TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology

Yufan Luo; Liu Liu; Zihao He; Shanshan Zhang; Peipei Huo; Zhihao Wang; Qin Jiaxin; Lianhe Zhao; Yang Wu; Dongdong Zhang; Dechao Bu; Runsheng Chen; Yi Zhao

doi:10.1016/j.csbj.2022.10.011

TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology

Comput Struct Biotechnol J. 2022 Oct 8:20:5680-5689. doi: 10.1016/j.csbj.2022.10.011. eCollection 2022.

Authors

Yufan Luo^{1

2}, Liu Liu¹, Zihao He¹, Shanshan Zhang³, Peipei Huo³, Zhihao Wang³, Qin Jiaxin¹, Lianhe Zhao¹, Yang Wu¹, Dongdong Zhang⁴, Dechao Bu^{1

5}, Runsheng Chen^{4

6}, Yi Zhao¹

Affiliations

¹ Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
² University of Chinese Academy of Sciences, Beijing 100049, China.
³ Luoyang Zhongke Information Industry Research Institute, Luoyang, China.
⁴ Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
⁵ Hwa Mei Hospital, University of Chinese Academy of Sciences, China.
⁶ Shenzhen Institute of Nucleic Acid Drug Research, Shenzhen Bay Laboratory Pingshan Translational Medicine Center, Shenzhen 510800, China.

Abstract

Recent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.

Keywords: Noncoding; PPI, Protein protein interaction; RNA; TF, Transcript Factor; Therapeutics; mRNA, messenger RNA; ncRNA, Noncoding RNA; siRNA, small interfering RNA.