Kuntal Ghosh - Research

Mechanistic Insights into Reverse Transcription–Driven HIV-1 Capsid Uncoating

One of the key events in the HIV-1 life cycle is reverse transcription, during which single-stranded RNA (ssRNA) is converted into double-stranded DNA (dsDNA). This process occurs inside the viral capsid and, once it reaches a critical threshold, drives capsid rupture or uncoating. Uncoating is essential for infection because it releases viral genetic material into the host cell. Despite its importance, many mechanistic details of this process remain poorly understood. To address this, we develop a method for simulating reverse transcription inside the capsid, termed Coarse-Grained Kinetic Monte Carlo (CG-KMC). CG-KMC stochastically adds deoxynucleotide triphosphates (dNTPs) to the RNA, enabling stepwise growth of the DNA. We implement this method within an integrative coarse-grained (CG) framework that combines a “bottom-up” capsid model with a “top-down” representation of the viral RNA/DNA genome. Using this approach, our simulations phenomenologically capture capsid rupture as reverse transcription progresses. They predict a wide array of uncoating pathways, and the resulting ruptured structures show excellent agreement with previously published cryo-ET images. We further perform an extensive analysis of the uncoating process, examining its mechanistic and kinetic aspects as well as the role of capsid–DNA interactions.

NNP/CG-MM: Systematic Embedding of All-Atom Neural Network Potentials in a Coarse-Grained Environment

Neural network potentials (NNPs), or neural network-based force fields, are gaining widespread attention for their ability to model complex chemical, materials, and biophysical systems. In NNPs, the total energy of the system can be decomposed into atom-centered components, where the energies and forces are described using a deep neural network. However, despite the flexibility and accuracy of such NNPs, they are typically more computationally intensive than classical molecular dynamics. There have been recent advancements in embedding NNPs into a molecular mechanics (MM)-based environment to maximize efficiency while retaining the accuracy of NNPs. In this work, we propose a method called NNP/CG-MM, in which an NN force field is systematically embedded into a coarse-grained environment (CG-MM). Coarse-graining (CG) involves constructing a simplified representation of a larger fine-grained (FG) system with the goal of significantly accelerating computations while maintaining the accuracy of the FG system. The NNP-CG coupling terms are constructed using the multiscale CG force-matching method. Our scheme is tested on simple liquids and in capturing the hydrophobic effect, where three-body effects in the CG solvent play a critical role.

Systematic Embedding of All-Atom Reactive Molecular Dynamics into a Coarse-Grained Environment

Quantum mechanics/molecular mechanics (QM/MM) simulations are widely used for modeling chemical reactivity in complex environments. In the QM/MM method, the reactive center under study is typically treated using explicit quantum chemical calculations, while the nonreactive environment is treated using classical molecular mechanics. However, for large systems, even with the use of classical force fields for the MM part, QM/MM simulations are very computationally costly, particularly for systems with complex MM dynamics. In this work, we propose a much faster alternative method for studying reactivity denoted by multiscale reactive molecular dynamics/coarse-grained molecular mechanics (MS-RMD/CG-MM). MS-RMD has been shown to be a powerful method for modeling reactions involving proton transport and in principle other reactions. This all-atom reactive MD model, systematically parametrized from constrained DFT calculations, is embedded in a CG environment in this work, where the CG force fields are derived using the multiscale coarse-graining (MS-CG) method. We apply this scheme to organic SN2 reactions in a CG polar solvent (acetone) as examples.

Paper: J. Chem. Theory Comput. 2025, 21, 17, 8456–8467

Can a coarse-grained water model capture the key physical features of the hydrophobic effect?

Coarse-grained (CG) molecular dynamics can be a powerful method for probing complex processes. However, most CG force fields use pairwise nonbonded interaction potentials sets, which can limit their ability to capture complex multi-body phenomena such as the hydrophobic effect. As the hydrophobic effect primarily manifests itself due to the nonpolar solute affecting the nearby hydrogen bonding network in water, capturing such effects using a simple one CG site or “bead” water model is a challenge. In this work, we systematically test the ability of CG one site water models for capturing critical features of the solvent environment around a hydrophobe as well as the potential of mean force (PMF) of neopentane association. We study two bottom-up models: a simple pairwise (SP) force-matched water model constructed using the multiscale coarse-graining method and the Bottom-Up Many-Body Projected Water (BUMPer) model, which has implicit three-body correlations. We also test the top-down monatomic (mW) and the Machine Learned mW (ML-mW) water models. The mW models perform well in capturing structural correlations but not the energetics of the PMF. BUMPer outperforms SP in capturing structural correlations and also gives an accurate PMF in contrast to the two mW models. Our study highlights the importance of including three-body interactions in CG water models, either explicitly or implicitly, while in general highlighting the applicability of bottom-up CG water models for studying hydrophobic effects in a quantitative fashion. This assertion comes with a caveat, however, regarding the accuracy of the enthalpy–entropy decomposition of the PMF of hydrophobe association.

Paper: J. Chem. Phys. 159, 224105 (2023)

Structure elucidation and construction of isomerisation pathways in small to moderate-sized (6–27) MgO nanoclusters: an adaptive mutation simulated annealing based analysis with quantum chemical calculations

Determination of global minimum structures and elucidation of reaction paths or minimum energy paths between low-lying minima are of great chemical importance. To that end, we have used our own Adaptive Mutation Simulated Annealing method to determine the global minimum and the minimum energy paths for various isomerisation reactions for small to moderate-sized (MgO)n (n = 6–27) clusters, using the Born–Mayer potential with suitable parameter values. The minimum energy structures obtained by us match well with previously reported data and are used as guess structures for further optimisation at the DFT level (using the B3LYP functional and DGDZVP basis set). Our optimised structures are found to match very well with the further DFT optimised structures, where the comparison is done by determining the root mean square deviation values as well as the radial distribution function profiles. A scheme is proposed to determine the minimum energy paths for isomerisation reactions for some cluster sizes where the transition state/s obtained by us, at very low computational cost, match well with those obtained from further optimisation using DFT calculations. We have shown the efficacy of our method in determining the reaction pathways, even for cases that involve multi-step reactions.

Paper: Phys. Chem. Chem. Phys., 2020,22, 9616-9629