William Sheffler

11.6k total citations · 8 hit papers
33 papers, 4.0k citations indexed

About

William Sheffler is a scholar working on Molecular Biology, Materials Chemistry and Ecology. According to data from OpenAlex, William Sheffler has authored 33 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 12 papers in Materials Chemistry and 8 papers in Ecology. Recurrent topics in William Sheffler's work include Protein Structure and Dynamics (17 papers), RNA and protein synthesis mechanisms (10 papers) and Enzyme Structure and Function (10 papers). William Sheffler is often cited by papers focused on Protein Structure and Dynamics (17 papers), RNA and protein synthesis mechanisms (10 papers) and Enzyme Structure and Function (10 papers). William Sheffler collaborates with scholars based in United States, United Kingdom and Sweden. William Sheffler's co-authors include David Baker, Neil P. King, Tamir Gonen, Todd O. Yeates, Shane Gonen, Jacob B. Bale, Ingemar André, Dan E. McNamara, John P. Sumida and M.R. Sawaya and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

William Sheffler

31 papers receiving 3.9k citations

Hit Papers

Computational Design of Self-Assembling Protein Nanomater... 2009 2026 2014 2020 2012 2014 2016 2009 2016 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy
    2012 522 citations Neil P. King, William Sheffler et al. Science profile →
  2. Accurate design of co-assembling multi-component protein nanomaterials
    2014 448 citations Neil P. King, Jacob B. Bale et al. Nature profile →
  3. Accurate design of megadalton-scale two-component icosahedral protein complexes
    2016 415 citations Jacob B. Bale, Shane Gonen et al. Science profile →
  4. Structure prediction for CASP8 with all‐atom refinement using Rosetta
    2009 380 citations Srivatsan Raman, Robert M. Vernon et al. Proteins Structure Function and Bioinformatics profile →
  5. Design of a hyperstable 60-subunit protein icosahedron
    2016 322 citations Yang Hsia, Jacob B. Bale et al. Nature profile →
  6. De novo design of a fluorescence-activating β-barrel
    2018 249 citations Jiayi Dou, Anastassia A. Vorobieva et al. Nature profile →
  7. Design of biologically active binary protein 2D materials
    2021 86 citations Ariel J. Ben‐Sasson, Joseph L. Watson et al. Nature profile →
  8. De novo design of pH-responsive self-assembling helical protein filaments
    2024 34 citations Hao Shen, Eric M. Lynch et al. Nature Nanotechnology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William Sheffler United States 24 3.1k 1.0k 786 547 481 33 4.0k
Neil P. King United States 26 3.0k 1.0× 577 0.6× 1.1k 1.3× 603 1.1× 669 1.4× 71 5.2k
Ingemar André Sweden 29 2.4k 0.8× 801 0.8× 430 0.5× 279 0.5× 228 0.5× 59 3.4k
Pehr B. Harbury United States 29 3.9k 1.3× 976 0.9× 306 0.4× 273 0.5× 542 1.1× 46 4.9k
Gail J. Bartlett United Kingdom 31 3.0k 1.0× 881 0.8× 244 0.3× 476 0.9× 232 0.5× 44 3.7k
Min Su United States 29 3.4k 1.1× 411 0.4× 652 0.8× 229 0.4× 175 0.4× 63 4.0k
Thomas H. LaBean United States 38 6.4k 2.1× 892 0.9× 1.3k 1.6× 536 1.0× 124 0.3× 87 7.5k
Scott E. Boyken United States 20 2.3k 0.7× 445 0.4× 232 0.3× 202 0.4× 269 0.6× 28 3.0k
Amy E. Keating United States 36 3.7k 1.2× 516 0.5× 222 0.3× 222 0.4× 490 1.0× 94 4.7k
Nils G. Walter United States 54 9.7k 3.2× 546 0.5× 892 1.1× 155 0.3× 218 0.5× 222 10.9k
Gregory A. Weiss United States 31 2.2k 0.7× 544 0.5× 434 0.6× 104 0.2× 765 1.6× 112 3.6k

Countries citing papers authored by William Sheffler

Since Specialization
Citations

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

Fields of papers citing papers by William Sheffler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William Sheffler. 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 William Sheffler. The network helps show where William Sheffler may publish in the future.

Co-authorship network of co-authors of William Sheffler

This figure shows the co-authorship network connecting the top 25 collaborators of William Sheffler. A scholar is included among the top collaborators of William Sheffler 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 William Sheffler. William Sheffler 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.
Wang, Shunzhi, Ryan D. Kibler, Andrew J. Borst, et al.. (2025). Bond-centric modular design of protein assemblies. Nature Materials. 24(10). 1644–1652. 1 indexed citations
2.
Shen, Hao, Eric M. Lynch, Joseph L. Watson, et al.. (2024). De novo design of pH-responsive self-assembling helical protein filaments. Nature Nanotechnology. 19(7). 1016–1021. 34 indexed citations breakdown →
3.
Sheffler, William, Quinton M. Dowling, Yang Hsia, et al.. (2023). Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock. PLoS Computational Biology. 19(5). e1010680–e1010680. 16 indexed citations
4.
Ben‐Sasson, Ariel J., Joseph L. Watson, William Sheffler, et al.. (2021). Author Correction: Design of biologically active binary protein 2D materials. Nature. 591(7850). E16–E16. 1 indexed citations
5.
Ben‐Sasson, Ariel J., Joseph L. Watson, William Sheffler, et al.. (2021). Design of biologically active binary protein 2D materials. Nature. 589(7842). 468–473. 86 indexed citations breakdown →
6.
Hsia, Yang, Rubul Mout, William Sheffler, et al.. (2021). Design of multi-scale protein complexes by hierarchical building block fusion. Nature Communications. 12(1). 2294–2294. 42 indexed citations
7.
Foight, Glenna, Zhizhi Wang, Per Greisen, et al.. (2019). Multi-input chemical control of protein dimerization for programming graded cellular responses. Nature Biotechnology. 37(10). 1209–1216. 57 indexed citations
8.
Marze, Nicholas, Shourya S. Roy Burman, William Sheffler, & Jeffrey J. Gray. (2018). Efficient flexible backbone protein–protein docking for challenging targets. Bioinformatics. 34(20). 3461–3469. 116 indexed citations
9.
Sahasrabuddhe, Aniruddha, Yang Hsia, Florian Büsch, et al.. (2018). Confirmation of intersubunit connectivity and topology of designed protein complexes by native MS. Proceedings of the National Academy of Sciences. 115(6). 1268–1273. 49 indexed citations
10.
Lu, Peilong, Duyoung Min, Frank DiMaio, et al.. (2018). Accurate computational design of multipass transmembrane proteins. Science. 359(6379). 1042–1046. 137 indexed citations
11.
Shen, Hao, Jorge A. Fallas, Eric M. Lynch, et al.. (2018). De novo design of self-assembling helical protein filaments. Science. 362(6415). 705–709. 110 indexed citations
12.
Dou, Jiayi, Anastassia A. Vorobieva, William Sheffler, et al.. (2018). De novo design of a fluorescence-activating β-barrel. Nature. 561(7724). 485–491. 249 indexed citations breakdown →
13.
Fallas, Jorge A., George Ueda, William Sheffler, et al.. (2016). Computational design of self-assembling cyclic protein homo-oligomers. Nature Chemistry. 9(4). 353–360. 93 indexed citations
14.
King, Neil P., Jacob B. Bale, William Sheffler, et al.. (2014). Accurate design of co-assembling multi-component protein nanomaterials. Nature. 510(7503). 103–108. 448 indexed citations breakdown →
15.
King, Neil P., William Sheffler, M.R. Sawaya, et al.. (2012). Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy. Science. 336(6085). 1171–1174. 522 indexed citations breakdown →
16.
Read, Randy J., Paul D. Adams, W.B. Arendall, et al.. (2011). A New Generation of Crystallographic Validation Tools for the Protein Data Bank. Structure. 19(10). 1395–1412. 345 indexed citations
17.
Sheffler, William & David Baker. (2010). RosettaHoles2: A volumetric packing measure for protein structure refinement and validation. Protein Science. 19(10). 1991–1995. 34 indexed citations
18.
Raman, Srivatsan, Robert M. Vernon, James Thompson, et al.. (2009). Structure prediction for CASP8 with all‐atom refinement using Rosetta. Proteins Structure Function and Bioinformatics. 77(S9). 89–99. 380 indexed citations breakdown →
19.
Das, Rhiju, Bin Qian, Srivatsan Raman, et al.. (2007). Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins Structure Function and Bioinformatics. 69(S8). 118–128. 145 indexed citations
20.
Sheffler, William, Eli Upfal, John M. Sedivy, & William Stafford Noble. (2005). A learned comparative expression measure for Affymetrix genechip DNA microarrays. PubMed. 2. 144–154. 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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