Shashank Deep

1.8k total citations
76 papers, 1.4k citations indexed

About

Shashank Deep is a scholar working on Molecular Biology, Physiology and Materials Chemistry. According to data from OpenAlex, Shashank Deep has authored 76 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Molecular Biology, 16 papers in Physiology and 14 papers in Materials Chemistry. Recurrent topics in Shashank Deep's work include Protein Structure and Dynamics (18 papers), Alzheimer's disease research and treatments (14 papers) and Enzyme Structure and Function (11 papers). Shashank Deep is often cited by papers focused on Protein Structure and Dynamics (18 papers), Alzheimer's disease research and treatments (14 papers) and Enzyme Structure and Function (11 papers). Shashank Deep collaborates with scholars based in India, United States and Sweden. Shashank Deep's co-authors include J. C. Ahluwalia, Nidhi Kaur Bhatia, Andrew P. Hinck, Zhanyong Shu, Cynthia S. Hinck, Alexander B. Taylor, P. John Hart, Anurag Sharma, Shyam Kishor and Shahid M. Nayeem and has published in prestigious journals such as Journal of Biological Chemistry, PLoS ONE and Journal of Molecular Biology.

In The Last Decade

Shashank Deep

72 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shashank Deep India 19 796 248 184 171 166 76 1.4k
Nicola D’Amelio Italy 20 526 0.7× 127 0.5× 155 0.8× 158 0.9× 90 0.5× 57 1.1k
Rajesh K. Grover India 17 702 0.9× 367 1.5× 167 0.9× 61 0.4× 91 0.5× 49 1.5k
Basir Ahmad India 26 1.5k 1.9× 410 1.7× 298 1.6× 121 0.7× 293 1.8× 67 2.0k
Arun Upadhyay India 23 971 1.2× 192 0.8× 201 1.1× 53 0.3× 150 0.9× 50 1.9k
Anna I. Sulatskaya Russia 20 848 1.1× 703 2.8× 101 0.5× 63 0.4× 238 1.4× 62 1.5k
Clinton R. Nishida United States 23 745 0.9× 382 1.5× 127 0.7× 106 0.6× 272 1.6× 31 1.9k
Masihuz Zaman India 23 1.3k 1.6× 533 2.1× 205 1.1× 45 0.3× 170 1.0× 52 1.7k
Ana M. Damas Portugal 30 1.5k 1.9× 613 2.5× 212 1.2× 50 0.3× 315 1.9× 85 2.3k
Zhao‐Min Lin China 24 562 0.7× 113 0.5× 136 0.7× 36 0.2× 218 1.3× 90 1.5k
Miquel Adrover Spain 22 543 0.7× 245 1.0× 178 1.0× 133 0.8× 171 1.0× 62 1.2k

Countries citing papers authored by Shashank Deep

Since Specialization
Citations

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

Fields of papers citing papers by Shashank Deep

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shashank Deep

This figure shows the co-authorship network connecting the top 25 collaborators of Shashank Deep. A scholar is included among the top collaborators of Shashank Deep 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 Shashank Deep. Shashank Deep 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
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Srivastava, Sukriti, Shashank Deep, & Sunil Kumar Khare. (2025). Mechanistic insights into Aβ42 aggregation inhibition by bacoside A and withanolide A: An in silico and in vitro approach. International Journal of Biological Macromolecules. 315(Pt 1). 144315–144315. 2 indexed citations
3.
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Bose, Pritha, Subodh Kumar, Nikhil Kumar, et al.. (2024). Tuned Manganese-Impregnated Mesoporous Silica Nanoparticles as a pH-Responsive Dual Imaging Probe. ACS Applied Bio Materials. 7(12). 8503–8516. 2 indexed citations
5.
Kumar, Nikhil, et al.. (2023). Binding studies of potential amyloid-β inhibiting chalcone derivative with bovine serum albumin. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 305. 123362–123362. 5 indexed citations
6.
Deep, Shashank, et al.. (2023). Mechanism of the interaction of toxic SOD1 fibrils with two potent polyphenols: curcumin and quercetin. Physical Chemistry Chemical Physics. 25(34). 23081–23091. 5 indexed citations
7.
Jahan, Ishrat, Aziz Ahmad, & Shashank Deep. (2023). Effect of flavonoids on the destabilization of α-synuclein fibrils and their conversion to amorphous aggregate: A molecular dynamics simulation and experimental study. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1871(6). 140951–140951. 7 indexed citations
8.
Deep, Shashank, et al.. (2022). Role of molecular and chemical chaperon in assisting refolding of BMP2 in E. coli. International Journal of Biological Macromolecules. 220. 204–210. 5 indexed citations
9.
Sharma, Gargi, et al.. (2021). Kinetics theories to understand the mechanism of aggregation of a protein and to design strategies for its inhibition. Biophysical Chemistry. 278. 106665–106665. 32 indexed citations
10.
Sharma, Shilpa & Shashank Deep. (2020). In-silico drug repurposing for targeting SARS-CoV-2 main protease (M pro ). Journal of Biomolecular Structure and Dynamics. 40(7). 3003–3010. 27 indexed citations
11.
Deep, Shashank, et al.. (2020). Thinking beyond tradition: Polyphenols as effective refolding modulators. International Journal of Biological Macromolecules. 148. 969–978. 5 indexed citations
12.
Weininger, Ulrich, et al.. (2019). Adsorption of unfolded Cu/Zn superoxide dismutase onto hydrophobic surfaces catalyzes its formation of amyloid fibrils. Protein Engineering Design and Selection. 32(2). 77–85. 1 indexed citations
13.
Jayaraj, Abhilash, Vinay Kumar, Brian S. J. Blagg, et al.. (2019). Stimulation of heat shock protein 90 chaperone function through binding of a novobiocin analog KU-32. Journal of Biological Chemistry. 294(16). 6450–6467. 11 indexed citations
14.
Respondek, Michal, et al.. (2017). Cu/Zn Superoxide Dismutase Forms Amyloid Fibrils under Near-Physiological Quiescent Conditions: The Roles of Disulfide Bonds and Effects of Denaturant. ACS Chemical Neuroscience. 8(9). 2019–2026. 23 indexed citations
15.
Gupta, Preeti & Shashank Deep. (2014). Intermediate conformation between native β-sheet and non-native α-helix is a precursor of trifluoroethanol-induced aggregation of Human Carbonic Anhydrase-II. Biochemical and Biophysical Research Communications. 449(1). 126–131. 9 indexed citations
16.
Bhatia, Nidhi Kaur & Shashank Deep. (2013). DIAGNOSTIC TOOLS FOR STRUCTURAL CHARACTERIZATION AND ELUCIDATION OF FIBRILS AND THEIR PRECURSORS IN AMYLOID FIBRIL FORMATION PATHWAY. Journal of Proteins and Proteomics. 4(2). 2 indexed citations
17.
Deep, Shashank, et al.. (2013). MODELS OF PROTEIN FOLDING. Journal of Proteins and Proteomics. 3(2). 5 indexed citations
18.
Deep, Shashank & J. C. Ahluwalia. (2003). Theoretical studies on solvation contribution to the thermodynamic stability of mutants of lysozyme T4. Protein Engineering Design and Selection. 16(6). 415–422. 2 indexed citations
19.
Hart, Peter ‘t, et al.. (2002). CRYSTAL STRUCTURE OF THE HUMAN TBR2 ECTODOMAIN-TGF-B3 COMPLEX. Nature Structural & Molecular Biology. 9(3). 14 indexed citations
20.
Deep, Shashank & J. C. Ahluwalia. (2002). Heat capacity of folding of proteins corrected for disulfide cross-links. Biophysical Chemistry. 97(1). 73–77. 2 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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