Alexander Schug

3.4k total citations
86 papers, 2.4k citations indexed

About

Alexander Schug is a scholar working on Molecular Biology, Materials Chemistry and Cell Biology. According to data from OpenAlex, Alexander Schug has authored 86 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Molecular Biology, 32 papers in Materials Chemistry and 8 papers in Cell Biology. Recurrent topics in Alexander Schug's work include Protein Structure and Dynamics (44 papers), RNA and protein synthesis mechanisms (30 papers) and Enzyme Structure and Function (29 papers). Alexander Schug is often cited by papers focused on Protein Structure and Dynamics (44 papers), RNA and protein synthesis mechanisms (30 papers) and Enzyme Structure and Function (29 papers). Alexander Schug collaborates with scholars based in Germany, United States and United Kingdom. Alexander Schug's co-authors include José N. Onuchic, Martin Weigt, Wolfgang Wenzel, Paul C. Whitford, Abhinav Verma, Hendrik Szurmant, Jeffrey K. Noel, Shachi Gosavi, Kevin Y. Sanbonmatsu and Heiko Lammert and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Alexander Schug

78 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexander Schug Germany 25 1.9k 713 287 227 143 86 2.4k
Yifan Song China 18 2.3k 1.2× 633 0.9× 237 0.8× 176 0.8× 160 1.1× 46 3.0k
Aaron S. Brewster United States 20 1.3k 0.7× 908 1.3× 153 0.5× 283 1.2× 71 0.5× 42 2.1k
Amy E. Keating United States 36 3.7k 2.0× 516 0.7× 303 1.1× 455 2.0× 161 1.1× 94 4.7k
Vikram Khipple Mulligan United States 20 2.5k 1.3× 494 0.7× 167 0.6× 154 0.7× 118 0.8× 32 3.0k
Scott E. Boyken United States 20 2.3k 1.2× 445 0.6× 173 0.6× 136 0.6× 167 1.2× 28 3.0k
Hidetoshi Kono Japan 29 2.5k 1.4× 582 0.8× 163 0.6× 71 0.3× 105 0.7× 107 2.9k
Gevorg Grigoryan United States 22 1.7k 0.9× 517 0.7× 105 0.4× 113 0.5× 96 0.7× 54 2.1k
Gaohua Liu United States 26 2.3k 1.3× 1.0k 1.4× 123 0.4× 214 0.9× 608 4.3× 66 2.9k
Zimei Bu United States 27 1.1k 0.6× 452 0.6× 115 0.4× 353 1.6× 147 1.0× 54 1.7k
Philip Lijnzaad Netherlands 21 2.1k 1.1× 360 0.5× 149 0.5× 139 0.6× 122 0.9× 38 2.7k

Countries citing papers authored by Alexander Schug

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Schug

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexander Schug

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Schug. A scholar is included among the top collaborators of Alexander Schug 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 Alexander Schug. Alexander Schug 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.
Pucci, Fabrizio, et al.. (2025). NucleoSeeker—precision filtering of RNA databases to curate high-quality datasets. NAR Genomics and Bioinformatics. 7(1). lqaf021–lqaf021.
2.
Berghoff, Marco, et al.. (2023). Multiscale Modeling of Spheroid Tumors: Effect of Nutrient Availability on Tumor Evolution. The Journal of Physical Chemistry B. 127(16). 3607–3615. 1 indexed citations
3.
Bazarova, Alina, Achim Basermann, Achim Streit, et al.. (2023). RNA contact prediction by data efficient deep learning. Communications Biology. 6(1). 913–913. 3 indexed citations
4.
Schug, Alexander, et al.. (2023). Towards cellular digital twins of in vivo tumors. Biophysical Journal. 122(3). 301a–302a. 3 indexed citations
5.
Lindner, Felix, Bettina Appel, Karsten Weis, et al.. (2023). CAG-Repeat RNA Hairpin Folding and Recruitment to Nuclear Speckles with a Pivotal Role of ATP as a Cosolute. Journal of the American Chemical Society. 145(17). 9571–9583. 12 indexed citations
6.
Schug, Alexander, et al.. (2022). Selection of representative structures from large biomolecular ensembles. The Journal of Chemical Physics. 156(14). 144102–144102. 1 indexed citations
7.
Christiansen, Alexander, et al.. (2021). The Trimeric Major Capsid Protein of Mavirus is stabilized by its Interlocked N-termini Enabling Core Flexibility for Capsid Assembly. Journal of Molecular Biology. 433(7). 166859–166859. 5 indexed citations
8.
Götz, Markus, et al.. (2021). Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions. Nature Machine Intelligence. 3(8). 727–734. 24 indexed citations
9.
Schug, Alexander, et al.. (2021). pyrexMD: Workflow-Orientated Python Package for Replica Exchange Molecular Dynamics. The Journal of Open Source Software. 6(68). 3325–3325. 1 indexed citations
10.
Berghoff, Marco, et al.. (2020). Cells in Silico – introducing a high-performance framework for large-scale tissue modeling. BMC Bioinformatics. 21(1). 436–436. 13 indexed citations
11.
Schug, Alexander, et al.. (2020). FRET Dyes Significantly Affect SAXS Intensities of Proteins. Israel Journal of Chemistry. 60(7). 725–734. 3 indexed citations
12.
Peter, Emanuel K., Joan–Emma Shea, & Alexander Schug. (2020). CORE-MD, a path correlated molecular dynamics simulation method. The Journal of Chemical Physics. 153(8). 84114–84114. 5 indexed citations
13.
Luo, Yi, et al.. (2019). Rising Up: Hierarchical Metal–Organic Frameworks in Experiments and Simulations. Advanced Materials. 31(26). e1901744–e1901744. 129 indexed citations
14.
Pucci, Fabrizio, et al.. (2019). pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences. Bioinformatics. 36(7). 2264–2265. 18 indexed citations
15.
Meyerhenke, Henning, et al.. (2019). diSTruct v1.0: generating biomolecular structures from distance constraints. Bioinformatics. 35(24). 5337–5338. 3 indexed citations
16.
Berghoff, Marco, et al.. (2019). Massively Parallel Large-scale Multi-model Simulation of Tumor Development. IEEE International Conference on High Performance Computing, Data, and Analytics. 1 indexed citations
17.
Mattes, Benjamin, Yonglong Dang, Gediminas Greicius, et al.. (2018). Wnt/PCP controls spreading of Wnt/β-catenin signals by cytonemes in vertebrates. eLife. 7. 103 indexed citations
18.
Uguzzoni, Guido, et al.. (2017). Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis. Proceedings of the National Academy of Sciences. 114(13). E2662–E2671. 65 indexed citations
19.
Dago, Angel E., Alexander Schug, Andrea Procaccini, et al.. (2012). Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis. Proceedings of the National Academy of Sciences. 109(26). E1733–42. 110 indexed citations
20.
Gambin, Yann, Alexander Schug, Edward A. Lemke, et al.. (2009). Direct single-molecule observation of a protein living in two opposed native structures. Proceedings of the National Academy of Sciences. 106(25). 10153–10158. 68 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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