Biology subjects

Pramanick, A.

Publications and source records attributed to Pramanick, A..

3 recordsLinked to original sources

bioRxiv2025

A Transformer based method for the Cap Analysis of Gene Expression and Gene Expression Tag associated 5' cap site prediction in RNA

5 RNA capping is one of the major post-transcriptional modifications for the mobility and stability of RNA molecules. Measuring 5 caps of RNAs can help quantify expression levels of mRNAs and lncRNAs. One of the most successful RNAseq methods that have used capping as a tool to quantify expression of transcription is Cap Analysis of Gene Expression(CAGE). Computational prediction of capping can therefore be used as a precursor to the prediction of transcriptional expression. Unfortunately, there is hardly any computational technique that has focused purely on predicting 5 capping. We have developed a transformer-based method for computational prediction of capping from DNA sequences. Our Llama and ReLoRA-based pre-training model, and Llama and LoRA-based fine-tuning model predict 5 cap sites. We have used Leave-one-chromosome-out-cross-validation for our model. The average accuracy, and F1-score after fine-tuning the human genome hg19(mouse genome mm9) for sequence classification is 79.12%(78.09%), and 78.11%(76.17%), respectively. We noted attention peak-based motifs having an aggregate Wilcoxon rank-sum p-value of 1.075e-10 between the attention peak region and the entire context window for the predicted positive motifs; an aggregate p-value of 7.17e-18 for the predicted negative motifs; and an aggregate p-value of 6.70e-08 between the attention peaks of the predicted positive and the predicted negative motifs. Our Llama-based approach aims to create a sequence-based framework to identify 5 capping sites corresponding to CAGE peaks. Our analysis reveals statistically significant motifs from the regions of peak attention scores, which demonstrates biological relevance for some through their resident sites matching with known TF motifs.

bioinformatics↗

bioRxiv2025

DeepPROTECTNeo: A Deep learning-based Personalized and RV-guided Optimization tool for TCR Epitope interaction using Context-aware Transformers

Background: The development of personalized cancer vaccines relies on accurately identifying neoepitopes capable of eliciting strong immune responses. T cell receptor (TCR)-epitope interactions are fundamental to cancer immunotherapy. Traditional computational approaches focus primarily on epitope-major histocompatibility complex (MHC) binding, often overlooking the critical contribution of TCR binding. Furthermore, the clinical applicability of existing methods is constrained by fragmented pipelines that require separate workflows for variant calling, HLA typing, and independent peptide-MHC (pMHC) or peptide-TCR (pTCR) evaluation stages. Results: We present DeepPROTECTNeo, a unified deep learning framework that integrates genomic variant detection, HLA typing, high-affinity pMHC binding prediction, variant-driven TCR repertoire mining, followed by a hybrid transformer-Convolutional Neural Network dual-branch feature extractor with an explicit cross-attention-based deep learning model for TCR-epitope binding prediction. Our reverse vaccinology-inspired biologically informed architecture integrates Bidirectional Long short-term memory (Bi-LSTM) sequence features, convolutional-attention physicochemical/evolutionary descriptors via gated fusion, and TCR numbered contextual embeddings to enable residue-level interpretable modelling. Under a strict TCR-split strategy, it achieved a mean AUROC of 0.7856 and AUPRC of 0.7932 outperforming six state-of-the-art predictors by 4-5% with tight inter-fold stability. The architecture maintains high robustness against structural hard negatives and imbalanced datasets, successfully recovering 18 of 34 validated high-affinity neoepitopes from a patient-specific cancer cohort. Conclusions: Experiments results demonstrate that DeepPROTECTNeo is a powerful, reliable end-to-end neoantigen prioritization framework that effectively models complex TCR-epitope interfaces directly from clinical sequencing data, providing a robust interpretable foundation to accelerate personalized cancer immunotherapy.

bioinformatics↗

bioRxiv2024

4D bioprinting shape-morphing tissues in granular support hydrogels: Sculpting structure and guiding maturation

During embryogenesis, organs undergo dynamic shape transformations that sculpt their final shape, composition, and function. Despite this, current organ bioprinting approaches typically employ bioinks that restrict cell-generated morphogenetic behaviours resulting in structurally static tissues. Here, we introduce a novel platform that enables the bioprinting of tissues that undergo programmable and predictable 4D shape-morphing driven by cell-generated forces. Our method utilises embedded bioprinting to deposit collagen-hyaluronic acid bioinks within yield-stress granular support hydrogels that can accommodate and regulate 4D shape-morphing through their viscoelastic properties. Importantly, we demonstrate precise control over 4D shape-morphing by modulating factors such as the initial print geometry, cell phenotype, bioink composition, and support hydrogel viscoelasticity. Further, we observed that shape-morphing actively sculpts cell and extracellular matrix alignment along the principal tissue axis through a stress-avoidance mechanism. To enable predictive design of 4D shape-morphing patterns, we developed a finite element model that accurately captures shape evolution at both the cellular and tissue levels. Finally, we show that programmed 4D shape-morphing enhances the structural and functional properties of iPSC-derived heart tissues. This ability to design, predict, and program 4D shape-morphing holds great potential for engineering organ rudiments that recapitulate morphogenetic processes to sculpt their final shape, composition, and function.

bioengineering↗