Puneet Rawat

• Rawat, P., Prabakaran, R., Kumar, S., & Gromiha, M. M. (2020). AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation. Bioinformatics, 36(5), 1439-1444.
• Rawat, P., Prabakaran, R., Sakthivel, R., Mary Thangakani, A., Kumar, S., & Gromiha, M. M. (2020). CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides. Amyloid, 27(2), 128-133.
• Rawat, P., Prabakaran, R., Kumar, S., & Gromiha, M. M. (2021). Exploring the sequence features determining amyloidosis in human antibody light chains. Scientific Reports, 11(1), 1-11.
• Rawat, P., Jemimah, S., Ponnuswamy, P. K., & Gromiha, M. M. (2021). Why are ACE2 binding coronavirus strains SARS‐CoV/SARS‐CoV‐2 wild and NL63 mild?. Proteins: Structure, Function, and Bioinformatics, 89(4), 389-398.
• Prabakaran, R., Rawat, P., Kumar, S., & Gromiha, M. M. (2021). Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. Briefings in bioinformatics.

• Sørensen, Christoffer V.; Hofmann, Nils; Rawat, Puneet; Sørensen, Frederik V.; Ljungars, Anne & Greiff, Victor [Show all 8 contributors for this article] (2024). ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns. Bioinformatics Advances. ISSN 2635-0041. 4(1). doi: 10.1093/bioadv/vbae020.
• Mhanna, Vanessa; Bashour, Habib; Lê Quý, Khang; Barennes, Pierre; Rawat, Puneet & Greiff, Victor [Show all 7 contributors for this article] (2024). Adaptive immune receptor repertoire analysis. Nature Reviews Disease Primers. ISSN 2056-676X. 4(1). doi: 10.1038/s43586-023-00284-1.
• Khan, Asif; Cowen-Rivers, Alexander I.; Grosnit, Antoine; Deik, Derrick-Goh-Xin; Robert, Philippe A. & Greiff, Victor [Show all 15 contributors for this article] (2023). Toward real-world automated antibody design with combinatorial Bayesian optimization. Cell Reports Methods. 3:100374(1), p. 1–21. doi: 10.1016/j.crmeth.2022.100374. Full text in Research Archive
• Sharma, Divya; Rawat, Puneet; Greiff, Victor; Janakiraman, Vani & Gromiha, M. Michael (2023). Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochimica et Biophysica Acta - Molecular Basis of Disease. ISSN 0925-4439. 1870(2). doi: 10.1016/j.bbadis.2023.166959.
• Robert, Philippe Paul Auguste; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael & Snapkov, Igor [Show all 25 contributors for this article] (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science. ISSN 2662-8457. 2(12), p. 845–865. doi: 10.1038/s43588-022-00372-4.
• Dorraji, Seyed Esmaeil; Borgen, Elin; Segura-Pena, Dario; Rawat, Puneet; Smorodina, Eva & Dunn, Claire [Show all 10 contributors for this article] (2022). Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform. Cancers. ISSN 2072-6694. 14:4859(19), p. 1–24. doi: 10.3390/cancers14194859. Full text in Research Archive Show summary

  Simple Summary In the present study, we addressed the unmet need for a molecular antibody (mAb) with high affinity and specificity against a truncated hyperactive isoform of human epidermal growth factor receptor 2 (HER2), called 611-carboxy terminal fragment (CTF)-p95HER2. Patients with p95HER2+ breast cancer are at risk of developing metastatic breast cancer with a poor prognosis and resistance to therapies targeting full-length HER2. We have generated a mAb named Oslo-2, which react specifically with 611-CTF-p95HER2 and has a high affinity. We also characterized the antigenic determinant (epitope) on the p95HER2 protein and the antigen-binding site (paratope) on the Oslo-2 mAb. The antibody can be used to develop antibody- or cell-based therapies targeting p95HER2, as well as a diagnostic assay to identify p95HER2+ disease. Abstract The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy. Contrary to full-length HER2, 611-p95HER2 has negligible normal tissue expression. There is currently no approved diagnostic assay to identify this subgroup and no therapy targeting this mechanism of tumor escape. The purpose of this study was to develop a monoclonal antibody (mAb) against 611-CTF-p95HER2. Hybridomas were generated from rats immunized with cells expressing 611-CTF. A hybridoma producing a highly specific Ab was identified and cloned further as a mAb. This mAb, called Oslo-2, gave strong staining for 611-CTF and no binding to full-length HER2, as assessed in cell lines and tissues by flow cytometry, immunohistochemistry and immunofluorescence. No cross-reactivity against HER2 negative controls was detected. Surface plasmon resonance analysis demonstrated a high binding affinity (equilibrium dissociation constant 2 nM). The target epitope was identified at the N-terminal end, using experimental alanine scanning. Further, the mAb paratope was identified and characterized with hydrogen-deuterium-exchange, and a molecular model for the (Oslo-2 mAb:611-CTF-p95HER2) complex was generated by an experimental-information-driven docking approach. We conclude that the Oslo-2 mAb has a high affinity and is highly specific for 611-CTF-p95HER2. The Ab may be used to develop potent and safe therapies, overcoming p95HER2-mediated tumor escape, as well as for developing diagnostic assays. Keywords: HER2; p95HER2; antibody; tumor-specific; breast cancer; cancer stem cells; HDX-MS; epitope and paratope mapping; docking

• Rawat, Puneet; Sharma, Divya; Pandey, Medha; Prabakaran, R. & Gromiha, M. Michael (2022). Understanding the mutational frequency in SARS-CoV-2 proteome using structural features. Computers in Biology and Medicine. ISSN 0010-4825. 147:105708, p. 1–9. doi: 10.1016/j.compbiomed.2022.105708. Full text in Research Archive
• Akbar, Rahmad; Bashour, Habib; Rawat, Puneet; Robert, Philippe Paul Auguste; Smorodina, Eva & Cotet, Tudor-Stefan [Show all 15 contributors for this article] (2022). Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. mAbs. ISSN 1942-0862. 14:e2008790(1), p. 1–35. doi: 10.1080/19420862.2021.2008790. Full text in Research Archive

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• Mhanna, Vanessa; Bashour, Habib; Lê Quý, Khang; Barennes, Pierre; Rawat, Puneet & Greiff, Victor [Show all 7 contributors for this article] (2024). Correction to: Adaptive immune receptor repertoire analysis (Nature Reviews Methods Primers, (2024), 4, 1, (6), 10.1038/s43586-023-00284-1). Nature reviews methods primers. ISSN 2662-8449. doi: 10.1038/s43586-024-00299-2.

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