RNA Flexibility Prediction With Sequence Profile and Predicted Solvent Accessibility

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 18; no. 5; pp. 2017 - 2022
• Main Authors: Wei, Hong, Wang, Boling, Yang, Jianyi, Gao, Jianzhao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accessibility; Biological activity; Computational Biology - methods; Crystallography, X-Ray; Flexibility; Forestry; Machine Learning; Nucleotide sequence; Pliability; Predictions; Protein B; Protein structure; Proteins; Proteins - chemistry; Proteins - metabolism; random forest; Ribonucleic acid; RNA; RNA - chemistry; RNA - genetics; RNA - metabolism; RNA B-factor; RNA solvent accessibility; Sequence Analysis, RNA - methods; Solvents; Solvents - chemistry; Solvents - metabolism; Support vector machines; Training
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2019.2956496

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/ .