Amy E. Keating

6.5k total citations
94 papers, 4.7k citations indexed

About

Amy E. Keating is a scholar working on Molecular Biology, Materials Chemistry and Organic Chemistry. According to data from OpenAlex, Amy E. Keating has authored 94 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Molecular Biology, 13 papers in Materials Chemistry and 11 papers in Organic Chemistry. Recurrent topics in Amy E. Keating's work include RNA and protein synthesis mechanisms (33 papers), Protein Structure and Dynamics (24 papers) and Machine Learning in Bioinformatics (13 papers). Amy E. Keating is often cited by papers focused on RNA and protein synthesis mechanisms (33 papers), Protein Structure and Dynamics (24 papers) and Machine Learning in Bioinformatics (13 papers). Amy E. Keating collaborates with scholars based in United States, Canada and Israel. Amy E. Keating's co-authors include Gevorg Grigoryan, J. Robert Newman, Robert A. Grant, Bonnie Berger, Gideon Schreiber, Taijiao Jiang, K. N. Houk, Orr Ashenberg, James R. Apgar and T. Scott Chen and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Amy E. Keating

92 papers receiving 4.7k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Amy E. Keating	3.7k	516	508	490	455	94	4.7k
R.L. Brady	3.3k 0.9×	591 1.1×	384 0.8×	368 0.8×	334 0.7×	87	4.7k
Jacek Otlewski	4.1k 1.1×	464 0.9×	296 0.6×	465 0.9×	825 1.8×	167	5.4k
Marko Hyvönen	3.4k 0.9×	324 0.6×	538 1.1×	312 0.6×	790 1.7×	97	4.6k
Lanette Fee	2.9k 0.8×	717 1.4×	237 0.5×	244 0.5×	358 0.8×	12	4.0k
Dieter H. Klaubert	3.5k 0.9×	518 1.0×	1.1k 2.2×	372 0.8×	484 1.1×	59	5.2k
M. Teresa Pisabarro	3.6k 1.0×	443 0.9×	363 0.7×	279 0.6×	1.1k 2.5×	100	5.0k
Peer R. E. Mittl	2.9k 0.8×	440 0.9×	380 0.7×	272 0.6×	273 0.6×	85	4.0k
Ivan R. Corrêa	4.0k 1.1×	323 0.6×	977 1.9×	388 0.8×	767 1.7×	98	5.5k
Graeme Winter	2.9k 0.8×	1.1k 2.2×	335 0.7×	270 0.6×	252 0.6×	48	4.2k
Karyn T. O’Neil	3.6k 1.0×	726 1.4×	226 0.4×	829 1.7×	361 0.8×	53	4.4k

Countries citing papers authored by Amy E. Keating

Since Specialization

Citations

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

Fields of papers citing papers by Amy E. Keating

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amy E. Keating. 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 Amy E. Keating. The network helps show where Amy E. Keating may publish in the future.

Co-authorship network of co-authors of Amy E. Keating

This figure shows the co-authorship network connecting the top 25 collaborators of Amy E. Keating. A scholar is included among the top collaborators of Amy E. Keating 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 Amy E. Keating. Amy E. Keating 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

Keating, Amy E., et al.. (2025). High-throughput discovery of inhibitory protein fragments with AlphaFold. Proceedings of the National Academy of Sciences. 122(6). e2322412122–e2322412122. 3 indexed citations

Lee, Hye‐Kyung, Rachael L. Philips, Sung‐Gwon Lee, et al.. (2025). STAT5B leukemic mutations, altering SH2 tyrosine 665, have opposing impacts on immune gene programs. Life Science Alliance. 8(7). e202503222–e202503222. 1 indexed citations

Ramos, Alejandra, et al.. (2024). Elaboration of the Homer1 recognition landscape reveals incomplete divergence of paralogous EVH1 domains. Protein Science. 33(8). e5094–e5094. 2 indexed citations

Mądry, Aleksander, et al.. (2024). Jointly Embedding Protein Structures and Sequences through Residue Level Alignment. PubMed. 2(4). 2 indexed citations

Keating, Amy E., et al.. (2023). Marginal specificity in protein interactions constrains evolution of a paralogous family. Proceedings of the National Academy of Sciences. 120(18). e2221163120–e2221163120. 15 indexed citations

Grant, Robert A., et al.. (2023). Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization. Structure. 31(3). 265–281.e7. 8 indexed citations

Desta, Israel, et al.. (2022). Neural network‐derived Potts models for structure‐based protein design using backbone atomic coordinates and tertiary motifs. Protein Science. 32(2). e4554–e4554. 9 indexed citations

Rigoldi, Federica, et al.. (2022). Molecular determinants of TRAF6 binding specificity suggest that native interaction partners are not optimized for affinity. Protein Science. 31(11). e4429–e4429. 6 indexed citations

Linghu, Changyang, Pablo A. Valdés, Or A. Shemesh, et al.. (2020). Spatial Multiplexing of Fluorescent Reporters for Imaging Signaling Network Dynamics. Cell. 183(6). 1682–1698.e24. 45 indexed citations

10.

Jenson, J.M., et al.. (2018). Peptide design by optimization on a data-parameterized protein interaction landscape. Proceedings of the National Academy of Sciences. 115(44). E10342–E10351. 41 indexed citations

11.

Park, Won Min, Mostafa Bedewy, Karl K. Berggren, & Amy E. Keating. (2017). Modular assembly of a protein nanotriangle using orthogonally interacting coiled coils. Scientific Reports. 7(1). 10577–10577. 39 indexed citations

12.

Chan, Gary C., et al.. (2016). Selective peptide inhibitors of antiapoptotic cellular and viral Bcl-2 proteins lead to cytochrome c release during latent Kaposi’s sarcoma-associated herpesvirus infection. DSpace@MIT (Massachusetts Institute of Technology). 3 indexed citations

13.

Keating, Amy E., et al.. (2016). Designing helical peptide inhibitors of protein–protein interactions. Current Opinion in Structural Biology. 39. 27–38. 58 indexed citations

14.

Negron, Christopher & Amy E. Keating. (2013). Multistate Protein Design Using CLEVER and CLASSY. Methods in enzymology on CD-ROM/Methods in enzymology. 523. 171–190. 20 indexed citations

15.

Ashenberg, Orr, Amy E. Keating, & Michael T. Laub. (2013). Helix Bundle Loops Determine Whether Histidine Kinases Autophosphorylate in cis or in trans. Journal of Molecular Biology. 425(7). 1198–1209. 62 indexed citations

16.

Dutta, Sanjib, et al.. (2010). Determinants of BH3 Binding Specificity for Mcl-1 versus Bcl-xL. Journal of Molecular Biology. 398(5). 747–762. 112 indexed citations

17.

Grigoryan, Gevorg, et al.. (2009). Design of protein-interaction specificity gives selective bZIP-binding peptides. Nature. 458(7240). 859–864. 285 indexed citations

18.

Ali, Mayssam H., Christina Taylor, Gevorg Grigoryan, et al.. (2005). Design of a Heterospecific, Tetrameric, 21-Residue Miniprotein with Mixed α/β Structure. Structure. 13(2). 225–234. 28 indexed citations

19.

Newman, J. Robert & Amy E. Keating. (2003). Comprehensive Identification of Human bZIP Interactions with Coiled-Coil Arrays. Science. 300(5628). 2097–2101. 381 indexed citations

20.

Keating, Thomas A., C. Gary Marshall, Christopher T. Walsh, & Amy E. Keating. (2002). The structure of VibH represents nonribosomal peptide synthetase condensation, cyclization and epimerization domains. Nature Structural Biology. 9(7). 522–6. 189 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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