Vikas Nanda

5.3k total citations
120 papers, 4.2k citations indexed

About

Vikas Nanda is a scholar working on Molecular Biology, Biomaterials and Materials Chemistry. According to data from OpenAlex, Vikas Nanda has authored 120 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Molecular Biology, 33 papers in Biomaterials and 18 papers in Materials Chemistry. Recurrent topics in Vikas Nanda's work include Protein Structure and Dynamics (30 papers), Collagen: Extraction and Characterization (21 papers) and RNA and protein synthesis mechanisms (16 papers). Vikas Nanda is often cited by papers focused on Protein Structure and Dynamics (30 papers), Collagen: Extraction and Characterization (21 papers) and RNA and protein synthesis mechanisms (16 papers). Vikas Nanda collaborates with scholars based in United States, India and Israel. Vikas Nanda's co-authors include William F. DeGrado, Ronald L. Koder, Niti Kant, Douglas H. Pike, Fei Xu, Amanda L. Stouffer, James D. Lear, Paul G. Falkowski, Peter B. Law and Cinque Soto and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Vikas Nanda

117 papers receiving 4.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vikas Nanda United States 36 2.5k 866 571 383 342 120 4.2k
Cristiano L. P. Oliveira Brazil 36 3.5k 1.4× 732 0.8× 1.0k 1.8× 204 0.5× 555 1.6× 184 5.9k
Olivier Lambert France 39 2.8k 1.1× 636 0.7× 631 1.1× 269 0.7× 691 2.0× 123 4.7k
В. В. Волков Russia 23 3.7k 1.5× 327 0.4× 1.9k 3.3× 165 0.4× 351 1.0× 143 6.1k
James D. Lear United States 48 6.1k 2.4× 603 0.7× 848 1.5× 329 0.9× 621 1.8× 87 7.8k
Paula J. Booth United Kingdom 45 5.0k 2.0× 654 0.8× 444 0.8× 609 1.6× 386 1.1× 118 6.2k
Carol R. Flach United States 36 1.5k 0.6× 362 0.4× 579 1.0× 599 1.6× 642 1.9× 80 4.0k
Timothy R. Dafforn United Kingdom 50 5.0k 2.0× 574 0.7× 566 1.0× 363 0.9× 488 1.4× 152 7.4k
Kai Griebenow Puerto Rico 48 4.8k 1.9× 960 1.1× 982 1.7× 708 1.8× 502 1.5× 134 7.0k
Jörg Stetefeld Canada 34 3.2k 1.3× 542 0.6× 583 1.0× 122 0.3× 287 0.8× 118 5.4k
Liang Guo China 37 4.2k 1.7× 461 0.5× 753 1.3× 115 0.3× 552 1.6× 114 6.5k

Countries citing papers authored by Vikas Nanda

Since Specialization
Citations

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

Fields of papers citing papers by Vikas Nanda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vikas Nanda

This figure shows the co-authorship network connecting the top 25 collaborators of Vikas Nanda. A scholar is included among the top collaborators of Vikas Nanda 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 Vikas Nanda. Vikas Nanda 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.
Miller, Michelle, Joshua T. Atkinson, J. Dongun Kim, et al.. (2023). The energetics and evolution of oxidoreductases in deep time. Proteins Structure Function and Bioinformatics. 92(1). 52–59. 7 indexed citations
2.
Nanda, Vikas, et al.. (2023). Purification of recombinant bacterial collagens containing structural perturbations. PLoS ONE. 18(5). e0285864–e0285864. 3 indexed citations
3.
Nanda, Vikas, et al.. (2023). Designing collagens to shed light on the multi-scale structure–function mapping of matrix disorders. SHILAP Revista de lepidopterología. 21. 100139–100139. 5 indexed citations
4.
Batra, Rohit, Troy D. Loeffler, Henry Chan, et al.. (2022). Machine learning overcomes human bias in the discovery of self-assembling peptides. Nature Chemistry. 14(12). 1427–1435. 100 indexed citations
5.
Atkinson, Joshua T., et al.. (2019). Protein tolerance to random circular permutation correlates with thermostability and local energetics of residue-residue contacts. Protein Engineering Design and Selection. 32(11). 489–501. 8 indexed citations
6.
Upadhya, Rahul, N. Sanjeeva Murthy, Cody L. Hoop, et al.. (2019). PET-RAFT and SAXS: High Throughput Tools To Study Compactness and Flexibility of Single-Chain Polymer Nanoparticles. Macromolecules. 52(21). 8295–8304. 53 indexed citations
7.
Sanman, Laura E., et al.. (2018). Catalytic linkage between caspase activity and proteostasis in Archaea. Environmental Microbiology. 21(1). 286–298. 4 indexed citations
8.
Pike, Douglas H., et al.. (2018). Structural and Dynamic Properties of Allergen and Non-Allergen Forms of Tropomyosin. Structure. 26(7). 997–1006.e5. 30 indexed citations
9.
Nanda, Vikas, et al.. (2017). Mass Spectrometric Analysis of TRPM6 and TRPM7 Phosphorylation Reveals Regulatory Mechanisms of the Channel-Kinases. Scientific Reports. 7(1). 42739–42739. 25 indexed citations
10.
Parmar, Avanish Singh, et al.. (2016). Circular Dichroism Spectroscopy of Collagen Fibrillogenesis: A New Use for an Old Technique. Biophysical Journal. 111(11). 2377–2386. 85 indexed citations
11.
Matteson, Paul G., Alejandro Q. Nato, Yong Lin, et al.. (2015). The orphan GPCR, Gpr161, regulates the retinoic acid and canonical Wnt pathways during neurulation. Developmental Biology. 402(1). 17–31. 22 indexed citations
12.
Nanda, Vikas, et al.. (2015). Structural principles for computational and de novo design of 4Fe–4S metalloproteins. Biochimica et Biophysica Acta (BBA) - Bioenergetics. 1857(5). 531–538. 25 indexed citations
13.
Huang, Ling, Douglas H. Pike, David E. Sleat, Vikas Nanda, & Peter Lobel. (2014). Potential Pitfalls and Solutions for Use of Fluorescent Fusion Proteins to Study the Lysosome. PLoS ONE. 9(2). e88893–e88893. 47 indexed citations
14.
Nanda, Vikas, et al.. (2013). Hemoprotein Design using Minimal Sequence Information. Biophysical Journal. 104(2). 661a–661a. 1 indexed citations
15.
Koder, Ronald L., et al.. (2013). An Artificial Safranine Enzyme which Activates Chemotherapeutic Prodrugs. Biophysical Journal. 104(2). 205a–205a. 1 indexed citations
16.
Nanda, Vikas, et al.. (2013). Prediction and Design of Outer Membrane Protein–Protein Interactions. Methods in molecular biology. 1063. 183–196. 4 indexed citations
17.
Grzyb, Joanna, Fei Xu, Vikas Nanda, et al.. (2012). Empirical and computational design of iron-sulfur cluster proteins. Biochimica et Biophysica Acta (BBA) - Bioenergetics. 1817(8). 1256–1262. 33 indexed citations
18.
19.
Stouffer, Amanda L., Rudresh Acharya, David Salom, et al.. (2008). Structural basis for the function and inhibition of an influenza virus proton channel. Nature. 451(7178). 596–599. 491 indexed citations
20.
Duong‐Ly, Krisna C., Vikas Nanda, William F. DeGrado, & Kathleen P. Howard. (2005). The conformation of the pore region of the M2 proton channel depends on lipid bilayer environment. Protein Science. 14(4). 856–861. 87 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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