Peter M. Kasson

10.5k total citations · 1 hit paper
81 papers, 8.0k citations indexed

About

Peter M. Kasson is a scholar working on Molecular Biology, Epidemiology and Immunology. According to data from OpenAlex, Peter M. Kasson has authored 81 papers receiving a total of 8.0k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Molecular Biology, 22 papers in Epidemiology and 12 papers in Immunology. Recurrent topics in Peter M. Kasson's work include Protein Structure and Dynamics (27 papers), Lipid Membrane Structure and Behavior (25 papers) and Influenza Virus Research Studies (20 papers). Peter M. Kasson is often cited by papers focused on Protein Structure and Dynamics (27 papers), Lipid Membrane Structure and Behavior (25 papers) and Influenza Virus Research Studies (20 papers). Peter M. Kasson collaborates with scholars based in United States, Sweden and Germany. Peter M. Kasson's co-authors include Erik Lindahl, Per Larsson, Sander Pronk, Berk Hess, Szilárd Páll, Roland Schulz, Pär Bjelkmar, Jeremy C. Smith, Rossen Apostolov and Michael R. Shirts and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Angewandte Chemie International Edition.

In The Last Decade

Peter M. Kasson

77 papers receiving 7.9k citations

Hit Papers

align trajectories

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.

GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit

2013 6.0k citations Peter M. Kasson et al. Bioinformatics profile →

Peers

Peter M. Kasson

MB

MC

AMPO

BE

OC

Pär Bjelkmar Sweden

Per Larsson Sweden

Noel Southall United States

Yifei Qi United States

Michael Holst United States

Taehoon Kim South Korea

Jens Erik Nielsen Ireland

Daniel R. Roe United States

Jing Huang China

Jason Swails United States

Pär Bjelkmar Sweden

Peter M. Kasson

4.5k ×0.9

MB

1.2k ×0.9

MC

905 ×1.0

AMPO

706 ×1.0

BE

777 ×1.1

OC

Citations per year, relative to Peter M. Kasson Peter M. Kasson (= 1×) peers Pär Bjelkmar

Countries citing papers authored by Peter M. Kasson

Since Specialization

Citations

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

Fields of papers citing papers by Peter M. Kasson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter M. Kasson. 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 Peter M. Kasson. The network helps show where Peter M. Kasson may publish in the future.

Co-authorship network of co-authors of Peter M. Kasson

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

All Works

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20 of 20 papers shown

1.

Crean, Rory, et al.. (2024). Key interaction networks: Identifying evolutionarily conserved non‐covalent interaction networks across protein families. Protein Science. 33(3). e4911–e4911. 9 indexed citations

2.

Crean, Rory, et al.. (2024). Friends and relatives: insight into conformational regulation from orthologues and evolutionary lineages using KIF and KIN. Faraday Discussions. 252(0). 341–353. 3 indexed citations

3.

Morbioli, Giorgio Gianini, et al.. (2023). The ACE2 receptor accelerates but is not biochemically required for SARS-CoV-2 membrane fusion. Chemical Science. 14(25). 6997–7004. 11 indexed citations

4.

Crean, Rory, Joanna S.G. Slusky, Peter M. Kasson, & Shina Caroline Lynn Kamerlin. (2023). KIF—Key Interactions Finder: A program to identify the key molecular interactions that regulate protein conformational changes. The Journal of Chemical Physics. 158(14). 144114–144114. 11 indexed citations

5.

Kasson, Peter M., et al.. (2022). Mechanistic dissection of antibody inhibition of influenza entry yields unexpected heterogeneity. Biophysical Journal. 122(11). 1996–2006. 3 indexed citations

6.

Kasson, Peter M.. (2021). Modeling biomolecular kinetics with large-scale simulation. Current Opinion in Structural Biology. 72. 95–102.

7.

Pabis, Anna, Robert J. Rawle, & Peter M. Kasson. (2020). Influenza hemagglutinin drives viral entry via two sequential intramembrane mechanisms. Proceedings of the National Academy of Sciences. 117(13). 7200–7207. 43 indexed citations

8.

Okamoto, Kenta, Ricardo J. Ferreira, Daniel S. D. Larsson, et al.. (2020). Acquired Functional Capsid Structures in Metazoan Totivirus-like dsRNA Virus. Structure. 28(8). 888–896.e3. 8 indexed citations

9.

Rawle, Robert J., Ana M. Villamil Giraldo, Steven G. Boxer, & Peter M. Kasson. (2019). Detecting and Controlling Dye Effects in Single-Virus Fusion Experiments. Biophysical Journal. 117(3). 445–452. 19 indexed citations

10.

Johnson, Jennifer M., et al.. (2018). gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations. Bioinformatics. 34(22). 3945–3947. 6 indexed citations

11.

Liu, Ying, T. Osinski, Fengbin Wang, et al.. (2018). Structural conservation in a membrane-enveloped filamentous virus infecting a hyperthermophilic acidophile. Nature Communications. 9(1). 3360–3360. 26 indexed citations

12.

Kasson, Peter M. & Shantenu Jha. (2018). Adaptive ensemble simulations of biomolecules. Current Opinion in Structural Biology. 52. 87–94. 12 indexed citations

13.

Lipsitch, Marc, William Barclay, Rahul Raman, et al.. (2016). Viral factors in influenza pandemic risk assessment. eLife. 5. 66 indexed citations

14.

Rawle, Robert J., Steven G. Boxer, & Peter M. Kasson. (2016). Disentangling Viral Membrane Fusion from Receptor Binding Using Synthetic DNA-Lipid Conjugates. Biophysical Journal. 111(1). 123–131. 33 indexed citations

15.

Dunning, Rebecca, Marta K. Domańska, Kelly A. Dryden, Mark Yeager, & Peter M. Kasson. (2015). Effect of Cholesterol Depletion on HA Distribution in the Viral Membrane of Influenza. Biophysical Journal. 108(2). 406a–407a. 2 indexed citations

16.

Domańska, Marta K., et al.. (2015). Hemagglutinin Spatial Distribution Shifts in Response to Cholesterol in the Influenza Viral Envelope. Biophysical Journal. 109(9). 1917–1924. 19 indexed citations

17.

Domańska, Marta K., et al.. (2013). Multiphasic Effects of Cholesterol on Influenza Fusion Kinetics Reflect Multiple Mechanistic Roles. Biophysical Journal. 105(6). 1383–1387. 34 indexed citations

18.

Kasson, Peter M., Johannes B. Huppa, Mark M. Davis, & Axel T. Brünger. (2004). Quantitative analysis of membrane protein localization and signaling. 540–541.

19.

Kasson, Peter M., Johannes B. Huppa, Michelle Krogsgaard, Mark M. Davis, & Axel T. Brünger. (2004). Quantitative Imaging of Lymphocyte Membrane Protein Reorganization and Signaling. Biophysical Journal. 88(1). 579–589. 6 indexed citations

20.

Rabinowitz, Joshua D., Marija Vrljic, Peter M. Kasson, et al.. (1998). Formation of a Highly Peptide-Receptive State of Class II MHC. Immunity. 9(5). 699–709. 114 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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