Predicting the Flexibility Profile of Ribosomal RNAs

Abstract

Flexibility in biomolecules is an important determinant of biological functionality, which can be measured quantitatively by atomic Debye–Waller factor or B‐factor. Although numerous works have been addressed on theoretical and computational studies of the B‐factor profiles of proteins, the methods used for predicting B‐factor values of nucleic acids, especially the complicated ribosomal RNAs (rRNAs), which are very functionally similar to proteins in providing matrix structures and in catalyzing biochemical reactions, still remain unexploited. In this article, we present a quantitative structure–flexibility relationship (QSFR) study with the aim at the quantitative prediction of rRNA B‐factor based on primary sequences (sequence‐based) and advanced structures (structure‐based) by using both linear and nonlinear machine learning approaches, including partial least squares regression (PLS), least squares support vector machine (LSSVM), and Gaussian process (GP). By rigorously examining the performance and reliability of constructed statistical models and by comparing our models in detail to those developed previously for protein B‐factors, we demonstrate that (i) rRNA B‐factors could be predicted at a similar level of accuracy with that of protein, (ii) a structure‐based approach performed much better as compared to sequence‐based methods in modeling of rRNA B‐factors, and (iii) rRNA flexibility is primarily governed by the local features of nonbonding potential landscapes, such as electrostatic and van der Waals forces.

B‐factor profile prediction for RNA flexibility using support vector machines

“…Similar performance in cross validation and test sets and similar performance to other machine learning models (Table ) confirm the robustness of the performance obtained. The performance is also similar to the published performance for a structure‐based method in a prior work, which is limited to rRNA. Analysis of performance of our sequence‐based method indicates that it provides reasonable accuracy in predicting temperature‐ B factors of rRNA, tRNA and protein‐bound RNAs, for long chains in particular.…”

“…Unlike multiple methods developed for protein temperature B ‐factors, there is only one study that predicted ribosomal RNA B ‐factor profiles based on their sequence and structure information . The correlation coefficient between predicted and actual temperature‐ B ‐factor is 0.39 for the best sequence‐based method and 0.48 for the best structure‐based method.…”

The Universally Conserved ATPase YchF Regulates Translation of Leaderless mRNA in Response to Stress Conditions

“…This could explain why several ribosomal proteins were cross-linked. In addition, YchF is suggested to bind to nucleic acids (Teplyakov et al, 2003), and the flexibility of ribosomal RNA (rRNA) on the ribosomal surface (Tian et al, 2010) could also rationalize the observation that YchF is not cross-linked to a single ribosomal protein. Finally, YchF has been shown to dimerize (Hannemann et al, 2016) and a YchF(N20pBpa) dimer would cover a larger surface on the ribosome and would also allow simultaneous cross-links to two distant ribosomal partner proteins.…”

Structure-based characterization of the binding of peptide to the human endophilin-1 Src homology 3 domain using position-dependent noncovalent potential analysis

“…The formula of the additional hydrophobic potential is U hp ij ¼ ÀðS i r i þ S j r j Þe Àd ij [33], where d ij is the distance between two atoms i and j, ρ i represents the Eisenberg atomic solvation parameters [34], and S i is the atomic solvent accessible surface area defined in the MSMS program [35]. Detailed descriptions of the procedure for calculating these nonbonding interactions can be found in our previous publications (with only slight modifications) [36,37].…”