Predicting the Flexibility Profile of Ribosomal RNAs

RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features

The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.

RNA Flexibility Prediction With Sequence Profile and Predicted Solvent Accessibility

Structural flexibility plays an essential role in many biological processes. B-factor is an important indicator to measure the flexibility of protein or RNA structures. Many methods were developed to predict protein B-factors, but few studies have been done for RNA B-factor prediction. In this paper, we proposed a new method RNAbval to predict RNA B-factors using random forest. The method was developed using a comprehensive set of features, including the sequence profile and predicted solvent accessibility. RNAbval achieved an improvement of 9.2-20.5 percent over the state-of-the-art method on two benchmark test datasets. The proposed method is available at http://yanglab.nankai.edu.cn/RNAbval/.

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

The universally conserved P-loop GTPases control diverse cellular processes, like signal transduction, ribosome assembly, cell motility, and intracellular transport and translation. YchF belongs to the Obg-family of P-loop GTPases and is one of the least characterized member of this family. It is unique because it preferentially hydrolyses ATP rather than GTP, but its physiological role is largely unknown. Studies in different organisms including humans suggest a possible role of YchF in regulating the cellular adaptation to stress conditions. In the current study, we explored the role of YchF in the model organism Escherichia coli. By western blot and promoter fusion experiments, we demonstrate that YchF levels decrease during stress conditions or when cells enter stationary phase. The decline in YchF levels trigger increased stress resistance and cells lacking YchF are resistant to multiple stress conditions, like oxidative stress, replication stress, or translational stress. By in vivo site directed cross-linking we demonstrate that YchF interacts with the translation initiation factor 3 (IF3) and with multiple ribosomal proteins at the surface of the small ribosomal subunit. The absence of YchF enhances the anti-association activity of IF3, stimulates the translation of leaderless mRNAs, and increases the resistance against the endoribonuclease MazF, which generates leaderless mRNAs during stress conditions. In summary, our data identify YchF as a stress-responsive regulator of leaderless mRNA translation.