Daniel Kavan

2.2k total citations · 1 hit paper
64 papers, 1.6k citations indexed

About

Daniel Kavan is a scholar working on Molecular Biology, Spectroscopy and Immunology. According to data from OpenAlex, Daniel Kavan has authored 64 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Molecular Biology, 15 papers in Spectroscopy and 11 papers in Immunology. Recurrent topics in Daniel Kavan's work include Mass Spectrometry Techniques and Applications (13 papers), Advanced Proteomics Techniques and Applications (9 papers) and Glycosylation and Glycoproteins Research (5 papers). Daniel Kavan is often cited by papers focused on Mass Spectrometry Techniques and Applications (13 papers), Advanced Proteomics Techniques and Applications (9 papers) and Glycosylation and Glycoproteins Research (5 papers). Daniel Kavan collaborates with scholars based in Czechia, France and Germany. Daniel Kavan's co-authors include Petr Novák, Vladimı́r Havlı́ček, Martin Strohalm, Michael Volný, Petr Man, Helena Ryšlavá, Ondřej Vaněk, Veronika Doubnerová, Petr Halada and Karel Bezouška and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Daniel Kavan

62 papers receiving 1.6k citations

Hit Papers

mMass 3: A Cross-Platform Software Environment for Precis... 2010 2026 2015 2020 2010 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Kavan Czechia 19 909 364 138 125 115 64 1.6k
Luca Signor France 21 773 0.9× 337 0.9× 45 0.3× 123 1.0× 110 1.0× 46 1.3k
Roland G. Huber Singapore 25 1.0k 1.1× 152 0.4× 141 1.0× 94 0.8× 128 1.1× 87 2.0k
Ewen Lescop France 24 1.3k 1.4× 322 0.9× 142 1.0× 115 0.9× 38 0.3× 59 1.9k
Martin Strohalm Czechia 15 725 0.8× 516 1.4× 94 0.7× 55 0.4× 54 0.5× 24 1.4k
Satoko Akashi Japan 25 1.5k 1.6× 677 1.9× 152 1.1× 157 1.3× 78 0.7× 111 2.4k
Yves Boulard France 27 1.0k 1.1× 431 1.2× 166 1.2× 59 0.5× 92 0.8× 107 2.3k
Emmanuelle Leize‐Wagner France 26 1.1k 1.2× 434 1.2× 203 1.5× 96 0.8× 134 1.2× 56 2.0k
Paul Curnow United Kingdom 19 1.5k 1.6× 195 0.5× 114 0.8× 100 0.8× 38 0.3× 47 2.1k
Mark Okon Canada 26 1.4k 1.5× 140 0.4× 211 1.5× 72 0.6× 112 1.0× 55 1.9k
Michael Mormann Germany 23 765 0.8× 393 1.1× 162 1.2× 68 0.5× 131 1.1× 60 1.4k

Countries citing papers authored by Daniel Kavan

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Kavan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Kavan

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Kavan. A scholar is included among the top collaborators of Daniel Kavan 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 Daniel Kavan. Daniel Kavan 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.
Kalaninová, Z., et al.. (2024). Postproline Cleaving Enzymes also Show Specificity to Reduced Cysteine. Analytical Chemistry. 96(48). 19084–19092.
2.
Feng, Yuzhen, Alan Kádek, Swati Saha, et al.. (2024). Glycan-induced structural activation softens the human papillomavirus capsid for entry through reduction of intercapsomere flexibility. Nature Communications. 15(1). 10076–10076. 4 indexed citations
4.
Kavan, Daniel, et al.. (2023). Quantifying the Impact of the Peptide Identification Framework on the Results of Fast Photochemical Oxidation of Protein Analysis. Journal of Proteome Research. 23(2). 609–617. 1 indexed citations
5.
Loginov, Dmitry S., Daniel Kavan, Eduardo Salido, et al.. (2022). Counterintuitive structural and functional effects due to naturally occurring mutations targeting the active site of the disease‐associated NQO1 enzyme*. FEBS Journal. 290(7). 1855–1873. 2 indexed citations
7.
Kukačka, Zdeněk, et al.. (2022). Top-Down Detection of Oxidative Protein Footprinting by Collision-Induced Dissociation, Electron-Transfer Dissociation, and Electron-Capture Dissociation. Analytical Chemistry. 94(28). 9993–10002. 11 indexed citations
8.
Giladi, Moshe, et al.. (2022). Structural basis for long-chain isoprenoid synthesis by cis -prenyltransferases. Science Advances. 8(20). eabn1171–eabn1171. 6 indexed citations
9.
Kavan, Daniel, et al.. (2021). Effects of heat treatment on metabolism of tobacco plants infected with Potato virus Y. Plant Biology. 23(S1). 131–141. 11 indexed citations
10.
Loginov, Dmitry S., Bruno Rizzuti, José L. Neira, et al.. (2021). A single evolutionarily divergent mutation determines the different FAD‐binding affinities of human and rat NQO1 due to site‐specific phosphorylation. FEBS Letters. 596(1). 29–41. 2 indexed citations
11.
Kukačka, Zdeněk, et al.. (2021). LinX: A Software Tool for Uncommon Cross-Linking Chemistry. Journal of Proteome Research. 20(4). 2021–2027. 4 indexed citations
12.
Vališ, Karel, Jiří Černý, Josef Chmelı́k, et al.. (2020). Motif orientation matters: Structural characterization of TEAD1 recognition of genomic DNA. Structure. 29(4). 345–356.e8. 3 indexed citations
13.
Hrabák, Jaroslav, et al.. (2020). Three-Dimensional Printed Target Plates for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry. Analytical Chemistry. 92(19). 12783–12788. 4 indexed citations
14.
Vaněk, Ondřej, Jan Bláha, Daniel Kavan, et al.. (2019). Production of recombinant soluble dimeric C-type lectin-like receptors of rat natural killer cells. Scientific Reports. 9(1). 17836–17836. 5 indexed citations
15.
Zeman, Jakub, Yuzuru Itoh, Zdeněk Kukačka, et al.. (2019). Binding of eIF3 in complex with eIF5 and eIF1 to the 40S ribosomal subunit is accompanied by dramatic structural changes. Nucleic Acids Research. 47(15). 8282–8300. 19 indexed citations
16.
Chmelı́k, Josef, Daniel Kavan, Zdeněk Kukačka, et al.. (2019). MS-Based Approaches Enable the Structural Characterization of Transcription Factor/DNA Response Element Complex. Biomolecules. 9(10). 535–535. 10 indexed citations
17.
Houser, Josef, et al.. (2019). HSPA1A conformational mutants reveal a conserved structural unit in Hsp70 proteins. Biochimica et Biophysica Acta (BBA) - General Subjects. 1864(1). 129458–129458. 6 indexed citations
18.
Man, Petr, Elizabeth A. Blackburn, Lenka Hernychová, et al.. (2016). Novel Entropically Driven Conformation-specific Interactions with Tomm34 Protein Modulate Hsp70 Protein Folding and ATPase Activities. Molecular & Cellular Proteomics. 15(5). 1710–1727. 16 indexed citations
19.
Kukačka, Zdeněk, et al.. (2015). Mapping protein structural changes by quantitative cross-linking. Methods. 89. 112–120. 25 indexed citations
20.
Strohalm, Martin, Petr Novák, Petr Pompach, et al.. (2009). Utilization of high‐accuracy FTICR‐MS data in protein quantitation experiments. Journal of Mass Spectrometry. 44(11). 1565–1570. 3 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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