Nicholas M. Levinson

1.8k total citations
27 papers, 1.4k citations indexed

About

Nicholas M. Levinson is a scholar working on Molecular Biology, Cell Biology and Oncology. According to data from OpenAlex, Nicholas M. Levinson has authored 27 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 10 papers in Cell Biology and 6 papers in Oncology. Recurrent topics in Nicholas M. Levinson's work include Microtubule and mitosis dynamics (10 papers), Protein Kinase Regulation and GTPase Signaling (7 papers) and Cancer-related Molecular Pathways (5 papers). Nicholas M. Levinson is often cited by papers focused on Microtubule and mitosis dynamics (10 papers), Protein Kinase Regulation and GTPase Signaling (7 papers) and Cancer-related Molecular Pathways (5 papers). Nicholas M. Levinson collaborates with scholars based in United States, Switzerland and France. Nicholas M. Levinson's co-authors include Steven G. Boxer, Miguel Saggu, John Kuriyan, Philip A. Cole, Stephen D. Fried, Kui Shen, Matthew A. Young, Martin Karplus, Michael A. Koldobskiy and Markus A. Seeliger and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Nicholas M. Levinson

26 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nicholas M. Levinson United States 18 741 230 226 221 183 27 1.4k
Ling Qin United States 24 1.5k 2.1× 191 0.8× 155 0.7× 502 2.3× 131 0.7× 43 2.3k
Congxin Liang United States 21 974 1.3× 416 1.8× 132 0.6× 437 2.0× 272 1.5× 29 2.2k
Carolyn A. Buser United States 27 2.0k 2.6× 184 0.8× 737 3.3× 403 1.8× 135 0.7× 47 2.6k
Maricel Torrent United States 21 651 0.9× 181 0.8× 162 0.7× 189 0.9× 301 1.6× 37 1.6k
Karl A. Koehler United States 19 693 0.9× 53 0.2× 192 0.8× 163 0.7× 176 1.0× 72 1.2k
Carsten Baldauf Germany 29 1.3k 1.7× 207 0.9× 76 0.3× 46 0.2× 207 1.1× 65 2.2k
Caroline M. R. Low United Kingdom 27 833 1.1× 147 0.6× 81 0.4× 289 1.3× 425 2.3× 52 2.8k
Sadamu Kurono Japan 17 670 0.9× 64 0.3× 225 1.0× 75 0.3× 197 1.1× 47 1.3k
Kamil Paruch Czechia 22 891 1.2× 35 0.2× 185 0.8× 611 2.8× 148 0.8× 52 1.7k
O K Glebov United States 14 704 1.0× 290 1.3× 31 0.1× 446 2.0× 177 1.0× 21 1.5k

Countries citing papers authored by Nicholas M. Levinson

Since Specialization
Citations

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

Fields of papers citing papers by Nicholas M. Levinson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicholas M. Levinson. 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 Nicholas M. Levinson. The network helps show where Nicholas M. Levinson may publish in the future.

Co-authorship network of co-authors of Nicholas M. Levinson

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas M. Levinson. A scholar is included among the top collaborators of Nicholas M. Levinson 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 Nicholas M. Levinson. Nicholas M. Levinson 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.
Tang, Jian, Ramkumar Moorthy, Özlem Demir, et al.. (2025). Targeting N-Myc in neuroblastoma with selective Aurora kinase A degraders. Cell chemical biology. 32(2). 352–362.e10. 6 indexed citations
3.
Greene, J.T., Joseph M. Muretta, Andrew R. Thompson, et al.. (2024). Allosteric coupling asymmetry mediates paradoxical activation of BRAF by type II inhibitors. eLife. 13. 2 indexed citations
4.
Levinson, Nicholas M., et al.. (2024). Exploring the conformational landscapes of protein kinases: perspectives from FRET and DEER. Biochemical Society Transactions. 52(3). 1071–1083. 2 indexed citations
5.
Faber, Erik B., Nan Wang, An‐Suei Yang, et al.. (2023). Development of allosteric and selective CDK2 inhibitors for contraception with negative cooperativity to cyclin binding. Nature Communications. 14(1). 27 indexed citations
6.
Tavernier, Nicolas, Y. Thomas, Suzanne Vigneron, et al.. (2021). Bora phosphorylation substitutes in trans for T-loop phosphorylation in Aurora A to promote mitotic entry. Nature Communications. 12(1). 1899–1899. 34 indexed citations
7.
Muretta, Joseph M., et al.. (2021). Allostery governs Cdk2 activation and differential recognition of CDK inhibitors. Nature Chemical Biology. 17(4). 456–464. 26 indexed citations
8.
Albanese, Steven K., Daniel L. Parton, Mehtap Işık, et al.. (2018). An Open Library of Human Kinase Domain Constructs for Automated Bacterial Expression. Biochemistry. 57(31). 4675–4689. 35 indexed citations
9.
Ruff, Emily F., Joseph M. Muretta, Andrew R. Thompson, et al.. (2018). A dynamic mechanism for allosteric activation of Aurora kinase A by activation loop phosphorylation. eLife. 7. 51 indexed citations
10.
Levinson, Nicholas M.. (2018). The multifaceted allosteric regulation of Aurora kinase A. Biochemical Journal. 475(12). 2025–2042. 45 indexed citations
11.
Ruff, Emily F., et al.. (2017). A water-mediated allosteric network governs activation of Aurora kinase A. Nature Chemical Biology. 13(4). 402–408. 51 indexed citations
12.
Levinson, Nicholas M. & Steven G. Boxer. (2014). A Conserved Water-Mediated Hydrogen Bond Network Underlies Selectivity of the Kinase Inhibitor Bosutinib. Biophysical Journal. 106(2). 647a–647a. 1 indexed citations
13.
Levinson, Nicholas M. & Steven G. Boxer. (2013). A conserved water-mediated hydrogen bond network defines bosutinib's kinase selectivity. Nature Chemical Biology. 10(2). 127–132. 131 indexed citations
14.
Saggu, Miguel, Nicholas M. Levinson, & Steven G. Boxer. (2012). Direct Measurements of Electric Fields in Weak Hydrogen Bonds. Biophysical Journal. 102(3). 269a–269a. 1 indexed citations
15.
Levinson, Nicholas M. & Steven G. Boxer. (2012). Structural and Spectroscopic Analysis of the Kinase Inhibitor Bosutinib and an Isomer of Bosutinib Binding to the Abl Tyrosine Kinase Domain. PLoS ONE. 7(4). e29828–e29828. 110 indexed citations
16.
Saggu, Miguel, Nicholas M. Levinson, & Steven G. Boxer. (2012). Experimental Quantification of Electrostatics in X–H···π Hydrogen Bonds. Journal of the American Chemical Society. 134(46). 18986–18997. 122 indexed citations
17.
Levinson, Nicholas M., et al.. (2011). Phosphate Vibrations Probe Local Electric Fields and Hydration in Biomolecules. Journal of the American Chemical Society. 133(34). 13236–13239. 42 indexed citations
18.
Levinson, Nicholas M., et al.. (2009). The Tyrosine Kinase Csk Dimerizes through Its SH3 Domain. PLoS ONE. 4(11). e7683–e7683. 21 indexed citations
19.
Levinson, Nicholas M., Markus A. Seeliger, Philip A. Cole, & John Kuriyan. (2008). Structural Basis for the Recognition of c-Src by Its Inactivator Csk. Cell. 134(1). 124–134. 103 indexed citations
20.
Levinson, Nicholas M., Kui Shen, Matthew A. Young, et al.. (2006). A Src-Like Inactive Conformation in the Abl Tyrosine Kinase Domain. PLoS Biology. 4(5). e144–e144. 257 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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