Joan Teyra

1.7k total citations
33 papers, 927 citations indexed

About

Joan Teyra is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Joan Teyra has authored 33 papers receiving a total of 927 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Molecular Biology, 8 papers in Materials Chemistry and 5 papers in Computational Theory and Mathematics. Recurrent topics in Joan Teyra's work include Protein Structure and Dynamics (14 papers), Ubiquitin and proteasome pathways (9 papers) and Enzyme Structure and Function (8 papers). Joan Teyra is often cited by papers focused on Protein Structure and Dynamics (14 papers), Ubiquitin and proteasome pathways (9 papers) and Enzyme Structure and Function (8 papers). Joan Teyra collaborates with scholars based in Canada, Germany and United States. Joan Teyra's co-authors include M. Teresa Pisabarro, Philip M. Kim, Sachdev S. Sidhu, Sergey A. Samsonov, Jason Moffat, Recep Çolak, Satra Nim, Jouhyun Jeon, Alessandro Datti and Jeffrey L. Wrana and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Bioinformatics.

In The Last Decade

Joan Teyra

32 papers receiving 921 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joan Teyra Canada 18 788 147 145 124 91 33 927
Mainak Guharoy Belgium 18 1.0k 1.3× 132 0.9× 227 1.6× 113 0.9× 68 0.7× 26 1.2k
Krishnadev Oruganty United States 15 698 0.9× 132 0.9× 105 0.7× 117 0.9× 63 0.7× 22 823
Christina Schindler Germany 15 676 0.9× 146 1.0× 149 1.0× 167 1.3× 47 0.5× 25 822
Benoît H. Dessailly United Kingdom 16 1.0k 1.3× 80 0.5× 233 1.6× 120 1.0× 63 0.7× 18 1.2k
Mohammad T. Mazhab‐Jafari Canada 16 942 1.2× 150 1.0× 132 0.9× 90 0.7× 88 1.0× 32 1.1k
Mert Gür Türkiye 15 731 0.9× 116 0.8× 141 1.0× 107 0.9× 36 0.4× 36 955
Sonja A. Dames Germany 16 967 1.2× 130 0.9× 137 0.9× 77 0.6× 113 1.2× 35 1.2k
Oleksandr Buzko United States 9 643 0.8× 93 0.6× 72 0.5× 82 0.7× 98 1.1× 9 861
Stefan Henrich Germany 13 793 1.0× 189 1.3× 131 0.9× 182 1.5× 175 1.9× 20 1.3k
Johan Schultz Sweden 17 873 1.1× 171 1.2× 81 0.6× 77 0.6× 188 2.1× 37 1.1k

Countries citing papers authored by Joan Teyra

Since Specialization
Citations

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

Fields of papers citing papers by Joan Teyra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joan Teyra

This figure shows the co-authorship network connecting the top 25 collaborators of Joan Teyra. A scholar is included among the top collaborators of Joan Teyra 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 Joan Teyra. Joan Teyra 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.
Tessier, Tanner M., Joan Teyra, Nick Jarvik, et al.. (2023). Structural and functional validation of a highly specific Smurf2 inhibitor. Protein Science. 33(2). e4885–e4885. 1 indexed citations
2.
Teyra, Joan, Shane Miersch, Lia Cardarelli, et al.. (2022). Development of Monoclonal Antibodies to Detect for SARS-CoV-2 Proteins. Journal of Molecular Biology. 434(10). 167583–167583. 4 indexed citations
3.
Chrustowicz, Jakub, Dawafuti Sherpa, Joan Teyra, et al.. (2021). Multifaceted N-Degron Recognition and Ubiquitylation by GID/CTLH E3 Ligases. Journal of Molecular Biology. 434(2). 167347–167347. 22 indexed citations
4.
Teyra, Joan, M. A. McLaughlin, Alex U. Singer, et al.. (2021). Comprehensive Assessment of the Relationship Between Site−2 Specificity and Helix α2 in the Erbin PDZ Domain. Journal of Molecular Biology. 433(18). 167115–167115.
5.
Middleton, A.J., Joan Teyra, Jingyi Zhu, Sachdev S. Sidhu, & Catherine L. Day. (2021). Identification of Ubiquitin Variants That Inhibit the E2 Ubiquitin Conjugating Enzyme, Ube2k. ACS Chemical Biology. 16(9). 1745–1756. 12 indexed citations
6.
Teyra, Joan, Andreas Ernst, Alex U. Singer, Frank Sicheri, & Sachdev S. Sidhu. (2019). Comprehensive analysis of all evolutionary paths between two divergent PDZ domain specificities. Protein Science. 29(2). 433–442. 8 indexed citations
7.
Stiffler, Michael A., Frank J. Poelwijk, Kelly P. Brock, et al.. (2019). Protein Structure from Experimental Evolution. Cell Systems. 10(1). 15–24.e5. 25 indexed citations
8.
Seetharaman, Ashwin, Joan Teyra, Taras Makhnevych, et al.. (2019). Yeast Two-Hybrid Analysis for Ubiquitin Variant Inhibitors of Human Deubiquitinases. Journal of Molecular Biology. 431(6). 1160–1171. 5 indexed citations
9.
Veggiani, Gianluca, et al.. (2019). The ubiquitin interacting motifs of USP37 act on the proximal Ub of a di-Ub chain to enhance catalytic efficiency. Scientific Reports. 9(1). 4119–4119. 11 indexed citations
10.
Strokach, Alexey, Carles Corbi‐Verge, Joan Teyra, & Philip M. Kim. (2018). Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions. Methods in molecular biology. 1851. 1–17. 13 indexed citations
11.
Teyra, Joan, Haiming Huang, Shobhit Jain, et al.. (2017). Comprehensive Analysis of the Human SH3 Domain Family Reveals a Wide Variety of Non-canonical Specificities. Structure. 25(10). 1598–1610.e3. 96 indexed citations
12.
Hershey, David M., Patrick Browne, Anthony T. Iavarone, et al.. (2016). Magnetite Biomineralization in Magnetospirillum magneticum Is Regulated by a Switch-like Behavior in the HtrA Protease MamE. Journal of Biological Chemistry. 291(34). 17941–17952. 19 indexed citations
13.
Çolak, Recep, Joan Teyra, Carles Corbi‐Verge, et al.. (2015). Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins. Cell Reports. 12(2). 183–189. 18 indexed citations
14.
Jeon, Jouhyun, Satra Nim, Joan Teyra, et al.. (2014). A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Medicine. 6(7). 57–57. 110 indexed citations
15.
16.
Hawkins, John, Hongbo Zhu, Joan Teyra, & M. Teresa Pisabarro. (2012). Reduced False Positives in PDZ Binding Prediction Using Sequence and Structural Descriptors. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(5). 1492–1503. 3 indexed citations
17.
Samsonov, Sergey A., Joan Teyra, & M. Teresa Pisabarro. (2011). Docking glycosaminoglycans to proteins: analysis of solvent inclusion. Journal of Computer-Aided Molecular Design. 25(5). 477–489. 64 indexed citations
18.
Samsonov, Sergey A., et al.. (2009). Analysis of the impact of solvent on contacts prediction in proteins. BMC Structural Biology. 9(1). 22–22. 10 indexed citations
19.
Teyra, Joan, et al.. (2008). SCOWLP classification: Structural comparison and analysis of protein binding regions. BMC Bioinformatics. 9(1). 9–9. 37 indexed citations
20.
Bonet, Jaume, Gianluigi Caltabiano, Michael A. Johnston, et al.. (2005). The role of residue stability in transient protein–protein interactions involved in enzymatic phosphate hydrolysis. A computational study. Proteins Structure Function and Bioinformatics. 63(1). 65–77. 11 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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