Swarnadeep Seth

This paper introduces a platform for creating de novo autogenic engineered living materials using CsgA-like proteins from extremophilic bacteria, demonstrating tunable mechanical properties and programmable functionalities.

CGAgentX develops transferable coarse-grained models using an agentic AI framework with specialized agents that autonomously optimize parameters to match experimental and simulation data.

The paper designs CsgA variants with altered β-strand lengths to explore structural plasticity in microbial β-solenoids, revealing how strand length affects nanofiber stability and material properties.

This paper introduces a hybrid ML framework to predict conformational properties of IDPs using sequence and physical features, achieving improved accuracy and interpretability.

The paper uses deep learning to rapidly identify missense mutations in intrinsically disordered proteins that significantly alter their structure.