Donald E. Elmore

1.5k total citations
52 papers, 1.2k citations indexed

About

Donald E. Elmore is a scholar working on Molecular Biology, Microbiology and Immunology. According to data from OpenAlex, Donald E. Elmore has authored 52 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 24 papers in Microbiology and 9 papers in Immunology. Recurrent topics in Donald E. Elmore's work include Antimicrobial Peptides and Activities (24 papers), Biochemical and Structural Characterization (16 papers) and Lipid Membrane Structure and Behavior (11 papers). Donald E. Elmore is often cited by papers focused on Antimicrobial Peptides and Activities (24 papers), Biochemical and Structural Characterization (16 papers) and Lipid Membrane Structure and Behavior (11 papers). Donald E. Elmore collaborates with scholars based in United States, United Kingdom and New Zealand. Donald E. Elmore's co-authors include Dennis A. Dougherty, Dania M. Figueroa, Kara J. Cutrona, Joshua A. Maurer, Steve Scheiner, C. Butler, Michael Meot‐Ner, Eleanor Fleming, Steven A. Spronk and Henry A. Lester and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and The Journal of Physical Chemistry B.

In The Last Decade

Donald E. Elmore

51 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Donald E. Elmore United States 18 867 467 194 147 129 52 1.2k
Anirban Ghosh India 24 724 0.8× 329 0.7× 106 0.5× 226 1.5× 104 0.8× 48 1.3k
Antje Pokorny United States 22 1.5k 1.8× 933 2.0× 196 1.0× 49 0.3× 180 1.4× 36 1.8k
Christopher Aisenbrey France 28 1.5k 1.8× 865 1.9× 178 0.9× 195 1.3× 215 1.7× 73 2.1k
Mark Okon Canada 26 1.4k 1.6× 155 0.3× 112 0.6× 135 0.9× 211 1.6× 55 1.9k
Sabine Castano France 20 928 1.1× 353 0.8× 77 0.4× 33 0.2× 178 1.4× 42 1.2k
Jiang Hong United States 14 749 0.9× 239 0.5× 92 0.5× 36 0.2× 85 0.7× 21 978
Anmin Tan United States 15 885 1.0× 612 1.3× 148 0.8× 36 0.2× 175 1.4× 29 1.2k
Ernesto E. Ambroggio Argentina 14 794 0.9× 272 0.6× 40 0.2× 283 1.9× 83 0.6× 36 1.0k
Chad Leidy Colombia 18 1.0k 1.2× 145 0.3× 54 0.3× 80 0.5× 145 1.1× 47 1.3k
Yukihiro Tamba Japan 14 870 1.0× 402 0.9× 55 0.3× 39 0.3× 197 1.5× 25 1.1k

Countries citing papers authored by Donald E. Elmore

Since Specialization
Citations

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

Fields of papers citing papers by Donald E. Elmore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Donald E. Elmore

This figure shows the co-authorship network connecting the top 25 collaborators of Donald E. Elmore. A scholar is included among the top collaborators of Donald E. Elmore 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 Donald E. Elmore. Donald E. Elmore 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.
Elmore, Donald E., et al.. (2019). Computational Modeling: Exploring How Mini Reserach Projects and Classroom Activities Impact Student Learning. Biophysical Journal. 116(3). 450a–450a. 1 indexed citations
2.
Elmore, Donald E., et al.. (2019). Hybrids made from antimicrobial peptides with different mechanisms of action show enhanced membrane permeabilization. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1861(10). 182980–182980. 21 indexed citations
3.
Elmore, Donald E., et al.. (2018). Systematic Analysis of Hybrid Antimicrobial Peptides. Biophysical Journal. 114(3). 453a–453a. 2 indexed citations
4.
Figueroa, Dania M., et al.. (2018). Characterizing Changes in Antimicrobial Peptide Mechanism Against Different Bacterial Strains. Biophysical Journal. 114(3). 456a–456a. 2 indexed citations
5.
Figueroa, Dania M., et al.. (2018). Production and Visualization of Bacterial Spheroplasts and Protoplasts to Characterize Antimicrobial Peptide Localization. Journal of Visualized Experiments. 10 indexed citations
6.
Figueroa, Dania M., et al.. (2016). The Role of Arginine and Lysine in Histone Derived Antimicrobial Peptides. Biophysical Journal. 110(3). 416a–416a. 3 indexed citations
7.
Mourtada, Rida, et al.. (2016). Investigating the Relationship Between Helicity and Activity in Antimicrobial Peptides with Stabilized α-Helical Structures. Biophysical Journal. 110(3). 415a–416a. 1 indexed citations
8.
Webb, Andrew C., et al.. (2014). Modular analysis of hipposin, a histone-derived antimicrobial peptide consisting of membrane translocating and membrane permeabilizing fragments. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1838(9). 2228–2233. 41 indexed citations
9.
Elmore, Donald E., et al.. (2012). Measuring Peptide Translocation into Large Unilamellar Vesicles. Journal of Visualized Experiments. e3571–e3571. 7 indexed citations
10.
Elmore, Donald E., et al.. (2011). Exploring the Response of Bacterial Cyclic Nucleotide Gated (bCNG) Ion Channels to Mechanical Stress. Biophysical Journal. 100(3). 104a–104a. 1 indexed citations
11.
Elmore, Donald E., et al.. (2011). Novel histone-derived antimicrobial peptides use different antimicrobial mechanisms. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1818(3). 869–876. 53 indexed citations
12.
Maurer, Joshua A., et al.. (2008). Confirming the Revised C-Terminal Domain of the MscL Crystal Structure. Biophysical Journal. 94(12). 4662–4667. 7 indexed citations
13.
Butler, C., et al.. (2008). Investigating the nucleic acid interactions and antimicrobial mechanism of buforin II. FEBS Letters. 582(12). 1715–1718. 74 indexed citations
14.
Sigman, Jeffrey A., et al.. (2008). Hydrogen bond residue positioning in the 599–611 loop of thimet oligopeptidase is required for substrate selection. FEBS Journal. 275(22). 5607–5617. 9 indexed citations
15.
Fleming, Eleanor, et al.. (2008). Effect of lipid composition on buforin II structure and membrane entry. Proteins Structure Function and Bioinformatics. 73(2). 480–491. 30 indexed citations
16.
Elmore, Donald E., et al.. (2006). Do Students Understand Liberal Arts Disciplines. Liberal education. 92(1). 48–55. 1 indexed citations
17.
Spronk, Steven A., Donald E. Elmore, & Dennis A. Dougherty. (2006). Voltage-Dependent Hydration and Conduction Properties of the Hydrophobic Pore of the Mechanosensitive Channel of Small Conductance. Biophysical Journal. 90(10). 3555–3569. 50 indexed citations
18.
Elmore, Donald E.. (2005). Molecular dynamics simulation of a phosphatidylglycerol membrane. FEBS Letters. 580(1). 144–148. 73 indexed citations
19.
Elmore, Donald E. & Dennis A. Dougherty. (2003). Investigating Lipid Composition Effects on the Mechanosensitive Channel of Large Conductance (MscL) Using Molecular Dynamics Simulations. Biophysical Journal. 85(3). 1512–1524. 72 indexed citations
20.
Elmore, Donald E. & Dennis A. Dougherty. (2001). Molecular Dynamics Simulations of Wild-Type and Mutant Forms of the Mycobacterium tuberculosis MscL Channel. Biophysical Journal. 81(3). 1345–1359. 73 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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