Login / Signup

Identification and Characterization of Glycine- and Arginine-Rich Motifs in Proteins by a Novel GAR Motif Finder Program.

Yi-Chun WangShang-Hsuan HuangChien-Ping ChangChuan Li
Published in: Genes (2023)
Glycine- and arginine-rich (GAR) motifs with different combinations of RG/RGG repeats are present in many proteins. The nucleolar rRNA 2'-O-methyltransferase fibrillarin (FBL) contains a conserved long N-terminal GAR domain with more than 10 RGG plus RG repeats separated by specific amino acids, mostly phenylanalines. We developed a GAR motif finder (GMF) program based on the features of the GAR domain of FBL. The G(0,3)-X(0,1)-R-G(1,2)-X(0,5)-G(0,2)-X(0,1)-R-G(1,2) pattern allows the accommodation of extra-long GAR motifs with continuous RG/RGG interrupted by polyglycine or other amino acids. The program has a graphic interface and can easily output the results as .csv and .txt files. We used GMF to show the characteristics of the long GAR domains in FBL and two other nucleolar proteins, nucleolin and GAR1. GMF analyses can illustrate the similarities and also differences between the long GAR domains in the three nucleolar proteins and motifs in other typical RG/RGG-repeat-containing proteins, specifically the FET family members FUS, EWS, and TAF15 in position, motif length, RG/RGG number, and amino acid composition. We also used GMF to analyze the human proteome and focused on the ones with at least 10 RGG plus RG repeats. We showed the classification of the long GAR motifs and their putative correlation with protein/RNA interactions and liquid-liquid phase separation. The GMF algorithm can facilitate further systematic analyses of the GAR motifs in proteins and proteomes.
Keyphrases
  • amino acid
  • quality improvement
  • machine learning
  • nitric oxide
  • deep learning
  • endothelial cells
  • transcription factor
  • pluripotent stem cells