Paul C. Whitford

5.0k total citations
79 papers, 3.6k citations indexed

About

Paul C. Whitford is a scholar working on Molecular Biology, Materials Chemistry and Genetics. According to data from OpenAlex, Paul C. Whitford has authored 79 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Molecular Biology, 24 papers in Materials Chemistry and 13 papers in Genetics. Recurrent topics in Paul C. Whitford's work include RNA and protein synthesis mechanisms (51 papers), Protein Structure and Dynamics (35 papers) and RNA modifications and cancer (26 papers). Paul C. Whitford is often cited by papers focused on RNA and protein synthesis mechanisms (51 papers), Protein Structure and Dynamics (35 papers) and RNA modifications and cancer (26 papers). Paul C. Whitford collaborates with scholars based in United States, Brazil and Germany. Paul C. Whitford's co-authors include José N. Onuchic, Jeffrey K. Noel, Karissa Y. Sanbonmatsu, Shachi Gosavi, Yaakov Levy, Alexander Schug, Osamu Miyashita, Ryan L. Hayes, Mariana Levi and Kien Nguyen and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Paul C. Whitford

78 papers receiving 3.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paul C. Whitford United States 31 3.1k 986 395 347 303 79 3.6k
Sungchul Hohng South Korea 32 4.1k 1.3× 758 0.8× 340 0.9× 650 1.9× 507 1.7× 80 5.4k
Gwyndaf Evans United Kingdom 31 2.5k 0.8× 1.8k 1.8× 240 0.6× 222 0.6× 159 0.5× 102 4.3k
Jiji Chen United States 32 2.3k 0.7× 761 0.8× 175 0.4× 937 2.7× 229 0.8× 52 4.3k
Gongpu Zhao United States 32 1.9k 0.6× 1.1k 1.1× 221 0.6× 429 1.2× 250 0.8× 56 4.0k
Andrea Soranno United States 24 3.9k 1.3× 1.3k 1.3× 126 0.3× 223 0.6× 445 1.5× 50 4.8k
Edward A. Lemke Germany 49 5.9k 1.9× 948 1.0× 379 1.0× 643 1.9× 344 1.1× 114 7.9k
Karissa Y. Sanbonmatsu United States 36 3.5k 1.1× 500 0.5× 367 0.9× 111 0.3× 344 1.1× 93 4.1k
Lois Pollack United States 41 3.1k 1.0× 992 1.0× 164 0.4× 951 2.7× 502 1.7× 117 4.6k
Jens Michaelis Germany 32 1.8k 0.6× 556 0.6× 236 0.6× 617 1.8× 434 1.4× 86 3.2k
Steven Hayward United Kingdom 31 3.0k 1.0× 2.0k 2.0× 250 0.6× 167 0.5× 556 1.8× 79 4.2k

Countries citing papers authored by Paul C. Whitford

Since Specialization
Citations

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

Fields of papers citing papers by Paul C. Whitford

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul C. Whitford. 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 Paul C. Whitford. The network helps show where Paul C. Whitford may publish in the future.

Co-authorship network of co-authors of Paul C. Whitford

This figure shows the co-authorship network connecting the top 25 collaborators of Paul C. Whitford. A scholar is included among the top collaborators of Paul C. Whitford 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 Paul C. Whitford. Paul C. Whitford 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.
Whitford, Paul C. & José N. Onuchic. (2025). Simulating biomolecules for physiological timescales. Current Opinion in Structural Biology. 92. 103039–103039. 1 indexed citations
2.
Singh, Vivek, Yuzuru Itoh, Andreas Naschberger, et al.. (2024). Mitoribosome structure with cofactors and modifications reveals mechanism of ligand binding and interactions with L1 stalk. Nature Communications. 15(1). 4272–4272. 20 indexed citations
3.
Grunst, Michael W., Zhuan Qin, Shilei Ding, et al.. (2024). Structure and inhibition of SARS-CoV-2 spike refolding in membranes. Science. 385(6710). 757–765. 20 indexed citations
4.
Wang, Ailun, et al.. (2023). Understanding the energetics of translation in bacterial and eukaryotic ribosomes. Biophysical Journal. 122(3). 317a–317a. 1 indexed citations
5.
Whitford, Paul C., et al.. (2023). The energy landscape of the ribosome. Biopolymers. 115(2). e23570–e23570. 3 indexed citations
6.
Nguyen, Kien, et al.. (2022). Ratchet, swivel, tilt and roll: a complete description of subunit rotation in the ribosome. Nucleic Acids Research. 51(2). 919–934. 17 indexed citations
7.
Markosian, Christopher, Daniela I. Staquicini, Prashant Dogra, et al.. (2022). Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection. Molecular Biology and Evolution. 39(5). 5 indexed citations
8.
Wang, Yang, Ailun Wang, Udayan Mohanty, & Paul C. Whitford. (2022). Precise Steric Features Control Aminoacyl-tRNA Accommodation on the Ribosome. The Journal of Physical Chemistry B. 126(42). 8447–8459. 1 indexed citations
9.
Wang, Ailun, Mariana Levi, Udayan Mohanty, & Paul C. Whitford. (2022). Diffuse Ions Coordinate Dynamics in a Ribonucleoprotein Assembly. Journal of the American Chemical Society. 144(21). 9510–9522. 11 indexed citations
10.
Onuchic, José N., et al.. (2021). Sterically confined rearrangements of SARS-CoV-2 Spike protein control cell invasion. eLife. 10. 37 indexed citations
11.
Fuchs, Gabriele, et al.. (2021). The Dynamics of Subunit Rotation in a Eukaryotic Ribosome. SHILAP Revista de lepidopterología. 1(2). 204–221. 10 indexed citations
12.
Contessoto, Vinícius G., Ailun Wang, Yang Wang, et al.. (2021). SMOG 2 and OpenSMOG: Extending the limits of structure‐based models. Protein Science. 31(1). 158–172. 15 indexed citations
13.
Hoffer, Eric D., Samuel Hong, Tatsuya Maehigashi, et al.. (2020). Structural insights into mRNA reading frame regulation by tRNA modification and slippery codon–anticodon pairing. eLife. 9. 27 indexed citations
14.
Levi, Mariana, et al.. (2020). A steric gate controls P/E hybrid-state formation of tRNA on the ribosome. Nature Communications. 11(1). 5706–5706. 13 indexed citations
15.
Lima, Angélica Nakagawa, et al.. (2019). Drift-diffusion (DrDiff) framework determines kinetics and thermodynamics of two-state folding trajectory and tunes diffusion models. The Journal of Chemical Physics. 151(11). 114106–114106. 15 indexed citations
16.
Noel, Jeffrey K., Mariana Levi, Mohit Raghunathan, et al.. (2016). SMOG 2: A Versatile Software Package for Generating Structure-Based Models. PLoS Computational Biology. 12(3). e1004794–e1004794. 211 indexed citations
17.
Hayes, Ryan L., Jeffrey K. Noel, Paul C. Whitford, et al.. (2014). Reduced Model Captures Mg2+-RNA Interaction Free Energy of Riboswitches. Biophysical Journal. 106(7). 1508–1519. 38 indexed citations
18.
Ahmed, Aqeel, Paul C. Whitford, Karissa Y. Sanbonmatsu, & Florence Tama. (2011). Consensus among flexible fitting approaches improves the interpretation of cryo-EM data. Journal of Structural Biology. 177(2). 561–570. 32 indexed citations
19.
Whitford, Paul C., Osamu Miyashita, Yaakov Levy, & José N. Onuchic. (2006). Conformational Transitions of Adenylate Kinase: Switching by Cracking. Journal of Molecular Biology. 366(5). 1661–1671. 246 indexed citations
20.
Whitford, Paul C., et al.. (1987). Human monoclonal antibodies and monoclonal antibody multispecificity. British Journal of Cancer. 56(6). 709–713. 15 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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