Theory & Computer Simulation to understand biology at the molecular level

Immerse yourself in the captivating world of molecular biology within our cutting-edge laboratory, where the fusion of Theory and computer Simulation drives groundbreaking research. Here, we unravel the enigmatic molecular mysteries, seamlessly bridging the realms of theory and reality through the power of virtual laboratories. With state-of-the-art simulations, we boldly traverse the concealed territories of cells and genes, bestowing unparalleled insights into the fundamental building blocks of life. In this digital era, our laboratory stands as an exhilarating gateway, unlocking the molecular wonders of biology. Join us on an exhilarating journey where science and innovation harmonize, pushing the boundaries of knowledge to new horizons.

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24+ Years Experience
30+ Supervised Research Scholar
5+ Major Research Projects
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About

My research interests include computational biophysics, protein folding, molecular dynamics simulation, and formal methods in molecular modeling. Our group works on developing novel computational tools for studying complex biological systems.

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Professor Pradipta Bandyopadhyay

Experienced Professor & Researcher

Experienced teacher and researcher in the broad area of theoretical & computational Biophysics, Biophysical chemistry. Expertise in molecular dynamics simulation, monte carlo simulation, statistical mechanics, machine learning, quantum chemical calculations, protein-ligand/ion interactions. Particularly interested in developing methodology and non-trivial applications of computational tools.

Current projects include development of methods for protein-ligand, protein-ion interactions using both physics & ML. Development of ultrafast solvation models, understanding the mechanism of cardiac muscle contraction & effect of mutations on key proteins involved in muscle contraction.

Professor at JNU for the last 10 years. Taught at IIT Guwahati & worked at Arqule Inc. at Redwood City, California. With modest facilities regularly publishing in top international journals. Have collaborations with some of the leading groups of the world.

Name Pradipta Bandyopadhyay
Phone 011-26738704
Email praban07@gmail.com
Country India

Research

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Current Research Topics :

Molecular Mechanism of Calcium Signalling and Engineering Proteins to Control Signal Transduction: Toward Understanding Calcium Signalling Pathways

Calcium ions (Ca2+) are involved in diverse biological processes by activating regulatory proteins such as calmodulin. The challenge in studying the binding process includes inaccuracy in the Ca2+ forcefield, sampling of conformational space and high computational costs.

We looked at the origin of the differential binding affinity of Ca2+ binding to different sites of calmodulin and developed a structure-based predictive model for binding affinity. In collaboration with experimentalists, we are applying quantum mechanics, statistical physics, and machine learning methods to elucidate how the biophysical properties of Ca2+ and proteins shape function at a cellular level. We are interested in rationalising and predicting Ca2+ affinity and engineering calcium-binding proteins to enhance the signalling process.

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DNA-ION Interaction

The storage, transmission, processing, and control of genetic information rely heavily on nucleic acids, which also act as catalysts and mechanochemical switches as well as carriers of the genetic code. Large interaction energies are provided by interactions between nucleic acid and ions. Therefore, these interactions are an essential aspect of a comprehensive explanation of the folding of functional DNAs and RNAs, of interactions of nucleic acids with ligands and macromolecule partners, and of the activity of RNAs and RNA-protein complexes and machines. Thermodynamic and mathematical frameworks for ion-nucleic acid interactions have been presented in various ways. With the help of recent experimental advances, it is now possible to describe the crucial aspects of the ion atmosphere and how they affect nucleic acids and their interactions in a way that is both more logical and scientifically supported.

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Implicit Solvation with Physical and Structural Properties: Analytical Modelling of Hydration Thermodynamics of Small Organic Molecules

Water is present in almost all chemical and biological phenomena. To study the biomolecules in water or water itself, two main approaches are used to model water: explicit and implicit. The explicit models are physically accurate but they trade off the speed, while implicit models have a speed advantage over physicality. We are trying to explore a third option that makes a different trade-off. We are developing an analytical model to calculate hydration thermodynamic properties using the statistical mechanical-based analytical variant of the MB water model. Here, we treat the solvation shell as having tetrahedral waters, treated through statistical mechanical averaging and combined with a surface physics term.

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Protein Unfolding

Protein unfolds under pressure, typically above 3 kbar. Why do proteins denature at high pressure? Despite decades of research, the mechanism of protein denaturation under pressure is still a hotly debated topic. Several views emerge from various experiments and computer simulations. The most widely accepted reason for unfolding is considered to be the penetration of water into the protein interior upon application of pressure, leading to unfolding. Applying high pressure restricts the translational and orientational movement of the water molecules; however, water insertion may relax the above two motions outside the protein molecule. Whether enthalpy or entropy is the main driver of protein unfolding is a key issue that we are trying to explore using computer simulations.

Previous Research Topics:

Development of enhanced Monte Carlo simulation techniques to explore the energy landscape of water clusters and biomolecules

We have developed an efficient Monte-Carlo-based method, Temperature Basin Paving, for the exploration of complex energy landscapes. We have successfully applied this method to different sizes of water clusters and small RNA hairpins, and we are continuously optimizing this method for more complex systems.

Properties of RNA with non-Watson-Crick base pair

Non-Watson-Crick base pairs are the basic necessity for different three-dimensional folds of RNAs and the activity of different Ribozymes. These folds make the interaction of RNAs with proteins, other molecules, and ions possible. Non-Watson-Crick base pairs always need some external factors like metal ions, other ribosomal strand or proteins for stabilization. In the current work, we are analyzing different aspects of the stabilization mechanism of non-Watson-Crick base pairs by these external factors.

Development of a model of the cytoplasm of a bacterial cell and understanding of diffusion and hydrodynamics

In this work, we have developed a computational model of E. coli cytoplasm to study the diffusive motion of proteins inside the cell. The model contains the most abundant proteins present E. coli cytoplasm that has been coarse-grained in the form of a collection of spheres. The presence of a large number of particles provides large volume exclusion and hydrodynamic interactions which in turn affect the diffusivity of proteins inside the cell. In this work, we are trying to determine the effect of crowding and hydrodynamic interactions on the diffusive motion of proteins.

Development of Random Walk model to study anomalous diffusion

Subdiffusion is ubiquitous in a crowded heterogeneous environment, but the exact cause of subdiffusion is still debatable. In this work, we are trying to understand the cause of subdiffusion in a crowded and heterogeneous environment using random walk models. By developing different models, we are working to understand the cause of transient and pure anomalous diffusion with constant diffusion exponent at all time scales.

Effect of molecular crowding on biomolecular systems

Living cells have vast varieties of molecules like nucleic acid, protein, small messenger molecules, osmolytes, carbohydrates, and insoluble molecules. These molecules are known to occupy a significant fraction of cellular volume(20-40%) and are referred to as molecular crowders. Molecular crowding has large quantitative effects on the structure, thermodynamics, and function of nucleic acids and proteins by excluding volume and interacting with them. It has been shown that molecular crowding affects properties of nucleic acid like melting temperature, hybridization, structural preference, diffusion, etc. In this work, we are trying to understand the structural, hydration, and thermodynamics of the canonical form of DNA in a crowded environment. One of the ways to understand DNA stability is to understand the thermodynamics of its hydration, which we are currently focused to look at.

Effect of salt electrostatics on like-charged protein-protein binding

Here, we are trying to explore the effect of salt ions on the like charged protein-protein binding. How does salt ion concentration in the system affects the like-charged protein-protein binding and what roles are played by salt ions near the binding interface which determines the free energy change of complex formation, are the questions we are trying to address.

Publications

Google Scholar Profile

Recent Publications :

  1. 1. Hydration Free Energies for Small Molecules with Physics-based Descriptors: Graph Neural Network with Cross Attention.
    Anuj Kumar Sirohi, Ajeet Kumar Yadav, Pradipta Bandyopadhyay (Submitted-2025). – ChemRxiv-2025 841, 141180, 2024.
    Link
  2. 2. Hydrophobic Solvation and Liquid Water: A Theoretical Investigation Using Thermodynamic Perturbation and Integral Equations Theory
    Umesh Roy, Pradipta Bandyopadhyay, Tomaz Urbic. J.Mol.Liq (2025) – Science Direct-Elsevier Journal of Molecular Liquids
    Link
  3. 3. Physics-Based Machine Learning to Predict Hydration Free Energies for Small Molecules with a Minimal Number of Descriptors: Interpretable and Accurate.
    Ajeet Kumar Yadav, Marvin V Prakash, Pradipta Bandyopadhyay.(2025) – American Chemical SocietyJournal of Physical Chemistry B (In press, 2025)
    Link
  4. 4. Understanding Cu+2 binding with DNA: A molecular dynamics study comparing Cu2+ and Mg2+ binding to the Dickerson DNA, Biophysical Chemistry.
    Angad Sharma, Hari O.S. Yadav, Pradipta Bandyopadhyay.(2025) – Science Direct-ElsevierBiophysical Chemistry Volume 316, 2025, 107347, ISSN 0301-4622,2025
    Link
  5. 5. Role of water in the pressure-induced unfolding of Bovine Pancreatic Trypsin Inhibitor (BPTI) protein: A molecular dynamics study
    UC Roy, P Bandyopadhyay – Chemical Physics Letters 841, 141180, 2024.
    Link
  6. 6. Prediction of Ca²⁺ binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures
    A Basit, D Choudhury, P Bandyopadhyay – Proteins: Structure, Function, and Bioinformatics, 2025.
    Link
  7. 7. Theoretical insights into the binding interaction of Nirmatrelvir with SARS-CoV-2 Mpro mutants (C145A and C145S): MD simulations and binding free-energy calculation to understand drug resistance
    P Purohit, M Panda, JT Muya, P Bandyopadhyay, BR Meher – J. Biomol. Struct. Dyn., 2023.
    Link
  8. 8. Nested Monte Carlo simulation of ionic systems using Debye-Hückel (DH) potential as importance function and optimizing DH potential with Kullback-Leibler divergence minimization
    R Srivastava, P Bandyopadhyay – J. Chem. Sci. 135 (2), 51, 2023.
    Link
  9. 9. Correlation between protein conformations and water structure and thermodynamics at high pressure: A molecular dynamics study of the BPTI protein
    UC Roy, P Bandyopadhyay – The Journal of Chemical Physics, 2023.
    Link
  10. 10. Crustwater: Modeling Hydrophobic Solvation
    AK Yadav, P Bandyopadhyay, EA Coutsias, KA Dill – The Journal of Physical Chemistry B, 2022.
    Link
  11. 11. Tests of a generalized Barker-Henderson perturbation theory for the phase coexistence diagram of an anisotropic potential
    B Kumari, SK Sarkar, P Bandyopadhyay – Chemical Physics 559, 111533, 2022.
    Link
  12. 12. Calcium ion binding to calmodulin mutants: A structure-based predictive model using charge scaling in molecular dynamics
    A Basit, AK Yadav, P Bandyopadhyay – J. Chem. Inf. Model., 2022.
    Link
  13. 13. A strategy to optimize peptide-based inhibitors against spike protein mutants of SARS-CoV-2
    P Priya, A Basit, P Bandyopadhyay – J. Biomol. Struct. Dyn., 2022.
    Link
  14. 14. Analytical 2D Model of Nonpolar and Ionic Solvation in Water
    AK Yadav, P Bandyopadhyay, T Urbic, KA Dill – J. Phys. Chem. B, 2021.
    Link
  15. 15. Calcium ion binding to calmodulin: binding free energy calculation using MM-PBSA method with implicit polarization
    A Basit, RK Mishra, P Bandyopadhyay – J. Biomol. Struct. Dyn., 2021.
  16. 16. Synthetic Tunability and Biophysical Basis for Fabricating Highly Fluorescent and Stable DNA Copper Nanoclusters
    N Tiwari, RK Mishra, S Gupta, R Srivastava, P Bandyopadhyay – Langmuir, 2021.
  17. 17. A look inside the black box: Using graph-theoretical descriptors to interpret a CF-CNN trained on neutral water clusters
    JA Bilbrey, JP Heindel, M Schram, P Bandyopadhyay – The Journal of Chemical Physics, 2020.
  18. 18. Salt-dependent free energy of binding and mechanism of β-lactoglobulin homodimer formation via MD and 3D-RISM
    R Srivastava, M Chattopadhyaya, P Bandyopadhyay – Phys. Chem. Chem. Phys., 2020.
  19. 19. Optimising parameters of Gibbs Ensemble Monte Carlo simulation: The role of multiple relaxation times
    B Kumari, P Bandyopadhyay, SK Sarkar – Molecular Simulation, 2020.
  20. 20. Atlas of putative minima and low-lying energy networks of water clusters (n=3–25)
    A Rakshit, P Bandyopadhyay, JP Heindel – The Journal of Chemical Physics, 2019.
  21. 21. Comparative evaluation of pair correlation functions for asymmetric electrolytes: HNC theory vs Monte Carlo
    P Bandyopadhyay, P Gupta-Bhaya – Chemical Physics Letters, 2019.

Publications Topic Wise :

Calcium Binding Proteins

  1. Calcium ion binding to the mutants of calmodulin: a structure-based computational predictive model of binding affinity using a charge scaling approach in molecular dynamics simulation.
    A Basit, AK Yadav, P Bandyopadhyay – Journal of Chemical Information and Modeling, 2022.
  2. Calcium ion binding to calmodulin: binding free energy calculation using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method by incorporating implicit polarization.
    A Basit, RK Mishra, P Bandyopadhyay – Journal of Biomolecular Structure and Dynamics, 2021.

Solvation Models

  1. Crustwater: Modeling Hydrophobic Solvation.
    AK Yadav, P Bandyopadhyay, EA Coutsias, KA Dill – The Journal of Physical Chemistry B, 2022.
  2. Analytical 2-Dimensional Model of Nonpolar and Ionic Solvation in Water.
    AK Yadav, P Bandyopadhyay, T Urbic, KA Dill – The Journal of Physical Chemistry B, 2021.

DNA-ion Interactions

  1. Synthetic Tunability and Biophysical Basis for Fabricating Highly Fluorescent and Stable DNA Copper Nanoclusters.
    N Tiwari, RK Mishra, S Gupta, R Srivastava, P Bandyopadhyay – Langmuir, 2021.

Cytoplasm, Molecular Crowding, and Diffusion

  1. Microscopic picture of water-ethylene glycol interaction near a model DNA by computer simulation: Concentration dependence, structure, and localized thermodynamics.
    Atul Kumar Jaiswal, Rakesh Srivastava, Preeti Pandey, Pradipta Bandyopadhyay – PLOS ONE, 13(11), e0206359, 2018.
  2. An analytical correlated random walk model and its application to understand subdiffusion in crowded environment.
    Sabeeha Hasnain, Pradipta Bandyopadhyay – Journal of Chemical Physics, 143(11), 114104, 2015.
  3. A New Coarse-Grained Model for E. coli Cytoplasm: Accurate Calculation of the Diffusion Coefficient of Proteins and Observation of Anomalous Diffusion.
    Sabeeha Hasnain, Christopher L. McClendon, Monica T. Hsu, Matthew P. Jacobson, Pradipta Bandyopadhyay – PLOS ONE, 9(9), e106466, 2014.
  4. A comparative Brownian dynamics investigation between small linear and circular DNA: Scaling of diffusion coefficient with size and topology of DNA.
    Sabeeha Hasnain, Matthew P. Jacobson, Pradipta Bandyopadhyay – Chemical Physics Letters, 591, 253–258, 2014.

Monte Carlo Based Optimization and Water Clusters

  1. A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters.
    JA Bilbrey, JP Heindel, M Schram, P Bandyopadhyay… – The Journal of Chemical Physics, 2020.
  2. Optimising the parameters of the Gibbs Ensemble Monte Carlo simulation of phase separation: the role of multiple relaxation times.
    B Kumari, P Bandyopadhyay, SK Sarkar – Molecular Simulation, 2020.
  3. Atlas of putative minima and low-lying energy networks of water clusters n = 3–25.
    A Rakshit, P Bandyopadhyay, JP Heindel… – The Journal of Chemical Physics, 2019.
  4. A comparative evaluation of pair correlation functions for a highly asymmetric electrolyte with mono and divalent counterions from integral equation theory in hypernetted chain (HNC) approximation and Monte Carlo simulation.
    P Bandyopadhyay, P Gupta-Bhaya – Chemical Physics Letters, 2019.
  5. Understanding the structure and hydrogen bonding network of (H2O)32 and (H2O)33: an improved Monte Carlo temperature basin paving (MCTBP) method and quantum theory of atoms in molecules (QTAIM) analysis.
    A Rakshit, T Yamaguchi, T Asada, P Bandyopadhyay – RSC Advances, 2017.
  6. A combination of Monte Carlo Temperature Basin Paving and graph theory: water cluster low energy structures and completeness of search.
    R Srivastava, S Shanker, A Rakshit, L Vig, P Bandyopadhyay – J. Chem. Sci., 128(9), 1507–1516, 2016.
  7. Low energy isomers of (H2O)25 from a hierarchical method based on Monte Carlo temperature basin paving and molecular tailoring approaches benchmarked by MP2 calculations.
    N Sahu, SR Gadre, A Rakshit, P Bandyopadhyay, E Miliordos, SS Xantheas – J. Chem. Phys., 141(16), 164304, 2014.
  8. Finding low energy minima of (H2O)25 and (H2O)30 with temperature basin paving Monte Carlo method with effective fragment potential: New ‘global minimum’ and graph theoretical characterization of low energy structures.
    A Rakshit, P Bandyopadhyay – Theoretical and Computational Chemistry, 1021, 206–214, 2013.
  9. Cooperative Roles of Charge Transfer and Dispersion Terms in Hydrogen-Bonded Networks of (H2O)n, n = 6, 11, and 16.
    S Iwata, P Bandyopadhyay, SS Xantheas – J. Phys. Chem. A, 117(30), 6641–6651, 2013.
  10. Facilitating Minima Search for Large Water Clusters at MP2 level via Molecular Tailoring.
    JP Furtado, AP Rahalkar, S Shanker, P Bandyopadhyay, SR Gadre – J. Phys. Chem. Lett., 3(16), 2253–2258, 2012.
  11. Monte Carlo Temperature Basin Paving with Effective Fragment Potential: An Efficient and Fast Method for Finding Low Energy Structures of Water Clusters (H2O)20 and (H2O)25.
    S Shanker, P Bandyopadhyay – J. Phys. Chem. A, 115(42), 11866–11875, 2011.
  12. Efficient conformational sampling by Monte Carlo Basin Paving method: Distribution of minima on the energy surface of (H2O)20 and (H2O)50.
    P Bandyopadhyay – Chem. Phys. Lett., 487, 133–138, 2010.

Enhanced Sampling and Wang-Landau Method

  1. Folding-unfolding transition in the mini-protein villin headpiece (HP35): An equilibrium study using the Wang-Landau algorithm.
    Priya Singh, S. K. Sarkar, P. Bandyopadhyay – Chem. Phys., 468, 1–8 (2016).
  2. Wang-Landau density of states based study of the folding-unfolding transition in the mini-protein Trp-cage (TC5b).
    Priya Singh, S. K. Sarkar, P. Bandyopadhyay – J. Chem. Phys., 141(1), 015103 (2014).
  3. Increasing the efficiency of Monte Carlo simulation with sampling from an approximate potential.
    Pradipta Bandyopadhyay – Chem. Phys. Lett., 556, 341–345 (2013).
  4. Accurate calculation of the Density of states near the ground state energy of the peptides Met-enkephalin and (Alanine)5 with the Wang-Landau method: Lessons learned.
    Priya Singh, Pradipta Bandyopadhyay – J. Atom. Mol. Opt. Phys., (2012).
  5. Understanding the applicability and limitations of Wang-Landau method for biomolecules: Met-enkephalin and Trp-cage.
    Priya Singh, Subir K. Sarkar, Pradipta Bandyopadhyay – Chem. Phys. Lett., 514(4), 357–361 (2011).
  6. Efficient conformational sampling by Monte Carlo Basin Paving method: Distribution of minima on the energy surface of (H2O)20 and (H2O)50.
    Pradipta Bandyopadhyay – Chem. Phys. Lett., 487, 133–138 (2010).
  7. Two surface Monte Carlo with Basin Hopping: quantum mechanical trajectory and multiple stationary points of water clusters.
    Pradipta Bandyopadhyay – J. Chem. Phys., 128(13), 134103 (2008).
  8. Accelerating QM/MM sampling using pure MM potential: the case of effective fragment potential.
    Pradipta Bandyopadhyay – J. Chem. Phys., 122, (1st March issue), 2005.

RNA

  1. How Mg2+ ion and water network affect the stability and structure of non-Watson-Crick base pairs in E. coli Loop E of 5S rRNA: A Molecular Dynamics and Reference Interaction Site Model (RISM) study.
    Sudhanshu Shanker, Pradipta Bandyopadhyay – Journal of Biomolecular Structure and Dynamics, 35(10), 2103–2122 (2016).
  2. Determination of low energy structures of a small RNA hairpin using Monte Carlo based techniques.
    Sudhanshu Shanker, Pradipta Bandyopadhyay – Journal of Biosciences, 37(3), 533–538 (2012).
  3. Monte Carlo Energy Landscape Paving and Basin Paving simulation of RNA T-loop hairpin.
    Pradipta Bandyopadhyay, Hungyo Kharerin – Chemical Physics Letters, 502, 130 (2011).

Application to Biological Systems

  1. A strategy to optimize the peptide-based inhibitors against different mutants of the spike protein of SARS-CoV-2.
    P Priya, A Basit, P Bandyopadhyay – Journal of Biomolecular Structure and Dynamics, 2022.
  2. Calculation of salt-dependent free energy of binding of β-lactoglobulin homodimer formation and mechanism of dimer formation using molecular dynamics simulation and three-dimensional reference interaction site model (3D-RISM): diffuse salt ions and non-polar interactions between the monomers favor the dimer formation.
    R Srivastava, M Chattopadhyaya, P Bandyopadhyay – Physical Chemistry Chemical Physics, 2020.
  3. Computational approach for molecular design using free energy contribution analysis.
    Toshio Asada, Pradipta Bandyopadhyay, Shiro Koseki – AIP Conference Proceedings, 2040(1), 020016 (2018).
  4. Free Energy Contribution Analysis Using Response Kernel Approximation: Insights into the Acylation Reaction of a Beta-Lactamase.
    Toshio Asada, Kanta Ando, Pradipta Bandyopadhyay, Shiro Koseki – J. Phys. Chem. B, 120(35), 9338–9346 (2016).
  5. Molecular dynamics simulations indicate that tyrosineB10 limits motions of distal histidine to regulate CO binding in soybean leghemoglobin.
    Smriti Sharma, Amit Kundu, Suman Kundu, Pradipta Bandyopadhyay – Proteins: Structure, Function, and Bioinformatics, 83(10), 1836–1848 (2015).
  6. Inter-chain hydrophobic clustering promotes rigidity in HIV-1 protease flap dynamics: New insights from Molecular Dynamics.
    Biswa Ranjan Meher, Mattaparthi Venkata Satish Kumar, Pradipta Bandyopadhyay – Journal of Biomolecular Structure & Dynamics, 32(6), 899–915 (2013).
  7. Conformational Dynamics of HIV-1 protease: a comparative molecular dynamics simulation study with multiple amber force fields.
    Biswa Ranjan Meher, Mattaparthi Venkata Satish Kumar, Smriti Sharma, Pradipta Bandyopadhyay – Journal of Bioinformatics and Computational Biology, 10(06), 1250018 (2012).
  8. Investigation of the acylation mechanism of class C beta-lactamase: pKa calculation, Molecular Dynamics simulation and quantum mechanical calculation.
    Smriti Sharma, Pradipta Bandyopadhyay – Journal of Molecular Modeling, 18(2), 481–492 (2012).
  9. Molecular Dynamics simulation of HIV-protease with polarizable and non-polarizable force fields.
    B. R. Meher, M. V. Satish Kumar, Pradipta Bandyopadhyay – Indian Journal of Physics, 83, 81 (2009).
  10. Drug resistance of HIV-1 Protease against JE-2147: I47V mutation investigated by molecular dynamics simulation.
    Pradipta Bandyopadhyay, B. R. Meher – Chemical Biology and Drug Design, 67(2), 155–161 (2006).

Others

  1. Computational approach for molecular design using free energy contribution analysis.
    T Asada, P Bandyopadhyay, S Koseki – AIP Conference Proceedings, 2018.
  2. Comparison of molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics-three-dimensional reference interaction site model (MM-3D-RISM) method to calculate the binding free energy of protein-ligand complexes: Effect of metal ion and advance statistical test.
    P Pandey, R Srivastava, P Bandyopadhyay – Chemical Physics Letters, 2018.
  3. Identification of inhibitors against α-Isopropylmalate Synthase of Mycobacterium tuberculosis using docking-MM/PBSA hybrid approach.
    Preeti Pandey, Andrew M Lynn, Pradipta Bandyopadhyay – Bioinformation, 13(5), 144–148 (2017).
  4. Computational investigation of kinetics of cross-linking reactions in proteins: importance in structure prediction.
    Pradipta Bandyopadhyay, Irwin D. Kuntz – Biopolymers, 91, 68 (2009). *Corresponding author.
  5. Riboswitch detection using profile hidden markov models.
    Payal Singh, Pradipta Bandyopadhyay, Sudha Bhattacharya, A Krishnamachari, Supratim Sengupta – BMC Bioinformatics, 10(1), 325 (2009).
  6. Partial Acetylation of Lysine Residues Improves Intra-Protein Cross-linking.
    Xin Gao, Pradipta Bandyopadhyay, Birgit Schilling, Malin M. Young, Naoaki Fujii, Tiba Aynechi, R. Kiplin Guy, Irwin D. Kuntz, Bradford W. Gibson – Analytical Chemistry, 80(4), 951–960 (2008).

Selected Publications from Ph.D. and Post-doc Days

  1. Direct Hydroxide attack is a plausible mechanism for amidase antibody 43C9.
    L. T. Chong, P. Bandyopadhyay, T. S. Scanlan, I. D. Kuntz, P. A. Kollman – J. Comp. Chem., 24, 1371 (2003).
  2. An integrated Effective Fragment-Polarizable Continuum approach to solvation: Theory and Application to Glycine.
    P. Bandyopadhyay, M. S. Gordon, B. Mennucci, J. Tomasi – J. Chem. Phys., 116, 5023 (2002).
  3. The effective fragment potential method: a QM-based MM approach to modeling environmental effects in chemistry.
    M. S. Gordon, M. Freitag, P. Bandyopadhyay, J. H. Jensen, V. Kairys, W. J. Stevens – J. Phys. Chem. A, 105, 293–307 (2001). (Feature Article)
  4. A combined discrete continuum solvation model: application to glycine.
    P. Bandyopadhyay, M. S. Gordon – J. Chem. Phys., 113, 1104 (2000).
  5. Ab initio Monte Carlo simulation using multicanonical algorithm: temperature dependence of the average structure of water dimer.
    P. Bandyopadhyay, S. Ten-no, S. Iwata – Molecular Physics, 96, 349 (1999).

Member

Meet the dedicated members of our research group, spanning from current scholars to past collaborators.

Principal Investigator

Dr. Pradipta Bandyopadhyay
Professor Pradipta Bandyopadhyay

Professor, School of Computational and Integrative Sciences, JNU

Curriculum Vitae (CV)
Google Scholar Profile
LinkedIn Profile

Professional Experience
  • Professor, Jawaharlal Nehru University (2014–present)
  • Associate Professor, JNU (2007–2014)
  • Assistant Professor, IIT Guwahati (2004–2007)
  • Scientist, ARQULE Inc., California (Sep–Dec 2002)
  • Postdoctoral Research, Lawrence Berkeley Lab & UCSF
  • Visiting Scientist, Stony Brook University (Jan–Mar 2020)
  • Visiting Scientist, University of Oklahoma (May–Oct 2019)
Education
  • Ph.D., Graduate University for Advanced Studies, Japan
  • M.Sc., Indian Institute of Technology, Kanpur
  • Postdoc, Iowa State University & University of California, San Francisco

Current Ph.D. Students

Nayaab
Nayaab Shahir Pandit

MSc in Microbiology and Bioinformatics, Jamia Millia Islamia, New Delhi

Docking

Anand
Anand Prakash Raw

M.Tech in Computer Science and Technology, SCSS, JNU, New Delhi

NA

Samir Rana
Samir Rana

M.Tech in Data Science, SCSS, JNU, New Delhi

Protein Ligand Binding Affinity Prediction Using Deep Learning.

Archana Bhati
Archana Bhati

Integrated M.Tech Biotechnology, Gautam Buddha University

MD Simulation of Protein.

Pooja
Pooja

M.Sc, Bioinformatics, M. D. University, Rohtak

Binding Affinity Predictions using physics-based and Machine Learning Approaches.

Abdul Basit
Abdul Basit

M.Sc, Biophysics, Jamia Millia Islamia, New Delhi

Calcium binding affinity of proteins using computational techniques.

Kankana Bhattacharjee
Kankana Bhattacharjee

Visiting Student, Ashoka University
Registered Supervisor: Dr. Aryya Ghosh
BS-MS in Chemistry, NIT Agartala

Molecular mechanism of EGFR-ligand binding via Free Energy Simulation.

Ramanujan Faculty

Dr. Hari O.S. Yadav
Dr. Hari O.S. Yadav

Affiliation: School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi

Postdoc: Theoretical and Computational Chemistry Lab, Department of Materials Chemistry, Nagoya University, Japan

Research Interests: Nanoparticle Self-Assembly, Hydrophobicity, Micelles, Membranes, Blood-compatible Polymers, Nanobiohybrid Drug Delivery Systems, Coarse-Grained Molecular Modelling.

Ph.D. / Post Doc Alumni

Angad Sharma
Dr. Angad Sharma

M.Sc, Physics, Delhi University

Thesis: Understanding the role of ions in modulating interaction in nucleic acids.

Ajeet Kumar Yadav
Dr. Ajeet Kumar Yadav

Ph.D., Computational & Systems Biology, JNU

Thesis: Hydration of polar and non-polar solutes – theory & simulation

Current: Postdoc, Nogoya University, Japan

Dr. Bina Kumari
Dr. Bina Kumari

M.Sc, Physics, SPS, JNU (Joint with Dr. Subir Sarkar)

Current: Assistant Professor, Brahmanand College, Kanpur

Dr. Rakesh Srivastava
Dr. Rakesh Srivastava

M.Tech, Computational & Systems Biology, JNU

Thesis: Theoretical and computational study of macromolecules under crowding

Current: Assistant Professor, VIT Bhopal

Dr. Avijit Rakshit
Dr. Avijit Rakshit

M.Sc, Chemistry, University of Kalyani

Thesis: Energy Landscape Exploration of Fluxional Molecules

Current: Chemical Assistant, Dept. of Revenue

Dr. Atul Kumar Jaiswal
Dr. Atul Kumar Jaiswal

M.Sc, Biotechnology, University of Allahabad (Joint with Dr. A. Krishnamachari)

Thesis: DNA-Solvent Interactions

Current: Assistant Professor, Jamia Hamdard

Dr. Preeti Pandey
Dr. Preeti Pandey

M.Sc, Bioinformatics, MMV, BHU, Varanasi (Joint with Prof. Andrew M. Lynn)

Thesis: Metabolic Pathway Analysis & Inhibitor Prediction of M. tuberculosis α-Isopropylmalate Synthase

Current: Postdoctoral Associate, Clemson University, USA

Dr. Sabeeha Hasnain
Dr. Sabeeha Hasnain

M.Tech Computational & Systems Biology, JNU; M.Sc Mathematics, BHU

Thesis: Random walk models for subdiffusion in crowded environments

Current: Assistant Professor, Mahindra University, Hyderabad

Dr. Sudhanshu Shanker
Dr. Sudhanshu Shanker

M.Sc, Bioinformatics, IIDS, University of Allahabad

Thesis: Algorithms to understand biomolecular structure and function

Current: Assistant Professor, Mahindra University, Hyderabad

Dr. Priya Singh
Dr. Priya Singh

M.Sc, Physics, Kanpur University (Joint with Prof. S. Sarkar, SPS, JNU)

Thesis: Enhanced simulation methods for protein folding

Smriti Sharma A.
Dr. Smriti Sharma A.

B.Pharm, GGSIP University, Delhi

Thesis: Beta-lactamase mechanism via MD, electrostatics, QM calculations

Umesh Chandra Roy
Dr. Umesh Chandra Roy

M.Sc, Physics, IITD; Ph.D. SPS, JNU (Dr. Subir Sarkar)

Postdoc, SCIS, JNU (Dr. Pradipta Bandyopadhyay)

Project: Water & biomolecules under high pressure

M.Tech/M.Sc. Current Students
M.Tech/M.Sc. Alumni
2023–25
  • Ruchika
2022–24
  • Tanya
2017–19
  • Sushil Shah
  • Dhara Awasthi
2016–18
  • Ajeet Kumar Yadav
  • Anuj Kumar Sirohi
2015–17
  • Jyoti Bhadana
2014–16
  • Ram Nayan Verma
2013–15
  • Mayank
2012–14
  • Rakesh Srivastava
  • Baniateilang Diengngan
2011–13
  • Rajan Shrivastava
  • Pawan Omar
2010–12
  • Navneet Chandra Verma
2009–11
  • Sabeeha Hasnain
2008–10
  • Hungyo Kharein

Present and Past Collaborators

  • Matt Jacobson (UCSF)
  • S. Xantheas (PNNL)
  • S. Gadre (Pune)
  • S. Iwata
  • T. Asada (Osaka Prefecture University)
  • Suman Kundu (Delhi University)
  • Subir Sarkar (Jawaharlal Nehru University)
  • Pinaki Gupta-Bhaya (HRI)
  • Ken A. Dill (Stony Brook University)
  • Evangelos A. Coutsias (Stony Brook University)
  • Tomaž Urbič (University of Ljubljana)
  • Manoj Munde (Jawaharlal Nehru University)

Contact

School of Computational and Integrative Sciences (SC&IS)

Jawaharlal Nehru University

New Delhi 110067, India.

Office: Cabin No.-16

Email: praban07@gmail.com

Phone: 011-26738704

Location: G5V9+2GX School of Computational And Integrative Sciences, Jawaharlal Nehru University, JNU Campus Rd, New Delhi, Delhi 110067

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