Jagannath Mondal

This study shows that pneumolysin, a bacterial toxin, can damage target cell membranes through vesicle fusion rather than pore formation.

This study identifies a conformational switch in the catalytic lysine of DERA that governs its enantioselectivity, revealing a mechanism for stereochemical outcome in aldol addition.

This study shows that crowder shape affects Sup35NM conformation and phase separation, with rod-like crowders promoting phase separation while spherical ones do not.

The paper fine-tunes a protein language model for PTM-site prediction and shows how model size, annotation depth, and modification chemistry affect PTM recognition.

This study compares diffusion and rectified-flow models in generating molecular conformations, showing diffusion models better capture ensemble structure while rectified-flow requires more expressive architectures.

This paper introduces a Transformer-based framework to predict full molecular dynamics trajectories from limited initial data, showing it captures long-range temporal structures and improves sampling compared to traditional methods.

The paper introduces a dataset and a graph-transformer model to predict IDR-protein binding affinity, achieving high accuracy and generalizing to designed binders.

This paper introduces a GAN-based method to generate molecular dynamics trajectories with kinetic fidelity, enabling efficient sampling of rare events in complex systems.

This paper develops a reaction-diffusion model to simulate liquid-liquid phase separation, showing how diffusivity and interactions affect droplet formation and stability.

This study evaluates the performance of a generative diffusion model in sampling protein conformations across different sizes and structures, showing it can reproduce key features but has limitations in capturing low-populated conformers.

This paper develops a method using AutoEncoders to compress Molecular Dynamics trajectories, achieving 98% space reduction while maintaining accurate reconstruction of atom coordinates.

This paper introduces an optimal annealing method for VAEs to improve protein conformational dynamics modeling by selecting the optimal β parameter using FVE scores.

This paper identifies the I-helix as the central regulator of allostery in Cytochrome P450cam, showing its conformational flexibility and role in substrate binding and channel dynamics.

This paper shows that a transformer model can predict future states of physicochemical systems faster than traditional simulations by learning from time-discretized molecular dynamics data.

This paper shows that enantiomeric histidine-rich peptides form coacervates that enhance antigen delivery to T cells, improving immune response.

This paper introduces URF, an unsupervised random forest method for learning protein conformational representations without prior labels, showing it performs as well as or better than existing methods.

This study shows that protein synthesis, particularly ribosome dynamics, enhances fluidity in bacterial cytoplasm by counteracting its glassy nature through metabolic activity.

This study computationally simulates how microenvironmental factors influence Aβ40's liquid-liquid phase separation, revealing salt-driven reentrant condensation and temperature-dependent behavior.

This study uses MD simulations and machine learning to reveal a two-stage binding mechanism of Hsp90 inhibitor Geldanamycin, showing stabilization of the loop-out conformation and involvement of both conformational selection and induced fit.

This study uses machine learning to show that fasudil modulates α-synuclein's conformational states through entropic expansion, offering insights into small molecule interactions with IDPs.

The paper develops a polymer physics model to simulate 3D chromatin architecture dynamics in developing lymphoid cells, revealing structural changes linked to gene regulation and cell fate.

This paper uses GPT-based models to generate protein sequences prone to liquid-liquid phase separation and validates their ability to form phase-separated states.

This study shows ATP prevents protein aggregation and dissolves pre-formed aggregates, suggesting it could be a therapeutic agent for protein-aggregation diseases.