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Singh, H. R.

Publications and source records attributed to Singh, H. R..

2 recordsLinked to original sources

Mesoscale landscaping of the TRIM protein family reveals a novel human condensatopathy

The mesoscale organization of cells is central to cellular physiology and pathology. Cellular condensates often form via biomolecular phase separation, mediated by intrinsically disordered regions (IDRs) and represent a key mechanism for mesoscale organization. The TRI-partite Motif (TRIM) family of ubiquitin ligases is implicated in diverse cellular functions and disease, yet the role of biomolecular condensation in TRIM family organization remains understudied. Here, we systematically investigate the mesoscale localization of 72 TRIM proteins, revealing that a majority form condensates in distinct cellular compartments. IDR content correlates with dynamic condensate formation, suggesting a critical role in mesoscale organization. Focusing on TRIM8, associated with a neuro-renal disorder, we demonstrate that disease-causing truncations of the TRIM8 C-terminal IDR result in a condensatopathy, characterized by disrupted condensation, proteasomal regulation, and TAK1/NF{kappa}B signaling. Functional assays in cellular and animal models link these disruptions to podocyte dysfunction and impaired response to injury. Our findings establish a framework for understanding condensatopathies and the mesoscale principles governing TRIM family organization and function.

cell biology↗

PICNIC identifies condensate-forming proteins across organisms

Biomolecular 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 validation in cellulo and 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).

bioinformatics↗