Sean M. Law

517 total citations
16 papers, 414 citations indexed

About

Sean M. Law is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Sean M. Law has authored 16 papers receiving a total of 414 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 4 papers in Materials Chemistry and 3 papers in Computational Theory and Mathematics. Recurrent topics in Sean M. Law's work include Protein Structure and Dynamics (10 papers), RNA and protein synthesis mechanisms (5 papers) and Bacterial Genetics and Biotechnology (3 papers). Sean M. Law is often cited by papers focused on Protein Structure and Dynamics (10 papers), RNA and protein synthesis mechanisms (5 papers) and Bacterial Genetics and Biotechnology (3 papers). Sean M. Law collaborates with scholars based in United States, Hong Kong and Malaysia. Sean M. Law's co-authors include Charles L. Brooks, Jessica K. Gagnon, Michael Feig, Aaron T. Frank, Shaliza Ibrahim, Mohamad Suffian Mohamad Annuar, Parviz Mohammadi, Anna K. Mapp, Logan S. Ahlstrom and Shayantani Mukherjee and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Molecular Biology and The Journal of Physical Chemistry B.

In The Last Decade

Sean M. Law

16 papers receiving 410 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sean M. Law United States 11 291 73 59 58 54 16 414
Sarah H. Lawrence United States 14 387 1.3× 162 2.2× 27 0.5× 23 0.4× 28 0.5× 19 463
Stuart P. Ballantine United Kingdom 12 329 1.1× 58 0.8× 26 0.4× 22 0.4× 18 0.3× 13 603
Douglas Burdette United States 11 334 1.1× 39 0.5× 18 0.3× 15 0.3× 165 3.1× 15 442
Matthew J. Grisewood United States 8 304 1.0× 67 0.9× 6 0.1× 53 0.9× 84 1.6× 9 392
Lezlee Dice United States 11 285 1.0× 16 0.2× 11 0.2× 41 0.7× 140 2.6× 15 408
Hilal Taymaz‐Nikerel Netherlands 12 536 1.8× 41 0.6× 4 0.1× 25 0.4× 144 2.7× 19 665
Hannes Link Switzerland 10 582 2.0× 55 0.8× 4 0.1× 31 0.5× 119 2.2× 13 738
Ajoy Velayudhan United States 14 435 1.5× 34 0.5× 12 0.2× 14 0.2× 253 4.7× 47 691
Vasiliy A. Portnoy United States 10 762 2.6× 21 0.3× 21 0.4× 16 0.3× 293 5.4× 10 835
Gen-Jun Xu China 14 314 1.1× 81 1.1× 3 0.1× 13 0.2× 162 3.0× 36 511

Countries citing papers authored by Sean M. Law

Since Specialization
Citations

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

Fields of papers citing papers by Sean M. Law

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sean M. Law

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

All Works

16 of 16 papers shown
1.
Wang, Beibei, et al.. (2016). Long-Range Signaling in MutS and MSH Homologs via Switching of Dynamic Communication Pathways. PLoS Computational Biology. 12(10). e1005159–e1005159. 8 indexed citations
2.
Feig, Michael, Beibei Wang, Monika Sharma, et al.. (2015). 10 How is fidelity maintained in nucleic acids? Two tales in DNA repair and DNA transcription from computer simulations. Journal of Biomolecular Structure and Dynamics. 33(sup1). 6–7. 1 indexed citations
3.
Ahlstrom, Logan S., Sean M. Law, Alex Dickson, & Charles L. Brooks. (2015). Multiscale Modeling of a Conditionally Disordered pH-Sensing Chaperone. Journal of Molecular Biology. 427(8). 1670–1680. 27 indexed citations
4.
Gagnon, Jessica K., Sean M. Law, & Charles L. Brooks. (2015). Flexible CDOCKER: Development and application of a pseudo‐explicit structure‐based docking method within CHARMM. Journal of Computational Chemistry. 37(8). 753–762. 100 indexed citations
5.
Law, Sean M., Jessica K. Gagnon, Anna K. Mapp, & Charles L. Brooks. (2014). Prepaying the entropic cost for allosteric regulation in KIX. Proceedings of the National Academy of Sciences. 111(33). 12067–12072. 50 indexed citations
6.
Law, Sean M., Logan S. Ahlstrom, Afra Panahi, & Charles L. Brooks. (2014). Hamiltonian Mapping Revisited: Calibrating Minimalist Models to Capture Molecular Recognition by Intrinsically Disordered Proteins. The Journal of Physical Chemistry Letters. 5(19). 3441–3444. 11 indexed citations
7.
Gagnon, Jessica K., Sean M. Law, & Charles L. Brooks. (2014). Flexible Cdocker: Development and Application of a Pseudo-Explicit Structure-Based Docking Method within Charmm. Biophysical Journal. 106(2). 646a–646a. 8 indexed citations
8.
Frank, Aaron T., Sean M. Law, Logan S. Ahlstrom, & Charles L. Brooks. (2014). Predicting Protein Backbone Chemical Shifts From Cα Coordinates: Extracting High Resolution Experimental Observables from Low Resolution Models. Journal of Chemical Theory and Computation. 11(1). 325–331. 11 indexed citations
9.
Law, Sean M., Aaron T. Frank, & Charles L. Brooks. (2014). PCASSO: A fast and efficient Cα‐based method for accurately assigning protein secondary structure elements. Journal of Computational Chemistry. 35(24). 1757–1761. 25 indexed citations
10.
Frank, Aaron T., Sean M. Law, & Charles L. Brooks. (2014). A Simple and Fast Approach for Predicting 1H and 13C Chemical Shifts: Toward Chemical Shift-Guided Simulations of RNA. The Journal of Physical Chemistry B. 118(42). 12168–12175. 25 indexed citations
11.
Law, Sean M., Bin W. Zhang, & Charles L. Brooks. (2013). pH‐sensitive residues in the p19 RNA silencing suppressor protein from carnation Italian ringspot virus affect siRNA binding stability. Protein Science. 22(5). 595–604. 19 indexed citations
12.
Law, Sean M. & Michael Feig. (2011). Base-Flipping Mechanism in Postmismatch Recognition by MutS. Biophysical Journal. 101(9). 2223–2231. 24 indexed citations
13.
Cheng, Yi‐Ming, Srinivasa M. Gopal, Sean M. Law, & Michael Feig. (2011). Molecular Dynamics Trajectory Compression with a Coarse-Grained Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(2). 476–486. 8 indexed citations
14.
Mohammadi, Parviz, Shaliza Ibrahim, Mohamad Suffian Mohamad Annuar, & Sean M. Law. (2011). Effects of different pretreatment methods on anaerobic mixed microflora for hydrogen production and COD reduction from palm oil mill effluent. Journal of Cleaner Production. 19(14). 1654–1658. 70 indexed citations
15.
Mukherjee, Shayantani, Sean M. Law, & Michael Feig. (2009). Deciphering the Mismatch Recognition Cycle in MutS and MSH2-MSH6 Using Normal-Mode Analysis. Biophysical Journal. 96(5). 1707–1720. 24 indexed citations
16.

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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