#FUS PROTEIN SCAFFOLD AND CLIENT PSP#

Each of these tools is based on specific protein features that are deemed to be driving forces behind LLPS.

#FUS PROTEIN SCAFFOLD AND CLIENT PSP#

Development of computational methods to predict PSPs is therefore of great importance for deeper understanding the biological function of LLPS.Ī recent review summarized a range of first-generation PSP prediction tools. Although tremendous progress has been made in understanding protein LLPS, the knowledge of prevalence and distribution of phase separation proteins (PSPs), or specifically “scaffolds”, is still lacking. Generally, phase separation-related proteins can be categorized as scaffolds that drive LLPS or as clients that integrate into the condensates formed by scaffolds. LLPS in biology is deemed to be fundamentally driven by multivalent interactions between molecules, which can occur in proteins between multiple folded domains or are mediated by intrinsically disordered regions (IDRs). Studies also indicate that the LLPS of proteins and the formation of biomolecular condensates may be regulated by RNA. Notably, several proteins are observed to form liquid-like membrane-less assemblies both in vitro and in vivo. More and more studies suggest that many cellular metabolic processes are regulated by LLPS, so are some intractable diseases such as ALS (amyotrophic lateral sclerosis) and AD (Alzheimer disease). However, membrane-less organelles, also called biomolecular condensates, have recently been recognized to compartmentalize cellular space through liquid–liquid phase separation (LLPS). Studies on cellular compartments have traditionally been focused on membrane-bound organelles such as endoplasmic reticulum. The compartmentalization of molecules in the cytoplasm is critical for efficient and precise biochemical reactions in eukaryotic cells. For further applications, we built a user-friendly PSPredictor web server ( ), which provides valuable information for potential PSPs recognition. PSPredictor could identifie novel scaffold proteins for stress granules and predict PSPs candidates in the human genome for further study. For further applications, we built a user-friendly PSPredictor web server ( ), which is accessible for prediction of potential PSPs. The proposed method achieves a tenfold cross-validation accuracy of 94.71%, and outperforms previously reported PSPs prediction tools. Our method combines the componential and sequential information during the protein embedding stage, and, adopts the machine learning algorithm for final predicting. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS.īased on the PSPs collected in LLPSDB, we developed a sequence-based prediction tool for LLPS proteins (PSPredictor), which is an attempt at general purpose of PSP prediction that does not depend on specific protein types. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Dysregulation of LLPS is implicated in a number of diseases. The liquid–liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function.

Fus protein scaffold and client