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.

• Abrantes, Rafaela; Forcados, Christopher; Warren, David; Santos-Ferreira, Liliana; Fleten, Karianne Giller & Senra, Emanuel [Vis alle 23 forfattere av denne artikkelen] (2025). Pan-carcinoma sialyl-Tn-targeting expands CAR therapy to solid tumors. Cell Reports Medicine. 6(9). doi: 10.1016/j.xcrm.2025.102350. Fulltekst i vitenarkiv Vis sammendrag

  Accurate identification of tumor-specific markers is vital for developing chimeric antigen receptor (CAR)-based therapies. While cell surface antigens are seldom cancer-restricted, their post-translational modifications (PTMs), particularly aberrant carbohydrate structures, offer attractive alternatives. Among these, the sialyl-Tn (STn) antigen stands out for its prevalent presence in various epithelial tumors. Although monoclonal antibodies (mAbs) against STn have been developed, their clinical application has been hindered by concerns regarding specificity. Herein, we describe AM52.1, a mAb with unprecedented specificity for STn and lack of reactivity with healthy tissues. The single-chain variable fragment (scFv) of AM52.1 was assembled into a second-generation CAR scaffold. AM52.1CAR T cells efficiently targeted STn-expressing cancer cell lines and patient-derived organoids (PDOs), while sparing STn-negative cells. In further preclinical models, AM52.1CAR T cells robustly controlled gastric and tubo-ovarian tumors, as well as colorectal cancer mucinous peritoneal metastases, highlighting their strong therapeutic potential for targeting and managing complex solid tumors.

• Ursu, Eugen; Minnegalieva, Aygul; Rawat, Puneet; Chernigovskaya, Maria; Tacutu, Robi & Sandve, Geir Kjetil Ferkingstad [Vis alle 8 forfattere av denne artikkelen] (2025). Training data composition determines machine learning generalization and biological rule discovery. Nature Machine Intelligence. 7(8), s. 1206–1219. doi: 10.1038/s42256-025-01089-5. Fulltekst i vitenarkiv Vis sammendrag

  Supervised machine learning models depend on training datasets containing positive and negative examples: dataset composition directly impacts model performance and bias. Given the importance of machine learning for immunotherapeutic design, we examined how different negative class definitions affect model generalization and rule discovery for antibody–antigen binding. Using synthetic-structure-based binding data, we evaluated models trained with various definitions of negative sets. Our findings reveal that high out-of-distribution performance can be achieved when the negative dataset contains more similar samples to the positive dataset, despite lower in-distribution performance. Furthermore, by leveraging ground-truth information, we show that binding rules associated with positive data change based on the negative data used. Validation on experimental data supported simulation-based observations. This work underscores the role of dataset composition in creating robust, generalizable and biology-aware sequence-based ML models

• Rawat, Puneet; Crescioli, Silvia; Prabakaran, R.; Sharma, Divya; Greiff, Victor & Reichert, Janice M. (2025). YAbS: The Antibody Society's antibody therapeutics database. mAbs. ISSN 1942-0862. 17(1). doi: 10.1080/19420862.2025.2468845. Fulltekst i vitenarkiv Vis sammendrag

  Therapeutic antibodies have gained prominence in recent years due to their precision in targeting specific diseases. As these molecules become increasingly essential in modern medicine, comprehensive data tracking and analysis are critical for advancing research and ensuring successful clinical outcomes. YAbS, The Antibody Society’s Antibody Therapeutics Database, serves as a vital resource for monitoring the development and clinical progress of therapeutic antibodies. The database catalogs detailed information on over 2,900 commercially sponsored investigational antibody candidates that have entered clinical study since 2000, as well as all approved antibody therapeutics. Data for the late-stage clinical pipeline and antibody therapeutics in regulatory review or approved (over 450 molecules) are openly accessible (https://db.antibodysociety.org). Antibody-related information includes molecular format, targeted antigen, current development status, indications studied, and the clinical development timeline of the antibodies, as well as the geographical region of company sponsors. Furthermore, the database supports in-depth industry trends analysis, facilitating the identification of innovative developments and the assessment of success rates within the field. This resource is continually updated and refined, providing invaluable insights to researchers, clinicians, and industry professionals engaged in antibody therapeutics development.

• Rawat, Puneet; Prabakaran, R.; Sharma, Divya; Mandala, Vasanth; Greiff, Victor & Kumar, Sandeep [Vis alle 7 forfattere av denne artikkelen] (2025). Investigating Local Sequence-Structural Attributes of Amyloidogenic Light Chain Variable Domains. Proteins: Structure, Function, and Bioinformatics. ISSN 0887-3585. 93(9), s. 1451–1464. doi: 10.1002/prot.26815. Fulltekst i vitenarkiv Vis sammendrag

  Light chain amyloidosis is a medical condition characterized by the aggregation of misfolded antibody light chains into insoluble amyloid fibrils in the target organs, causing organ dysfunction, organ failure, and death. Despite extensive research to understand the factors contributing to amyloidogenesis, accurately predicting whether a given protein will form amyloids under specific conditions remains a formidable challenge. In this study, we have conducted a comprehensive analysis to understand the amyloidogenic tendencies within a dataset containing 1828 (348 amyloidogenic and 1480 non-amyloidogenic) antibody light chain variable region (VL) sequences obtained from the AL-Base database. Physicochemical and structural features often associated with protein aggregation, such as net charge, isoelectric point (pI), and solvent-exposed hydrophobic regions did not reveal a consistent association with the aggregation capability of the antibody light chains. However, the solvent-exposed aggregation-prone regions (APRs) occur with higher frequencies among the amyloidogenic light chains when compared with the non-amyloidogenic ones, with the difference ranging from 2% to 15% at various relative solvent-accessible surface area (rASA) cutoffs. We have, for the first time, identified structural gatekeeping residues around the APRs and assessed their impact on the amyloidogenicity of the antibody light chains. The non-amyloidogenic light chains contain these structural gatekeeper residues vicinal to their APRs more often than the amyloidogenic ones. We observed that the rASA cutoff of 35% is optimal for identifying the surface-exposed APRs, and a 4 Å distance cutoff from the APR motif(s) is optimal for identifying the structural gatekeeper residues. Moreover, lambda light chains were found to contain solvent-exposed APRs more often and surrounded by fewer gatekeepers, rendering them more susceptible to aggregation. The insights gained from this report have significant implications for understanding the molecular origins of light-chain amyloidosis in humans and the design of aggregation-resistant therapeutic antibodies.

• Bashour, Habib; Smorodina, Eva; Pariset, Matteo; Zhong, Jahn; Akbar, Rahmad & Chernigovskaia, Maria [Vis alle 15 forfattere av denne artikkelen] (2024). Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability. Communications Biology. 7(1), s. 1–25. doi: 10.1038/s42003-024-06561-3. Fulltekst i vitenarkiv Vis sammendrag

  Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

• Mhanna, Vanessa; Bashour, Habib; Quy, Khang Le; Barennes, Pierre; Rawat, Puneet & Greiff, Victor [Vis alle 7 forfattere av denne artikkelen] (2024). Adaptive immune receptor repertoire analysis. Nature Reviews Disease Primers. 4(1), s. 1–25. doi: 10.1038/s43586-023-00284-1. Fulltekst i vitenarkiv
• Sørensen, Christoffer V.; Hofmann, Nils; Rawat, Puneet; Sørensen, Frederik V.; Ljungars, Anne & Greiff, Victor [Vis alle 8 forfattere av denne artikkelen] (2024). ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns. Bioinformatics Advances. 4:vbae020(1), s. 1–5. doi: 10.1093/bioadv/vbae020. Fulltekst i vitenarkiv Vis sammendrag

  High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil the full potential of display techniques, like antibody phage display, in molecular biology. However, a significant challenge lies in the processing and analysis of such data. Furthermore, there is a notable absence of open-access user-friendly software tools that can be utilized by scientists lacking programming expertise. Here, we present ExpoSeq as an easy-to-use tool to explore, process, and visualize HTS data from antibody discovery campaigns like an expert while only requiring a beginner’s knowledge.

• Khan, Asif; Cowen-Rivers, Alexander I.; Grosnit, Antoine; Deik, Derrick-Goh-Xin; Robert, Philippe A. & Greiff, Victor [Vis alle 15 forfattere av denne artikkelen] (2023). Toward real-world automated antibody design with combinatorial Bayesian optimization. Cell Reports Methods. 3:100374(1), s. 1–21. doi: 10.1016/j.crmeth.2022.100374. Fulltekst i vitenarkiv Vis sammendrag

  Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

• 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. Fulltekst i vitenarkiv
• 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, s. 1–9. doi: 10.1016/j.compbiomed.2022.105708. Fulltekst i vitenarkiv Vis sammendrag

  The prolonged transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in the human population has led to demographic divergence and the emergence of several location-specific clusters of viral strains. Although the effect of mutation(s) on severity and survival of the virus is still unclear, it is evident that certain sites in the viral proteome are more/less prone to mutations. In fact, millions of SARS-CoV-2 sequences collected all over the world have provided us a unique opportunity to understand viral protein mutations and develop novel computational approaches to predict mutational patterns. In this study, we have classified the mutation sites into low and high mutability classes based on viral isolates count containing mutations. The physicochemical features and structural analysis of the SARS-CoV-2 proteins showed that features including residue type, surface accessibility, residue bulkiness, stability and sequence conservation at the mutation site were able to classify the low and high mutability sites. We further developed machine learning models using above-mentioned features, to predict low and high mutability sites at different selection thresholds (ranging 5–30% of topmost and bottommost mutated sites) and observed the improvement in performance as the selection threshold is reduced (prediction accuracy ranging from 65 to 77%). The analysis will be useful for early detection of variants of concern for the SARS-CoV-2, which can also be applied to other existing and emerging viruses for another pandemic prevention.

• Robert, Philippe Paul Auguste; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael & Snapkov, Igor [Vis alle 25 forfattere av denne artikkelen] (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science. 2(12), s. 845–865. doi: 10.1038/s43588-022-00372-4. Fulltekst i vitenarkiv
• Akbar, Rahmad; Bashour, Habib; Rawat, Puneet; Robert, Philippe Paul Auguste; Smorodina, Eva & Cotet, Tudor-Stefan [Vis alle 15 forfattere av denne artikkelen] (2022). Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. mAbs. ISSN 1942-0862. 14:e2008790(1), s. 1–35. doi: 10.1080/19420862.2021.2008790. Fulltekst i vitenarkiv Vis sammendrag

  Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

• Dorraji, Seyed Esmaeil; Borgen, Elin; Segura-Pena, Dario; Rawat, Puneet; Smorodina, Eva & Dunn, Claire [Vis alle 10 forfattere av denne artikkelen] (2022). Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform. Cancers. 14:4859(19), s. 1–24. doi: 10.3390/cancers14194859. Fulltekst i vitenarkiv Vis sammendrag

  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

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• Mhanna, Vanessa; Bashour, Habib; Quý, Khang Lê; Barennes, Pierre; Rawat, Puneet & Greiff, Victor [Vis alle 7 forfattere av denne artikkelen] (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. doi: 10.1038/s43586-024-00299-2. Fulltekst i vitenarkiv

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