Mandar T. Naik

2.3k total citations · 1 hit paper
40 papers, 1.6k citations indexed

About

Mandar T. Naik is a scholar working on Molecular Biology, Biomedical Engineering and Biomaterials. According to data from OpenAlex, Mandar T. Naik has authored 40 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 6 papers in Biomedical Engineering and 4 papers in Biomaterials. Recurrent topics in Mandar T. Naik's work include Ubiquitin and proteasome pathways (7 papers), Lignin and Wood Chemistry (6 papers) and Biofuel production and bioconversion (5 papers). Mandar T. Naik is often cited by papers focused on Ubiquitin and proteasome pathways (7 papers), Lignin and Wood Chemistry (6 papers) and Biofuel production and bioconversion (5 papers). Mandar T. Naik collaborates with scholars based in United States, Taiwan and India. Mandar T. Naik's co-authors include Nicolas L. Fawzi, Veronica H. Ryan, Qiang Li, Joshua S. Yuan, Theodora Myrto Perdikari, Wilson K. Serem, Scott Watters, Anastasia C. Murthy, Shangxian Xie and Tai-huang Huang and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Mandar T. Naik

40 papers receiving 1.6k citations

Hit Papers

SARS‐CoV‐2 nucleocapsid protein phase‐separates with RNA ... 2020 2026 2022 2024 2020 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mandar T. Naik United States 19 967 327 162 148 113 40 1.6k
Feng Rao China 25 1.3k 1.3× 203 0.6× 43 0.3× 251 1.7× 159 1.4× 59 2.3k
Kenth Johansson Sweden 23 1.1k 1.1× 99 0.3× 64 0.4× 28 0.2× 245 2.2× 42 2.0k
Marzia Bedoni Italy 24 876 0.9× 438 1.3× 42 0.3× 50 0.3× 52 0.5× 61 1.6k
Dan Chen China 19 514 0.5× 155 0.5× 22 0.1× 55 0.4× 43 0.4× 63 1.2k
Claire Monge France 23 991 1.0× 372 1.1× 27 0.2× 340 2.3× 33 0.3× 37 1.9k
Anning Li China 24 763 0.8× 394 1.2× 43 0.3× 268 1.8× 44 0.4× 92 2.0k
Yijuan Zhang China 21 907 0.9× 288 0.9× 15 0.1× 253 1.7× 63 0.6× 53 1.7k
Christopher Lindsay United Kingdom 18 333 0.3× 346 1.1× 16 0.1× 131 0.9× 127 1.1× 44 1.1k
Lilian T. Costa Brazil 14 276 0.3× 233 0.7× 29 0.2× 81 0.5× 30 0.3× 22 782

Countries citing papers authored by Mandar T. Naik

Since Specialization
Citations

This map shows the geographic impact of Mandar T. Naik'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 Mandar T. Naik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mandar T. Naik more than expected).

Fields of papers citing papers by Mandar T. Naik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mandar T. Naik. 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 Mandar T. Naik. The network helps show where Mandar T. Naik may publish in the future.

Co-authorship network of co-authors of Mandar T. Naik

This figure shows the co-authorship network connecting the top 25 collaborators of Mandar T. Naik. A scholar is included among the top collaborators of Mandar T. Naik 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 Mandar T. Naik. Mandar T. Naik 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.
Mishra, Biswajit, Anindya Basu, Fadi Shehadeh, et al.. (2025). Antimicrobial peptide developed with machine learning sequence optimization targets drug resistant Staphylococcus aureus in mice. Journal of Clinical Investigation. 135(12). 4 indexed citations
2.
Kavathekar, Maithili, Bansidhar Tarai, Mandar T. Naik, et al.. (2025). PathCrisp: an innovative molecular diagnostic tool for early detection of NDM-resistant infections. Scientific Reports. 15(1). 490–490. 1 indexed citations
3.
Yang, Xin, Mengia S. Rioult-Pedotti, Kim M. Hansen, et al.. (2024). Peptidomimetic inhibitors targeting TrkB/PSD-95 signaling improves cognition and seizure outcomes in an Angelman Syndrome mouse model. Neuropsychopharmacology. 50(5). 772–782. 2 indexed citations
5.
Shi, Xin, Xiaozhong Zhou, Gang Chen, et al.. (2024). Targeting the postsynaptic scaffolding protein PSD-95 enhances BDNF signaling to mitigate depression-like behaviors in mice. Science Signaling. 17(834). eadn4556–eadn4556. 19 indexed citations
6.
Clark, Nathaniel E., Adam Katolik, E. Murphy, et al.. (2023). Activation of human RNA lariat debranching enzyme Dbr1 by binding protein TTDN1 occurs though an intrinsically disordered C-terminal domain. Journal of Biological Chemistry. 299(9). 105100–105100. 3 indexed citations
7.
Qiu, Chenxi, et al.. (2023). Thiolutin has complex effects in vivo but is a direct inhibitor of RNA polymerase II in vitro. Nucleic Acids Research. 52(5). 2546–2564. 8 indexed citations
8.
Li, Qiang, Daxian Cao, Mandar T. Naik, et al.. (2022). Molecular Engineering of Biorefining Lignin Waste for Solid-State Electrolyte. ACS Sustainable Chemistry & Engineering. 10(27). 8704–8714. 25 indexed citations
9.
Li, Qiang, Cheng Hu, Mengjie Li, et al.. (2021). Enhancing the multi-functional properties of renewable lignin carbon fibers via defining the structure–property relationship using different biomass feedstocks. Green Chemistry. 23(10). 3725–3739. 52 indexed citations
10.
Newton, Jocelyn C., Mandar T. Naik, E. Murphy, et al.. (2021). Phase separation of the LINE-1 ORF1 protein is mediated by the N-terminus and coiled-coil domain. Biophysical Journal. 120(11). 2181–2191. 32 indexed citations
11.
Patel, Jagdish Suresh, C. Scott Wylie, F. Marty Ytreberg, et al.. (2020). Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness. PLoS ONE. 15(5). e0233509–e0233509. 18 indexed citations
12.
Li, Qiang, Cheng Hu, Mengjie Li, et al.. (2020). Discovering Biomass Structural Determinants Defining the Properties of Plant-Derived Renewable Carbon Fiber. iScience. 23(8). 101405–101405. 18 indexed citations
13.
Soma, Shivatheja, Mandar T. Naik, Aren Boulet, et al.. (2019). COA6 Is Structurally Tuned to Function as a Thiol-Disulfide Oxidoreductase in Copper Delivery to Mitochondrial Cytochrome c Oxidase. Cell Reports. 29(12). 4114–4126.e5. 43 indexed citations
14.
Wang, Ailin, Alexander E. Conicella, Hermann Broder Schmidt, et al.. (2018). A single N‐terminal phosphomimic disrupts TDP‐43 polymerization, phase separation, and RNA splicing. The EMBO Journal. 37(5). 316 indexed citations
15.
Naik, Mandar T., et al.. (2017). Isolation and Characterization of Cyclic C33 Botryococcenes and a Trimethylsqualene Isomer from Botryococcus braunii Race B. Journal of Natural Products. 80(4). 953–958. 3 indexed citations
16.
Chang, Chi‐Fon, et al.. (2015). The RING domain of human promyelocytic leukemia protein (PML). Journal of Biomolecular NMR. 61(2). 173–180. 5 indexed citations
17.
Naik, Mandar T., et al.. (2014). The B-box 1 dimer of human promyelocytic leukemia protein. Journal of Biomolecular NMR. 60(4). 275–281. 12 indexed citations
18.
Naik, Mandar T., et al.. (2010). NMR chemical shift assignments of a complex between SUMO-1 and SIM peptide derived from the C-terminus of Daxx. Biomolecular NMR Assignments. 5(1). 75–77. 2 indexed citations
19.
Naik, Mandar T., Nuttee Suree, Udayar Ilangovan, et al.. (2005). Staphylococcus aureus Sortase A Transpeptidase. Journal of Biological Chemistry. 281(3). 1817–1826. 86 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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