Laura E. McKnight

1.4k total citations · 1 hit paper
10 papers, 868 citations indexed

About

Laura E. McKnight is a scholar working on Molecular Biology, Computational Theory and Mathematics and Plant Science. According to data from OpenAlex, Laura E. McKnight has authored 10 papers receiving a total of 868 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 4 papers in Computational Theory and Mathematics and 4 papers in Plant Science. Recurrent topics in Laura E. McKnight's work include Computational Drug Discovery Methods (4 papers), S100 Proteins and Annexins (4 papers) and Genomics and Chromatin Dynamics (3 papers). Laura E. McKnight is often cited by papers focused on Computational Drug Discovery Methods (4 papers), S100 Proteins and Annexins (4 papers) and Genomics and Chromatin Dynamics (3 papers). Laura E. McKnight collaborates with scholars based in United States and South Africa. Laura E. McKnight's co-authors include Howell Moffett, Matthias T. Stephan, Sirkka B. Stephan, Weihang Ji, Smitha P.S. Pillai, Martin E. Wohlfahrt, Hans‐Peter Kiem, Abigail R. Lambert, Michael Coon and David J. Weber and has published in prestigious journals such as Nature Communications, Nature Nanotechnology and Journal of Medicinal Chemistry.

In The Last Decade

Laura E. McKnight

10 papers receiving 855 citations

Hit Papers

In situ programming of leukaemia-specific T cells using s... 2017 2026 2020 2023 2017 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. In situ programming of leukaemia-specific T cells using synthetic DNA nanocarriers
    2017 593 citations Sirkka B. Stephan, Howell Moffett et al. Nature Nanotechnology profile →

Peers

Laura E. McKnight
Michael Coon United States
Jennifer A. Rohrs United States
P. Urbas United States
Yeongseon Byeon South Korea
Karthik Tiruthani United States
Urška Kamenšek Slovenia
Ge Zhu China
Xuewei Zhao United States
Junjun Chu China
Αndroulla N. Miliotou Greece
Michael Coon United States
Laura E. McKnight
Citations per year, relative to Laura E. McKnight Laura E. McKnight (= 1×) peers Michael Coon

Countries citing papers authored by Laura E. McKnight

Since Specialization
Citations

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

Fields of papers citing papers by Laura E. McKnight

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laura E. McKnight

This figure shows the co-authorship network connecting the top 25 collaborators of Laura E. McKnight. A scholar is included among the top collaborators of Laura E. McKnight 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 Laura E. McKnight. Laura E. McKnight is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Selker, Eric U., et al.. (2022). Tup1 is critical for transcriptional repression in Quiescence in S. cerevisiae. PLoS Genetics. 18(12). e1010559–e1010559. 9 indexed citations
2.
McKnight, Laura E., et al.. (2021). Basis of specificity for a conserved and promiscuous chromatin remodeling protein. eLife. 10. 13 indexed citations
3.
McKnight, Laura E., et al.. (2021). Rapid and inexpensive preparation of genome-wide nucleosome footprints from model and non-model organisms. STAR Protocols. 2(2). 100486–100486. 8 indexed citations
4.
McKnight, Laura E., et al.. (2019). Engineered Chromatin Remodeling Proteins for Precise Nucleosome Positioning. Cell Reports. 29(8). 2520–2535.e4. 8 indexed citations
5.
Stephan, Sirkka B., Howell Moffett, Laura E. McKnight, et al.. (2017). In situ programming of leukaemia-specific T cells using synthetic DNA nanocarriers. Nature Nanotechnology. 12(8). 813–820. 593 indexed citations breakdown →
6.
Melville, Zephan, et al.. (2017). X-ray crystal structure of human calcium-bound S100A1. Acta Crystallographica Section F Structural Biology Communications. 73(4). 215–221. 11 indexed citations
7.
Moffett, Howell, Michael Coon, Stefan Radtke, et al.. (2017). Hit-and-run programming of therapeutic cytoreagents using mRNA nanocarriers. Nature Communications. 8(1). 389–389. 147 indexed citations
8.
Cavalier, Michael C., Mohd. Imran Ansari, Paul T. Wilder, et al.. (2016). Small Molecule Inhibitors of Ca2+-S100B Reveal Two Protein Conformations. Journal of Medicinal Chemistry. 59(2). 592–608. 15 indexed citations
9.
McKnight, Laura E., E. Prabhu Raman, Paul T. Wilder, et al.. (2012). Structure-Based Discovery of a Novel Pentamidine-Related Inhibitor of the Calcium-Binding Protein S100B. ACS Medicinal Chemistry Letters. 3(12). 975–979. 20 indexed citations
10.
McKnight, Laura E., et al.. (2012). The Evolution of S100B Inhibitors for the Treatment of Malignant Melanoma. Future Medicinal Chemistry. 5(1). 97–109. 44 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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2026