Alejandro D. Nadra

2.0k total citations
44 papers, 1.2k citations indexed

About

Alejandro D. Nadra is a scholar working on Molecular Biology, Cell Biology and Genetics. According to data from OpenAlex, Alejandro D. Nadra has authored 44 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 9 papers in Cell Biology and 5 papers in Genetics. Recurrent topics in Alejandro D. Nadra's work include Protein Structure and Dynamics (11 papers), Hemoglobin structure and function (9 papers) and RNA and protein synthesis mechanisms (7 papers). Alejandro D. Nadra is often cited by papers focused on Protein Structure and Dynamics (11 papers), Hemoglobin structure and function (9 papers) and RNA and protein synthesis mechanisms (7 papers). Alejandro D. Nadra collaborates with scholars based in Argentina, Italy and Spain. Alejandro D. Nadra's co-authors include Darío A. Estrı́n, Marcelo A. Martí, G. Gay, Silvia M. Velasquez, José M. Estevez, Luciana Capece, Ariel A. Aptekmann, Andreu Alibés, Diego U. Ferreiro and Leonardo G. Alonso and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Alejandro D. Nadra

42 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alejandro D. Nadra Argentina 20 853 422 214 119 67 44 1.2k
Hiro Nakamura Japan 21 640 0.8× 144 0.3× 294 1.4× 158 1.3× 61 0.9× 47 1.1k
Hideaki Shiraishi Japan 17 842 1.0× 330 0.8× 135 0.6× 115 1.0× 16 0.2× 63 1.2k
Patrice Hamel United States 24 1.4k 1.6× 208 0.5× 187 0.9× 96 0.8× 45 0.7× 41 1.6k
Yuval Blat United States 17 1.2k 1.4× 262 0.6× 317 1.5× 262 2.2× 24 0.4× 26 1.6k
Chin‐Hwa Hu Taiwan 23 597 0.7× 92 0.2× 261 1.2× 142 1.2× 23 0.3× 59 1.5k
Deqiang Yao China 16 512 0.6× 137 0.3× 99 0.5× 58 0.5× 48 0.7× 40 817
Paola Dominici Italy 25 1.2k 1.4× 660 1.6× 403 1.9× 49 0.4× 36 0.5× 74 2.0k
Rob Horsefield Sweden 9 967 1.1× 200 0.5× 145 0.7× 131 1.1× 32 0.5× 11 1.4k
Juan M. Capasso United States 19 777 0.9× 92 0.2× 288 1.3× 52 0.4× 90 1.3× 35 1.2k
Michael S. Bereman United States 23 987 1.2× 114 0.3× 103 0.5× 39 0.3× 35 0.5× 57 1.7k

Countries citing papers authored by Alejandro D. Nadra

Since Specialization
Citations

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

Fields of papers citing papers by Alejandro D. Nadra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alejandro D. Nadra. 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 Alejandro D. Nadra. The network helps show where Alejandro D. Nadra may publish in the future.

Co-authorship network of co-authors of Alejandro D. Nadra

This figure shows the co-authorship network connecting the top 25 collaborators of Alejandro D. Nadra. A scholar is included among the top collaborators of Alejandro D. Nadra 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 Alejandro D. Nadra. Alejandro D. Nadra 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.
2.
Nadra, Alejandro D., et al.. (2023). From in-silico screening to in-vitro evaluation: Enhancing the detection of Microcystins with engineered PP1 mutant variants. Journal of Structural Biology. 215(4). 108043–108043.
3.
Noseda, Diego G., Cecilia D’Alessio, Javier Santos, et al.. (2023). Development of a Cost-Effective Process for the Heterologous Production of SARS-CoV-2 Spike Receptor Binding Domain Using Pichia pastoris in Stirred-Tank Bioreactor. Fermentation. 9(6). 497–497. 1 indexed citations
4.
Ferrero, Lucía, Ariel A. Aptekmann, Eliana Marzol, et al.. (2023). Transcription factor NAC1 activates expression of peptidase-encoding AtCEPs in roots to limit root hair growth. PLANT PHYSIOLOGY. 194(1). 81–93. 4 indexed citations
5.
González, María Carolina, et al.. (2022). Optimization and validation of a protein phosphatase inhibition assay for accessible microcystin detection. Talanta. 255. 124174–124174. 6 indexed citations
6.
Aptekmann, Ariel A., et al.. (2022). Transcription factor specificity limits the number of DNA-binding motifs. PLoS ONE. 17(1). e0263307–e0263307. 5 indexed citations
7.
Simonetti, Leandro, et al.. (2020). An update on genetic variants of theNKX2‐5. Human Mutation. 41(7). 1187–1208. 8 indexed citations
8.
Herrera, María Georgina, Martín E. Noguera, Liliana Daín, et al.. (2019). Frataxin Structure and Function. Sub-cellular biochemistry. 93. 393–438. 21 indexed citations
9.
Aptekmann, Ariel A. & Alejandro D. Nadra. (2018). Core promoter information content correlates with optimal growth temperature. Scientific Reports. 8(1). 1313–1313. 6 indexed citations
10.
Marasco, Luciano E., Inés Lucía Patop, Benjamin Basanta, et al.. (2017). Design and evaluation of an incoherent feed-forward loop for an arsenic biosensor based on standard iGEM parts. PubMed. 2(1). ysx006–ysx006. 15 indexed citations
11.
Mangano, Silvina, Hee-Seung Choi, Eliana Marzol, et al.. (2017). Molecular link between auxin and ROS-mediated polar growth. Proceedings of the National Academy of Sciences. 114(20). 5289–5294. 198 indexed citations
12.
Nadra, Alejandro D., et al.. (2016). Diseño, implementación, re-diseño, re-implementación, ..., de un biosensor. 921–925. 1 indexed citations
13.
Nadra, Alejandro D.. (2015). SensAr: producto innovador, experiencia excepcional. Americanae (AECID Library). 14(1). 11–17. 1 indexed citations
14.
Giordano, Daniela, Stefania Abbruzzetti, Francesco Nicoletti, et al.. (2012). Biophysical Characterisation of Neuroglobin of the Icefish, a Natural Knockout for Hemoglobin and Myoglobin. Comparison with Human Neuroglobin. PLoS ONE. 7(12). e44508–e44508. 36 indexed citations
15.
Velasquez, Silvia M., Martiniano M. Ricardi, Paula Virginia Fernández, et al.. (2011). O-Glycosylated Cell Wall Proteins Are Essential in Root Hair Growth. Science. 332(6036). 1401–1403. 239 indexed citations
16.
Alibés, Andreu, Alejandro D. Nadra, Federico De Masi, et al.. (2010). Using protein design algorithms to understand the molecular basis of disease caused by protein–DNA interactions: the Pax6 example. Nucleic Acids Research. 38(21). 7422–7431. 50 indexed citations
17.
Alibés, Andreu, Luís Serrano, & Alejandro D. Nadra. (2010). Structure-Based DNA-Binding Prediction and Design. Methods in molecular biology. 649. 77–88. 11 indexed citations
18.
Bikiel, Damián E., Leonardo Boechi, Luciana Capece, et al.. (2006). Modeling heme proteins using atomistic simulations. Physical Chemistry Chemical Physics. 8(48). 5611–5628. 68 indexed citations
19.
Ferreiro, Diego U., Mariano Dellarole, Alejandro D. Nadra, & G. Gay. (2005). Free Energy Contributions to Direct Readout of a DNA Sequence. Journal of Biological Chemistry. 280(37). 32480–32484. 29 indexed citations
20.
Ferreiro, Diego U., Luís Maurício T. R. Lima, Alejandro D. Nadra, et al.. (2000). Distinctive Cognate Sequence Discrimination, Bound DNA Conformation, and Binding Modes in the E2 C-Terminal Domains from Prototype Human and Bovine Papillomaviruses. Biochemistry. 39(47). 14692–14701. 30 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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