Karsten Schnatbaum

5.3k total citations · 2 hit papers
23 papers, 1.5k citations indexed

About

Karsten Schnatbaum is a scholar working on Molecular Biology, Spectroscopy and Genetics. According to data from OpenAlex, Karsten Schnatbaum has authored 23 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 8 papers in Spectroscopy and 5 papers in Genetics. Recurrent topics in Karsten Schnatbaum's work include Advanced Proteomics Techniques and Applications (8 papers), Mass Spectrometry Techniques and Applications (6 papers) and Coagulation, Bradykinin, Polyphosphates, and Angioedema (5 papers). Karsten Schnatbaum is often cited by papers focused on Advanced Proteomics Techniques and Applications (8 papers), Mass Spectrometry Techniques and Applications (6 papers) and Coagulation, Bradykinin, Polyphosphates, and Angioedema (5 papers). Karsten Schnatbaum collaborates with scholars based in Germany, United Kingdom and Sweden. Karsten Schnatbaum's co-authors include Bernhard Küster, Daniel P. Zolg, Mathias Wilhelm, Tobias Schmidt, Ulf Reimer, Johannes Zerweck, Tobias Knaute, Bernard Delanghe, Stephan Aiche and Andreas Hühmer and has published in prestigious journals such as Analytical Chemistry, Nature Methods and Journal of Medicinal Chemistry.

In The Last Decade

Karsten Schnatbaum

23 papers receiving 1.5k citations

Hit Papers

Prosit: proteome-wide prediction of peptide tandem mass s... 2019 2026 2021 2023 2019 2019 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. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
    2019 519 citations Siegfried Gessulat, Tobias Schmidt et al. Nature Methods profile →
  2. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues
    2019 449 citations Dongxue Wang, Daniel P. Zolg et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Karsten Schnatbaum Germany 12 1.0k 588 213 135 120 23 1.5k
DaRue A. Prieto United States 18 1.1k 1.1× 860 1.5× 192 0.9× 204 1.5× 45 0.4× 34 1.8k
Aleksey Nakorchevsky United States 6 716 0.7× 581 1.0× 72 0.3× 57 0.4× 24 0.2× 7 1.2k
Rebekah L. Gundry United States 23 1.5k 1.4× 586 1.0× 162 0.8× 215 1.6× 58 0.5× 70 2.1k
Leroi V. DeSouza Canada 28 1.5k 1.5× 772 1.3× 192 0.9× 264 2.0× 19 0.2× 46 2.2k
Tobias Schmidt Germany 14 1.5k 1.5× 737 1.3× 143 0.7× 187 1.4× 37 0.3× 18 2.0k
Terri A. Addona United States 10 1.0k 1.0× 842 1.4× 226 1.1× 117 0.9× 71 0.6× 11 1.5k
Jana Zecha Germany 13 1.1k 1.0× 495 0.8× 126 0.6× 163 1.2× 86 0.7× 16 1.5k
David H. Perlman United States 15 964 1.0× 473 0.8× 101 0.5× 129 1.0× 81 0.7× 20 1.4k
Matthew Fitzgibbon United States 19 802 0.8× 435 0.7× 245 1.2× 355 2.6× 28 0.2× 38 1.5k
Andy T. Kong United States 8 1.3k 1.3× 796 1.4× 108 0.5× 144 1.1× 38 0.3× 10 1.7k

Countries citing papers authored by Karsten Schnatbaum

Since Specialization
Citations

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

Fields of papers citing papers by Karsten Schnatbaum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Karsten Schnatbaum

This figure shows the co-authorship network connecting the top 25 collaborators of Karsten Schnatbaum. A scholar is included among the top collaborators of Karsten Schnatbaum 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 Karsten Schnatbaum. Karsten Schnatbaum 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.
Loyal, Lucie, Julian Braun, Ulf Reimer, et al.. (2025). Hybrid immunity-based induction of durable pan-endemic-coronavirus immunity in the elderly. Cell Reports. 44(2). 115314–115314. 1 indexed citations
2.
Schnatbaum, Karsten, Pavlo Holenya, Michael Drosch, et al.. (2024). An Overview of Peptides and Peptide Pools for Antigen-Specific Stimulation in T-Cell Assays. Methods in molecular biology. 2768. 29–50. 1 indexed citations
3.
Chu, Chang, Florian Kern, Karsten Schnatbaum, et al.. (2022). T-cell proliferation assay for the detection of SARS-CoV-2-specific T-cells. Clinica Chimica Acta. 532. 130–136. 7 indexed citations
4.
The, Matthew, Daniel P. Zolg, Florian Bayer, et al.. (2022). Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides. Analytical Chemistry. 94(20). 7181–7190. 19 indexed citations
5.
Parker, Robert, Arun Tailor, Peng Xu, et al.. (2021). The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires. Molecular & Cellular Proteomics. 20. 100124–100124. 23 indexed citations
6.
Holenya, Pavlo, Ulf Reimer, Wolfram Woltersdorf, et al.. (2021). Peptide microarray‐based analysis of antibody responses to SARS‐CoV‐2 identifies unique epitopes with potential for diagnostic test development. European Journal of Immunology. 51(7). 1839–1849. 36 indexed citations
7.
Zecha, Jana, Chien‐Yun Lee, Florian Bayer, et al.. (2020). Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing. Molecular & Cellular Proteomics. 19(9). 1503–1522. 65 indexed citations
8.
Schnatbaum, Karsten, Victor Solis‐Mezarino, Johannes Zerweck, et al.. (2020). New Approaches for Absolute Quantification of Stable‐Isotope‐Labeled Peptide Standards for Targeted Proteomics Based on a UV Active Tag. PROTEOMICS. 20(10). e2000007–e2000007. 7 indexed citations
9.
Gessulat, Siegfried, Tobias Schmidt, Daniel P. Zolg, et al.. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature Methods. 16(6). 509–518. 519 indexed citations breakdown →
10.
Zolg, Daniel P., Mathias Wilhelm, Tobias Schmidt, et al.. (2018). ProteomeTools: Systematic Characterization of 21 Post-translational Protein Modifications by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Using Synthetic Peptides. Molecular & Cellular Proteomics. 17(9). 1850–1863. 75 indexed citations
11.
Lee, Chien‐Yun, Dongxue Wang, Mathias Wilhelm, et al.. (2018). Mining the Human Tissue Proteome for Protein Citrullination. Molecular & Cellular Proteomics. 17(7). 1378–1391. 90 indexed citations
12.
Zolg, Daniel P., Mathias Wilhelm, Peng Yu, et al.. (2017). PROCAL: A Set of 40 Peptide Standards for Retention Time Indexing, Column Performance Monitoring, and Collision Energy Calibration. PROTEOMICS. 17(21). 48 indexed citations
13.
Schnatbaum, Karsten, Hans‐Ulrich Schmoldt, Laura M. Plum, et al.. (2014). Peptide microarrays enable rapid mimotope optimization for pharmacokinetic analysis of the novel therapeutic antibody IMAB362. Biotechnology Journal. 9(4). 545–554. 8 indexed citations
14.
Faußner, Alexander, Steffen Schüssler, Marcel Bermúdez, et al.. (2012). Binding characteristics of [3H]‐JSM10292: a new cell membrane‐permeant non‐peptide bradykinin B2 receptor antagonist. British Journal of Pharmacology. 167(4). 839–853. 10 indexed citations
15.
Gibson, Christoph, Karsten Schnatbaum, Elsa Locardi, et al.. (2009). Novel Small Molecule Bradykinin B 2 Receptor Antagonists. Journal of Medicinal Chemistry. 52(14). 4370–4379. 21 indexed citations
16.
Zischinsky, Gunther, Roland Stragies, Christoph Gibson, et al.. (2009). Novel small molecule bradykinin B1 receptor antagonists. Part 2: 5-membered diaminoheterocycles. Bioorganic & Medicinal Chemistry Letters. 20(3). 1229–1232. 5 indexed citations
17.
Locardi, Elsa, Gunther Zischinsky, Roland Stragies, et al.. (2009). Novel small molecule bradykinin B1 receptor antagonists. Part 1: Benzamides and semicarbazides. Bioorganic & Medicinal Chemistry Letters. 20(3). 1225–1228. 5 indexed citations
18.
Schnatbaum, Karsten, Roland Stragies, Christoph Gibson, et al.. (2009). Novel small molecule bradykinin B1 receptor antagonists. Part 3: Hydroxyurea derivatives. Bioorganic & Medicinal Chemistry Letters. 20(3). 1233–1236. 4 indexed citations
19.
Gueler, Faikah, Song Rong, Wilfried Gwinner, et al.. (2008). Complement 5a Receptor Inhibition Improves Renal Allograft Survival. Journal of the American Society of Nephrology. 19(12). 2302–2312. 103 indexed citations
20.
Schnatbaum, Karsten, Elsa Locardi, Dirk Scharn, et al.. (2006). Peptidomimetic C5a receptor antagonists with hydrophobic substitutions at the C-terminus: Increased receptor specificity and in vivo activity. Bioorganic & Medicinal Chemistry Letters. 16(19). 5088–5092. 38 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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