B. Krumm

4.0k total citations · 2 hit papers
40 papers, 2.1k citations indexed

About

B. Krumm is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Immunology. According to data from OpenAlex, B. Krumm has authored 40 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 17 papers in Cellular and Molecular Neuroscience and 5 papers in Immunology. Recurrent topics in B. Krumm's work include Receptor Mechanisms and Signaling (22 papers), Neuropeptides and Animal Physiology (12 papers) and Neuroscience and Neuropharmacology Research (4 papers). B. Krumm is often cited by papers focused on Receptor Mechanisms and Signaling (22 papers), Neuropeptides and Animal Physiology (12 papers) and Neuroscience and Neuropharmacology Research (4 papers). B. Krumm collaborates with scholars based in United States, China and South Korea. B. Krumm's co-authors include Bryan L. Roth, Tao Che, Jeffrey F. DiBerto, Junpeng Deng, Yan Xiang, Wim G. J. Hol, Reid H. J. Olsen, Kuglae Kim, Reinhard Grisshammer and Justin G. English and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

B. Krumm

38 papers receiving 2.1k citations

Hit Papers

TRUPATH, an open-source biosensor platform for interrogat... 2020 2026 2022 2024 2020 2020 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
B. Krumm United States 25 1.4k 838 207 203 188 40 2.1k
Prem N. Yadav United States 30 1.5k 1.1× 907 1.1× 107 0.5× 102 0.5× 213 1.1× 81 2.6k
Poncho Meisenheimer United States 16 1.4k 1.0× 306 0.4× 93 0.4× 94 0.5× 167 0.9× 25 1.8k
Barbara J. Ebersole United States 17 952 0.7× 745 0.9× 203 1.0× 36 0.2× 119 0.6× 28 1.6k
Bin Zhou United States 24 958 0.7× 457 0.5× 32 0.2× 295 1.5× 89 0.5× 78 2.0k
Debra A. Kendall United States 40 3.1k 2.3× 1.4k 1.6× 47 0.2× 130 0.6× 137 0.7× 116 4.9k
Jonathan Bard United States 25 2.2k 1.6× 1.9k 2.3× 37 0.2× 112 0.6× 288 1.5× 56 3.7k
Hongli Hu China 18 2.2k 1.6× 1.2k 1.5× 25 0.1× 302 1.5× 53 0.3× 36 2.6k
E Shaw Switzerland 26 1.1k 0.8× 413 0.5× 184 0.9× 69 0.3× 248 1.3× 57 2.9k
Philip G. Strange United Kingdom 41 3.3k 2.4× 2.5k 3.0× 31 0.1× 373 1.8× 118 0.6× 181 4.7k
Michael R. Tota United States 25 1.7k 1.3× 899 1.1× 22 0.1× 105 0.5× 126 0.7× 38 3.0k

Countries citing papers authored by B. Krumm

Since Specialization
Citations

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

Fields of papers citing papers by B. Krumm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of B. Krumm

This figure shows the co-authorship network connecting the top 25 collaborators of B. Krumm. A scholar is included among the top collaborators of B. Krumm 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 B. Krumm. B. Krumm 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.
Krumm, B., et al.. (2025). Orchestrating NTSR1 signaling from the interface. Cell Research. 35(6). 395–396.
2.
Roth, Bryan L. & B. Krumm. (2024). Molecular glues as potential GPCR therapeutics. Biochemical Pharmacology. 228. 116402–116402. 7 indexed citations
3.
Krumm, B., Nicholas J. Kapolka, W.H. Ludlam, et al.. (2024). A neurodevelopmental disorder mutation locks G proteins in the transitory pre-activated state. Nature Communications. 15(1). 6643–6643. 5 indexed citations
4.
Gumpper, Ryan H., Yongfeng Liu, D. Dewran Koçak, et al.. (2024). Bitter taste receptor activation by cholesterol and an intracellular tastant. Nature. 628(8008). 664–671. 43 indexed citations
5.
Wang, Chunyu, Yongfeng Liu, Marion Lanier, et al.. (2024). High-affinity agonists reveal recognition motifs for the MRGPRD GPCR. Cell Reports. 43(12). 114942–114942. 1 indexed citations
6.
Han, Jianming, Jingying Zhang, Sarah M. Bernhard, et al.. (2023). Ligand and G-protein selectivity in the κ-opioid receptor. Nature. 617(7960). 417–425. 43 indexed citations
7.
Krumm, B., Jeffrey F. DiBerto, Reid H. J. Olsen, et al.. (2023). Neurotensin Receptor Allosterism Revealed in Complex with a Biased Allosteric Modulator. Biochemistry. 62(7). 1233–1248. 36 indexed citations
8.
Zhang, Shicheng, He Chen, Chengwei Zhang, et al.. (2022). Inactive and active state structures template selective tools for the human 5-HT5A receptor. Nature Structural & Molecular Biology. 29(7). 677–687. 18 indexed citations
9.
Zhang, Shicheng, Ryan H. Gumpper, Xi‐Ping Huang, et al.. (2022). Molecular basis for selective activation of DREADD-based chemogenetics. Nature. 612(7939). 354–362. 49 indexed citations
10.
Liu, Yongfeng, Can Cao, Xi‐Ping Huang, et al.. (2022). Ligand recognition and allosteric modulation of the human MRGPRX1 receptor. Nature Chemical Biology. 19(4). 416–422. 26 indexed citations
11.
Zhuang, Youwen, B. Krumm, Huibing Zhang, et al.. (2021). Mechanism of dopamine binding and allosteric modulation of the human D1 dopamine receptor. Cell Research. 31(5). 593–596. 62 indexed citations
12.
Xu, Peiyu, Sijie Huang, Chunyou Mao, et al.. (2021). Structures of the human dopamine D3 receptor-Gi complexes. Molecular Cell. 81(6). 1147–1159.e4. 57 indexed citations
13.
Kim, Kuglae, Tao Che, Ouliana Panova, et al.. (2020). Structure of a Hallucinogen-Activated Gq-Coupled 5-HT2A Serotonin Receptor. Cell. 182(6). 1574–1588.e19. 306 indexed citations breakdown →
14.
Che, Tao, Justin G. English, B. Krumm, et al.. (2020). Nanobody-enabled monitoring of kappa opioid receptor states. Nature Communications. 11(1). 1145–1145. 93 indexed citations
15.
Krumm, B., Sangbae Lee, S. Bhattacharya, et al.. (2016). Structure and dynamics of a constitutively active neurotensin receptor. Scientific Reports. 6(1). 38564–38564. 54 indexed citations
16.
Krumm, B., et al.. (2015). Structural prerequisites for G-protein activation by the neurotensin receptor. Nature Communications. 6(1). 7895–7895. 80 indexed citations
17.
Krumm, B. & Reinhard Grisshammer. (2015). Peptide ligand recognition by G protein-coupled receptors. Frontiers in Pharmacology. 6. 48–48. 29 indexed citations
18.
Krumm, B., Xiangzhi Meng, Zhixin Wang, Yan Xiang, & Junpeng Deng. (2012). A Unique Bivalent Binding and Inhibition Mechanism by the Yatapoxvirus Interleukin 18 Binding Protein. PLoS Pathogens. 8(8). e1002876–e1002876. 24 indexed citations
19.
Li, Yongchao, Laura B. Dunn, Elisa Greggio, et al.. (2009). The R1441C mutation alters the folding properties of the ROC domain of LRRK2. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1792(12). 1194–1197. 36 indexed citations
20.
Fan, Erkang, M.A. Robien, Zhongsheng Zhang, et al.. (2004). Structural biology and structure-based inhibitor design of cholera toxin and heat-labile enterotoxin. International Journal of Medical Microbiology. 294(4). 217–223. 22 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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