Palash Bera

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

This study uses agent-based modeling to show how morphology and motility differences between bacterial species affect their spatial organization and collective behavior in mixed populations.

This paper uses machine learning to analyze Hi-C data from E. Coli, revealing structural patterns and predicting DNA dynamics, showing that a subset of contact pairs suffices for accurate predictions.

This study shows ribosomes in E. coli are not entirely excluded from the nucleoid, with some located in a central void, suggesting dynamic localization for efficient translation.