PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms

Abstract

AbstractBiomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins involved in biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by identifying amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder and outperforms previous methods. We performed extensive experimental validationin celluloand demonstrated that PICNIC accurately predicts 21 out of 24 condensate-forming proteins regardless of their structural disorder content. Even though increasing disorder content was associated with organismal complexity, we found no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we applied a novel machine learning classifier to interrogate condensate components at single protein and whole-proteome levels across the tree of life (picnic.cd-code.org).