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.