arXiv subjects

Sanjib Senapati

Publications and source records attributed to Sanjib Senapati.

5 recordsLinked to original sources

arXiv2026

An Integrative Computational Approach to Predict Viral Epitopes by Targeting the MHC-TCR Complexation

T-cell immunity acts as a major defense system against controlling viral infections in vertebrates. During viral entry, innate immune cells degrade the viral proteins (antigens) and present them on their surface via Major Histocompatibility (MHC) proteins. T-cell receptors (TCRs) recognize these antigens/peptides presented by MHC (pMHC), initiating a T-cell mediated immune response. Despite its significance, the mechanism by which pMHC-TCR binding triggers T-cell activation remains unclear. In this study, we employed an integrative computational approach combining Bioinformatics, Molecular Dynamics (MD) simulations, and Machine Learning (ML) to identify viral epitopes as potential vaccine candidates. We performed large-scale all-atom and coarse-grained MD simulations on MHC-peptide-TCR complexes embedded into dendritic and T-cells, for which experimental immunogenicity data is available. One hundred fifty such systems are simulated for 1 μs each to capture the conformational and dynamical changes that underlie T-cell activation. Our ML model (DynamiT), trained on simulation-derived structural and dynamical features extracted from 2500 time points, revealed key determinants responsible for T-cell activation with an accuracy of 73.3%. Notably, we have identified the bending of the TCR transmembrane region, major dynamic motions of the TCRα constant region and the buried surface area at the pMHC and TCR interface as critical factors influencing immune response initiation. Our approach unravels the mechanism of T-cell mediated immune response and helps ML-guided screening of viral epitopes for vaccine development.

q-bio.BM↗

arXiv2026

Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery

Genome sequencing is essential to decode genetic information, identify organisms, understand diseases and advance personalized medicine. A critical step in any genome sequencing technique is genome assembly. However, de novo genome assembly, which involves constructing an entire genome sequence from scratch without a reference genome, presents significant challenges due to its high computational complexity, affecting both time and accuracy. In this study, we propose a hybrid approach utilizing a quantum computing-based optimization algorithm integrated with classical pre-processing to expedite the genome assembly process. Specifically, we present a method to solve the Hamiltonian and Eulerian paths within the genome assembly graph using gate-based quantum computing through a Higher-Order Binary Optimization (HOBO) formulation with the Variational Quantum Eigensolver algorithm (VQE), in addition to a novel bitstring recovery mechanism to improve optimizer traversal of the solution space. A comparative analysis with classical optimization techniques was performed to assess the effectiveness of our quantum-based approach in genome assembly. The results indicate that, as quantum hardware continues to evolve and noise levels diminish, our formulation holds a significant potential to accelerate genome sequencing by offering faster and more accurate solutions to the complex challenges in genomic research.

quant-ph↗

arXiv2025

Capturing Protein Free Energy Landscape using Efficient Quantum Encoding

Protein folding is one of the age-old biological problems that refers to the mechanism of understanding and predicting how a protein's linear sequence of amino acids folds into its specific three dimensional structure.This structure is critical, as a protein's functionality is inherently linked to its final folded form. Misfolding can lead to severe diseases such as Alzheimer's and cystic fibrosis, highlighting the biological and clinical importance of understanding protein folding mechanisms. This work presents a novel turn based encoding optimization algorithm for predicting the folded structures of peptides and small proteins. Our approach builds upon our previous research, where our objective function focused on hydrophobic collapse, a fundamental phenomenon underlying the protein folding process. In this work, we extend that framework by not only incorporating hydrophobic interactions but also including all non bonded interactions modeled using the Miyazawa Jernigan potential. We constructed a Hamiltonian from the defined objective function that encodes the folding process on a three dimensional face centered cubic lattice, offering superior packing efficiency and a realistic representation of protein conformations. This Hamiltonian is then solved using classical and quantum solvers to explore the vast conformational space of proteins. To identify the lowest-energy folded configurations, we utilize the Variational Quantum Eigensolver implemented on IBM 133 qubit hardware. The predicted structures are validated against experimental data using root mean square deviation as a metric and compared against classical simulated annealing and molecular dynamics simulation results. Our findings highlight the promise of hybrid classical and quantum approaches in advancing protein folding predictions, particularly for sequences with low homology.

quant-ph↗

arXiv2023

An approach to solve the coarse-grained Protein folding problem in a Quantum Computer

Protein folding, which dictates the protein structure from its amino acid sequence, is half a century old problem of biology. The function of the protein correlates with its structure, emphasizing the need of understanding protein folding for studying the cellular and molecular mechanisms that occur within biological systems. Understanding protein structures and enzymes plays a critical role in target based drug designing, elucidating protein-related disease mechanisms, and innovating novel enzymes. While recent advancements in AI based protein structure prediction methods have solved the protein folding problem to an extent, their precision in determining the structure of the protein with low sequence similarity is limited. Classical methods face challenges in generating extensive conformational samplings, making quantum-based approaches advantageous for solving protein folding problems. In this work we developed a novel turn based encoding algorithm that can be run on a gate based quantum computer for predicting the structure of smaller protein sequences using the HP model as an initial framework, which can be extrapolated in its application to larger and more intricate protein systems in future. The HP model best represents a major step in protein folding phenomena - the hydrophobic collapse which brings the hydrophobic amino acid to the interior of a protein. The folding problem is cast in a 3D cubic lattice with degrees of freedom along edges parallel to the orthogonal axes, as well as along diagonals parallel to the axial planes. While, the original formulation with higher order terms can be run on gate based quantum hardwares, the QUBO formulation can give results on both classical softwares employing annealers and IBM CPLEX as well as quantum hardwares.

quant-ph↗

arXiv2018

Ionic liquids make DNA rigid

Persistence length of dsDNA is known to decrease with increase in ionic concentration of the solution. In contrast to this, here we show that persistence length of dsDNA increases dramatically as a function of ionic liquid (IL) concentration. Using all atomic explicit solvent molecular dynamics simulations and theoretical models we present, for the first time, a systematic study to determine the mechanical properties of dsDNA in various hydrated ionic liquids at different concentrations. We find that dsDNA in 50 wt% ILs have lower persistence length and stretch modulus in comparison to 80 wt% ILs. We further observe that both persistence length and stretch modulus of dsDNA increase as we increase the ILs concentration. Present trend of stretch modulus and persistence length of dsDNA with ILs concentration supports the predictions of the macroscopic elastic theory, in contrast to the behavior exhibited by dsDNA in monovalent salt. Our study further suggests the preferable ILs that can be used for maintaining DNA stability during long-term storage.

physics.bio-ph↗