Arginine Forks Are a Widespread Motif to Recognize Phosphate Backbones and Guanine Nucleobases in the RNA Major Groove

• Published in: Journal of the American Chemical Society 2020-11, Vol.142 (47), p.19835-19839
• Main Authors: Chavali, Sai Shashank, Cavender, Chapin E, Mathews, David H, Wedekind, Joseph E
• Format: Article
• Language: English
• Subjects: Arginine - chemistry; Arginine - metabolism; Binding Sites; Guanine - chemistry; Guanine - metabolism; HIV Long Terminal Repeat - genetics; HIV-1 - metabolism; Nucleic Acid Conformation; Phosphates - chemistry; Phosphates - metabolism; RNA, Viral - chemistry; RNA, Viral - metabolism; tat Gene Products, Human Immunodeficiency Virus - genetics; tat Gene Products, Human Immunodeficiency Virus - metabolism
• ISSN: 0002-7863; 1520-5126
• DOI: 10.1021/jacs.0c09689

RNA recognition by proteins is central to biology. Here we demonstrate the existence of a recurrent structural motif, the “arginine fork”, that codifies arginine readout of cognate backbone and guanine nucleobase interactions in a variety of protein–RNA complexes derived from viruses, metabolic enzymes, and ribosomes. Nearly 30 years ago, a theoretical arginine fork model was posited to account for the specificity between the HIV-1 Tat protein and TAR RNA. This model predicted that a single arginine should form four complementary contacts with nearby phosphates, yielding a two-pronged backbone readout. Recent high-resolution structures of TAR–protein complexes have unveiled new details, including (i) arginine interactions with the phosphate backbone and the major-groove edge of guanine and (ii) simultaneous cation−π contacts between the guanidinium group and flanking nucleobases. These findings prompted us to search for arginine forks within experimental protein–RNA structures retrieved from the Protein Data Bank. The results revealed four distinct classes of arginine forks that we have defined using a rigorous but flexible nomenclature. Examples are presented in the context of ribosomal and nonribosomal interfaces with analysis of arginine dihedral angles and structural (suite) classification of RNA targets. When arginine fork chemical recognition principles were applied to existing structures with unusual arginine–guanine recognition, we found that the arginine fork geometry was more consistent with the experimental data, suggesting the utility of fork classifications to improve structural models. Software to analyze arginine–RNA interactions has been made available to the community.