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Bhargab Ghoshal

Publications and source records attributed to Bhargab Ghoshal.

3 recordsLinked to original sources

The Quantum Cliff: A Critical Proton Tunneling Threshold Determines Clinical Severity in RPE65-Mediated Retinal Disease

Predicting clinical severity from genotype remains a fundamental challenge in molecular medicine, particularly for enzymes whose function depends on sub-atomic-scale geometry. Mutations in the \textit{RPE65} isomerohydrolase cause Leber Congenital Amaurosis (LCA) and related retinal diseases; however, the kinetic mechanisms connecting sub-atomic-scale perturbations to blindness remain unclear. In this study, we demonstrate that mutations in the human visual isomerase RPE65 are governed by a quantum-mechanical threshold effect arising from proton tunneling in the active site. We established a hybrid quantum-classical structure-to-phenotype pipeline combining AlphaFold structure prediction with \textit{ab initio} quantum simulation using the Variational Quantum Eigensolver (VQE) to analyze minimal proton-coupled electron transfer in the visual cycle. Our analysis reveals that many pathogenic mutations do not merely occlude the active site, but rather strongly reduce the quantum probability of proton tunneling. We observed a sharp non-linear effect, termed the "Quantum Cliff," where minute structural changes (below 0.1 Å) reduce the reaction rate by multiple orders of magnitude. Based on these findings, we introduce a dimensionless Relative Quantum Activity Score (RQAS) that isolates the geometry-controlled exponential sensitivity of the reaction rate and successfully distinguishes between mild and severe patient phenotypes. These results suggest that RPE65 operates near a quantum-critical point, where sub-Angstrom structural perturbations induce a catastrophic loss of function. Furthermore, our findings establish quantum tunneling as a predictive mechanistic link between atomic structure and clinical phenotype, proposing a general framework for quantum-structural disease modeling.

cs.ET

Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma Grading

Pancreatic cancers have one of the worst prognoses compared to other cancers, as they are diagnosed when cancer has progressed towards its latter stages. The current manual histological grading for diagnosing pancreatic adenocarcinomas is time-consuming and often results in misdiagnosis. In digital pathology, AI-based cancer grading must be extremely accurate in prediction and uncertainty quantification to improve reliability and explainability and are essential for gaining clinicians trust in the technology. We present Bayesian Convolutional Neural Networks for automated pancreatic cancer grading from MGG and HE stained images to estimate uncertainty in model prediction. We show that the estimated uncertainty correlates with prediction error. Specifically, it is useful in setting the acceptance threshold using a metric that weighs classification accuracy-reject trade-off and misclassification cost controlled by hyperparameters and can be employed in clinical settings.

eess.IV

Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development

B-cell epitopes play a key role in stimulating B-cells, triggering the primary immune response which results in antibody production as well as the establishment of long-term immunity in the form of memory cells. Consequently, being able to accurately predict appropriate linear B-cell epitope regions would pave the way for the development of new protein-based vaccines. Knowing how much confidence there is in a prediction is also essential for gaining clinicians' trust in the technology. In this article, we propose a calibrated uncertainty estimation in deep learning to approximate variational Bayesian inference using MC-DropWeights to predict epitope regions using the data from the immune epitope database. Having applied this onto SARS-CoV-2, it can more reliably predict B-cell epitopes than standard methods. This will be able to identify safe and effective vaccine candidates against Covid-19.

q-bio.QM