Madoka Akimoto

756 total citations
31 papers, 562 citations indexed

About

Madoka Akimoto is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Madoka Akimoto has authored 31 papers receiving a total of 562 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 6 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Madoka Akimoto's work include Receptor Mechanisms and Signaling (12 papers), Protein Kinase Regulation and GTPase Signaling (9 papers) and Computational Drug Discovery Methods (7 papers). Madoka Akimoto is often cited by papers focused on Receptor Mechanisms and Signaling (12 papers), Protein Kinase Regulation and GTPase Signaling (9 papers) and Computational Drug Discovery Methods (7 papers). Madoka Akimoto collaborates with scholars based in Canada, United States and Germany. Madoka Akimoto's co-authors include Giuseppe Melacini, Rajeevan Selvaratnam, Bryan VanSchouwen, Stephen Boulton, Susan S. Taylor, Geeta Verma, Oliver F. Lange, Zaiyong Zhang, Eric A. Accili and Jinfeng Huang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Biological Chemistry.

In The Last Decade

Madoka Akimoto

31 papers receiving 561 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Madoka Akimoto Canada 15 471 98 78 72 69 31 562
Henrik Keränen Sweden 10 313 0.7× 107 1.1× 42 0.5× 42 0.6× 24 0.3× 10 420
Chidochangu P. Mpamhanga United Kingdom 8 438 0.9× 158 1.6× 53 0.7× 113 1.6× 22 0.3× 10 635
David Ban United States 11 662 1.4× 37 0.4× 21 0.3× 40 0.6× 58 0.8× 14 787
Meredith A. Skiba United States 14 561 1.2× 69 0.7× 24 0.3× 196 2.7× 52 0.8× 23 714
Michael Forstner Switzerland 15 453 1.0× 23 0.2× 71 0.9× 59 0.8× 34 0.5× 21 589
Philippe Cronet Germany 13 654 1.4× 44 0.4× 79 1.0× 115 1.6× 19 0.3× 15 787
Arina Hadziselimovic United States 13 728 1.5× 67 0.7× 368 4.7× 155 2.2× 62 0.9× 17 999
Kristoff T. Homan United States 17 755 1.6× 68 0.7× 45 0.6× 363 5.0× 124 1.8× 26 898
П.В. Ершов Russia 14 229 0.5× 46 0.5× 35 0.4× 67 0.9× 14 0.2× 63 547
Apurba Bhattarai United States 11 462 1.0× 157 1.6× 63 0.8× 62 0.9× 6 0.1× 16 590

Countries citing papers authored by Madoka Akimoto

Since Specialization
Citations

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

Fields of papers citing papers by Madoka Akimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Madoka Akimoto

This figure shows the co-authorship network connecting the top 25 collaborators of Madoka Akimoto. A scholar is included among the top collaborators of Madoka Akimoto 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 Madoka Akimoto. Madoka Akimoto 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.
Huang, Jinfeng, et al.. (2023). Early-Onset Parkinson Mutation Remodels Monomer–Fibril Interactions to Allosterically Amplify Synuclein’s Amyloid Cascade. SHILAP Revista de lepidopterología. 3(12). 3485–3493. 1 indexed citations
2.
Akimoto, Madoka, et al.. (2022). Allosteric regulation of cyclic nucleotide-dependent protein kinases. Canadian Journal of Chemistry. 100(9). 649–659. 1 indexed citations
3.
Ahmed, Rashik, et al.. (2021). Atomic Resolution Map of Hierarchical Self-Assembly for an Amyloidogenic Protein Probed through Thermal 15 N–R 2 Correlation Matrices. Journal of the American Chemical Society. 143(12). 4668–4679. 13 indexed citations
4.
Akimoto, Madoka, Stephen Boulton, Jinfeng Huang, et al.. (2021). Noncanonical protein kinase A activation by oligomerization of regulatory subunits as revealed by inherited Carney complex mutations. Proceedings of the National Academy of Sciences. 118(21). 9 indexed citations
5.
VanSchouwen, Bryan, et al.. (2021). State-selective frustration as a key driver of allosteric pluripotency. Chemical Science. 12(34). 11565–11575. 6 indexed citations
6.
Shimomura, Takushi, Yoshihiro Kubo, Takayuki Oka, et al.. (2021). Mechanism of hERG inhibition by gating-modifier toxin, APETx1, deduced by functional characterization. BMC Molecular and Cell Biology. 22(1). 3–3. 10 indexed citations
7.
Das, Rahul, et al.. (2021). Backbone resonance assignment of the cAMP-binding domains of the protein kinase A regulatory subunit Iα. Biomolecular NMR Assignments. 15(2). 379–382. 1 indexed citations
8.
Boulton, Stephen, Cristina Olivieri, Madoka Akimoto, et al.. (2020). CHESPA/CHESCA-SPARKY: automated NMR data analysis plugins for SPARKY to map protein allostery. Bioinformatics. 37(8). 1176–1177. 11 indexed citations
9.
Boulton, Stephen, et al.. (2020). Allosteric Mechanisms of Nonadditive Substituent Contributions to Protein-Ligand Binding. Biophysical Journal. 119(6). 1135–1146. 9 indexed citations
10.
VanSchouwen, Bryan, et al.. (2020). Allosteric inhibition explained through conformational ensembles sampling distinct “mixed” states. Computational and Structural Biotechnology Journal. 18. 3803–3818. 32 indexed citations
11.
Akimoto, Madoka, et al.. (2020). Allosteric pluripotency as revealed by protein kinase A. Science Advances. 6(25). eabb1250–eabb1250. 22 indexed citations
12.
Akimoto, Madoka, et al.. (2020). An NMR based phosphodiesterase assay. Chemical Communications. 56(58). 8091–8094. 3 indexed citations
13.
Huang, Jinfeng, et al.. (2020). Mechanism of allosteric inhibition in the Plasmodium falciparum cGMP-dependent protein kinase. Journal of Biological Chemistry. 295(25). 8480–8491. 16 indexed citations
14.
Boulton, Stephen, et al.. (2017). Free energy landscape remodeling of the cardiac pacemaker channel explains the molecular basis of familial sinus bradycardia. Journal of Biological Chemistry. 292(15). 6414–6428. 17 indexed citations
15.
16.
VanSchouwen, Bryan, et al.. (2015). Role of Dynamics in the Autoinhibition and Activation of the Hyperpolarization-activated Cyclic Nucleotide-modulated (HCN) Ion Channels. Journal of Biological Chemistry. 290(29). 17642–17654. 20 indexed citations
17.
Boulton, Stephen, et al.. (2014). A Tool Set to Map Allosteric Networks through the NMR Chemical Shift Covariance Analysis. Scientific Reports. 4(1). 7306–7306. 56 indexed citations
18.
Akimoto, Madoka, Zaiyong Zhang, Stephen Boulton, et al.. (2014). A Mechanism for the Auto-inhibition of Hyperpolarization-activated Cyclic Nucleotide-gated (HCN) Channel Opening and Its Relief by cAMP. Journal of Biological Chemistry. 289(32). 22205–22220. 62 indexed citations
19.
Selvaratnam, Rajeevan, Darryl R. Jones, Madoka Akimoto, et al.. (2013). A Novel Non-canonical Forkhead-associated (FHA) Domain-binding Interface Mediates the Interaction between Rad53 and Dbf4 Proteins. Journal of Biological Chemistry. 289(5). 2589–2599. 15 indexed citations
20.
Selvaratnam, Rajeevan, Madoka Akimoto, Bryan VanSchouwen, & Giuseppe Melacini. (2012). cAMP-dependent allostery and dynamics in Epac: an NMR view. Biochemical Society Transactions. 40(1). 219–223. 18 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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