, et al.. (2009). Solution Nuclear Magnetic Resonance Structure of Membrane-Integral Diacylglycerol Kinase. Science. 324(5935). 1726–1729. 168 indexed citations
20.
Hare, Brian, et al.. (1993). Synthesis and characterization of a 13C-labeled α-mannosyl glycolipid analog from [13C]glucose. Chemistry and Physics of Lipids. 66(1-2). 155–158. 4 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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