Protein–RNA interactions: structural biology and computational modeling techniques

Abstract

RNA-binding proteins are functionally diverse within cells, being involved in RNA-metabolism, translation, DNA damage repair, and gene regulation at both the transcriptional and post-transcriptional levels. Much has been learnt about their interactions with RNAs through structure determination techniques and computational modeling. This review gives an overview of the structural data currently available for protein-RNA complexes, and discusses the technical issues facing structural biologists working to solve their structures. The review focuses on three techniques used to solve the 3-dimensional structure of protein-RNA complexes at atomic resolution, namely X-ray crystallography, solution nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The review then focuses on the main computational modeling techniques that use these atomic resolution data: discussing the prediction of RNA-binding sites on unbound proteins, docking proteins, and RNAs, and modeling the molecular dynamics of the systems. In conclusion, the review looks at the future directions this field of research might take.

Zinc and Copper Ions Differentially Regulate Prion-Like Phase Separation Dynamics of Pan-Virus Nucleocapsid Biomolecular Condensates

“…PrLD-containing proteins are central to dynamic protein interaction network coordinating hubs [ 62 ]. Crystallization of protein–RNA complexes has historically been particularly difficult due to high conformational flexibility by PrLDs [ 63 ], these causing LLPS during protein crystallization, with no further characterization of resulting aggregates and gels generally considered to be disordered phases [ 64 ]. The lowering of free energy by liquid droplet formation during crystallization experiments should be considered in future BMC-targeting drug design [ 65 , 66 ].…”

Partner‐specific prediction of RNA‐binding residues in proteins: A critical assessment

“…Identifying the individual AAs and rNTs that form interfaces in protein‐RNA complexes is a crucial step in understanding the mechanisms of recognition in protein‐RNA interactions. Computational approaches, including statistical machine learning methods, for predicting RNA‐binding sites in RBPs are increasingly valuable because biophysical characterization of protein‐RNA complexes is difficult . Most existing methods for predicting RNA‐binding residues in RBPs use only the sequence and/or structural features of the RBP, without considering the sequence or structure of its putative RNA partner(s) even though RBPs such as Cas9 proteins, Pumilio and zinc finger proteins are known to contain modular RNA‐binding motifs or domains that recognize specific RNA sequence and/or structural features of their binding partners; Base‐specific hydrogen bonding, electrostatic interactions, and geometric factors influence binding specificity .…”

“…A major limitation in Table 8 difficult. 19 Most existing methods for predicting RNA-binding residues in RBPs use only the sequence and/or structural features of the RBP, without considering the sequence or structure of its putative RNA partner(s) even though RBPs such as Cas9 proteins, 11 Pumilio 12 and zinc finger proteins 13 are known to contain modular RNA-binding motifs or domains that recognize specific RNA sequence and/or structural features of their binding partners 10 ; Base-specific hydrogen bonding, 59 electrostatic interactions, and geometric factors influence binding specificity. 60 Reliable computational tools for predicting RNA binding residues in RBPs can both complement and help narrow the focus of biophysical and molecular genetic methods for identifying features critical for recognition in protein-RNA interactions.…”

“…Experimental methods for identifying protein‐RNA interfaces include methods for solving the structures of protein‐RNA complexes, for example, x‐ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and individual nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) . However, because biophysical methods for characterizing protein‐RNA interfaces are particularly challenging, there is an increasing reliance on computational methods, including in particular, statistical machine learning methods, for predicting RNA‐binding sites in RBPs . Most of the existing methods for predicting RNA‐binding sites of RBPs are partner‐agnostic in that they do not take into account the characteristics of the putative RNA binding partner .…”

Structural Changes of RNA in Complex with Proteins in the SRP

“…RNP complex formation also requires an intricate combination of interactions between the RNA and protein. Based on several structural studies using X-ray crystallography, nuclear magnetic resonance spectroscopy (NMR), and cryo electron microscopy (Cryo-EM), computational modeling (Patel et al, 2017 ; Pérez-Cano et al, 2017 ) and database analysis of the structures in the PDB (Jones et al, 2001 ; Treger and Westhof, 2001 ; Jeong et al, 2003 ; Ellis et al, 2007 ; Bahadur et al, 2008 ; Gupta and Gribskov, 2011 ; Jones, 2016 ) some relationship between RNAs and how they bind to protein have been identified. RNA and protein binding can be classified as either base-specific, protein side-chain specific or non-specific interactions (Jones et al, 2001 ; Treger and Westhof, 2001 ; Jeong et al, 2003 ; Gupta and Gribskov, 2011 ; Iwakiri et al, 2012 ; Pérez-Cano et al, 2017 ).…”