Olivier Lichtarge

18.3k total citations · 3 hit papers
165 papers, 8.6k citations indexed

About

Olivier Lichtarge is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Olivier Lichtarge has authored 165 papers receiving a total of 8.6k indexed citations (citations by other indexed papers that have themselves been cited), including 143 papers in Molecular Biology, 30 papers in Genetics and 20 papers in Cellular and Molecular Neuroscience. Recurrent topics in Olivier Lichtarge's work include Protein Structure and Dynamics (37 papers), Receptor Mechanisms and Signaling (31 papers) and Bioinformatics and Genomic Networks (30 papers). Olivier Lichtarge is often cited by papers focused on Protein Structure and Dynamics (37 papers), Receptor Mechanisms and Signaling (31 papers) and Bioinformatics and Genomic Networks (30 papers). Olivier Lichtarge collaborates with scholars based in United States, Canada and France. Olivier Lichtarge's co-authors include Henry R. Bourne, Fred E. Cohen, Ivana Mihalek, Mathew E. Sowa, Panagiotis Katsonis, I. Reš, Srinivasan Madabushi, Angela D. Wilkins, Theodore G. Wensel and Søren P. Sheikh and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Olivier Lichtarge

165 papers receiving 8.5k 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.

An Evolutionary Trace Method Defines Binding Surfaces Common to Protein Families

1996 977 citations Olivier Lichtarge, Fred E. Cohen et al. profile →
β-Arrestin-dependent, G Protein-independent ERK1/2 Activation by the β2 Adrenergic Receptor

2005 616 citations Srinivasan Madabushi, Olivier Lichtarge et al. Journal of Biological Chemistry profile →
Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas

2019 421 citations Lawrence A. Donehower, Teng‐Kuei Hsu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Olivier Lichtarge United States	46	7.1k	1.6k	1.3k	812	789	165	8.6k
Rama Ranganathan United States	41	7.2k 1.0×	1.6k 1.0×	1.3k 1.0×	1.0k 1.3×	600 0.8×	91	9.0k
M. Madan Babu United Kingdom	57	11.9k 1.7×	2.1k 1.3×	1.4k 1.0×	1.2k 1.5×	587 0.7×	133	14.0k
Jian Jin United States	55	8.7k 1.2×	1.1k 0.7×	583 0.4×	322 0.4×	483 0.6×	267	10.7k
Marc A. Martı́-Renom Spain	45	11.5k 1.6×	1.0k 0.6×	1.7k 1.3×	1.6k 2.0×	980 1.2×	123	15.4k
Hongyi Zhou United States	42	6.0k 0.9×	943 0.6×	546 0.4×	1.3k 1.6×	756 1.0×	145	8.2k
Igor F. Tsigelny United States	42	3.0k 0.4×	658 0.4×	399 0.3×	408 0.5×	514 0.7×	166	5.4k
James Inglese United States	59	9.9k 1.4×	2.9k 1.8×	602 0.5×	468 0.6×	1.8k 2.3×	192	13.8k
Miguel A. Andrade‐Navarro Germany	60	11.9k 1.7×	904 0.6×	1.3k 1.0×	467 0.6×	521 0.7×	268	15.3k
Boris Ν. Kholodenko United States	53	9.4k 1.3×	664 0.4×	630 0.5×	228 0.3×	954 1.2×	217	11.5k
Avner Schlessinger United States	41	4.2k 0.6×	392 0.2×	551 0.4×	658 0.8×	543 0.7×	110	6.0k

Countries citing papers authored by Olivier Lichtarge

Since Specialization

Citations

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

Fields of papers citing papers by Olivier Lichtarge

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Olivier Lichtarge

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chinnam, Naga Babu, Roopa Thapar, Altaf H. Sarker, et al.. (2024). ASCC1 structures and bioinformatics reveal a novel helix-clasp-helix RNA-binding motif linked to a two-histidine phosphodiesterase. Journal of Biological Chemistry. 300(6). 107368–107368. 4 indexed citations

Bourquard, Thomas, Kwanghyuk Lee, Ismael Al‐Ramahi, et al.. (2023). Functional variants identify sex-specific genes and pathways in Alzheimer’s Disease. Nature Communications. 14(1). 2765–2765. 11 indexed citations

Lee, Kwanghyuk, et al.. (2023). Evolutionary Action–Machine Learning Model Identifies Candidate Genes Associated With Early‐Onset Coronary Artery Disease. Journal of the American Heart Association. 12(17). e029103–e029103. 5 indexed citations

Marciano, David C., Chen Wang, Teng‐Kuei Hsu, et al.. (2022). Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli. Nature Communications. 13(1). 22 indexed citations

Hsu, Teng‐Kuei, Amanda Koire, Byung‐Kwon Choi, et al.. (2022). A general calculus of fitness landscapes finds genes under selection in cancers. Genome Research. 32(5). 916–929. 7 indexed citations

Wang, Chen, David C. Marciano, Amanda M. Williams, et al.. (2021). Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and greater coronavirus family. Bioinformatics. 37(22). 4033–4040. 7 indexed citations

Sharma, Amit, Tikam Chand Dakal, Hongde Liu, et al.. (2021). PPAR-Responsive Elements Enriched with Alu Repeats May Contribute to Distinctive PPARγ–DNMT1 Interactions in the Genome. Cancers. 13(16). 3993–3993. 4 indexed citations

Sharma, Amit, Arijit Biswas, Hongde Liu, et al.. (2019). Mutational Landscape of the BAP1 Locus Reveals an Intrinsic Control to Regulate the miRNA Network and the Binding of Protein Complexes in Uveal Melanoma. Cancers. 11(10). 1600–1600. 31 indexed citations

Clarke, Callisia N., Panagiotis Katsonis, Amanda Koire, et al.. (2018). Comprehensive Genomic Characterization of Parathyroid Cancer Identifies Novel Candidate Driver Mutations and Core Pathways. Journal of the Endocrine Society. 3(3). 544–559. 47 indexed citations

10.

Wilkins, Angela D., et al.. (2016). Intramolecular allosteric communication in dopamine D2 receptor revealed by evolutionary amino acid covariation. Proceedings of the National Academy of Sciences. 113(13). 3539–3544. 29 indexed citations

11.

Osman, Abdullah A., Marcus M. Monroe, Marcus V. Ortega Alves, et al.. (2014). Wee-1 Kinase Inhibition Overcomes Cisplatin Resistance Associated with High-Risk TP53 Mutations in Head and Neck Cancer through Mitotic Arrest Followed by Senescence. Molecular Cancer Therapeutics. 14(2). 608–619. 88 indexed citations

12.

Erdin, Serkan, et al.. (2013). Prediction and experimental validation of enzyme substrate specificity in protein structures. Proceedings of the National Academy of Sciences. 110(45). E4195–202. 31 indexed citations

13.

Rodríguez, Gustavo, et al.. (2010). Evolution-guided discovery and recoding of allosteric pathway specificity determinants in psychoactive bioamine receptors. Proceedings of the National Academy of Sciences. 107(17). 7787–7792. 81 indexed citations

14.

Yao, Hui, Ivana Mihalek, & Olivier Lichtarge. (2006). Rank information: A structure‐independent measure of evolutionary trace quality that improves identification of protein functional sites. Proteins Structure Function and Bioinformatics. 65(1). 111–123. 17 indexed citations

15.

Raviscioni, Michele, et al.. (2006). Evolutionary identification of a subtype specific functional site in the ligand binding domain of steroid receptors. Proteins Structure Function and Bioinformatics. 64(4). 1046–1057. 19 indexed citations

16.

Quan, Xiao‐Jiang, Tinneke Denayer, Jiekun Yan, et al.. (2004). Evolution of neural precursor selection: functional divergence of proneural proteins. Development. 131(8). 1679–1689. 50 indexed citations

17.

Lin, Chin‐Yu, et al.. (2003). Conserved Motifs in Somatostatin, D2-dopamine, and α2B-Adrenergic Receptors for Inhibiting the Na-H Exchanger, NHE1. Journal of Biological Chemistry. 278(17). 15128–15135. 25 indexed citations

18.

Dunbrack, Roland L., Dietlind L. Gerloff, Michael J. Bower, et al.. (1997). Meeting review: the Second Meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP2), Asilomar, California, December 13–16, 1996. PubMed. 2(2). R27–R42. 44 indexed citations

19.

Lichtarge, Olivier, Craig Cornelius, Bruce G. Buchanan, & Oleg Jardetzky. (1987). Validation of the first step of the heuristic refinement method for the derivation of solution structures of proteins from NMR data. Proteins Structure Function and Bioinformatics. 2(4). 340–358. 15 indexed citations

20.

Hayes‐Roth, Barbara, Bruce G. Buchanan, Olivier Lichtarge, et al.. (1986). Protean: deriving protein structure from constraints. National Conference on Artificial Intelligence. 904–909. 44 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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