Krishna Shrinivas

Physics-guided design of intrinsically disordered proteins

This paper designs intrinsically disordered proteins using a physics-guided machine learning framework to encode complex signal processing and condensate properties.

Generalized design of sequence-ensemble-function relationships for intrinsically disordered proteins

This paper introduces a computational framework for designing intrinsically disordered proteins by inverting molecular simulations to create sequence-ensemble-function relationships.

Immiscible proteins compete for RNA binding to order condensate layers

The paper shows that core and shell proteins in paraspeckles compete for RNA binding, leading to layered condensate organization through immiscibility and competitive binding.

Enhancer features that drive formation of transcriptional condensates

This study identifies enhancer features that drive transcriptional condensate formation through phase separation mechanisms involving TF binding site valency, density, and affinity.