TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology

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, insta-bility 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 incorpo-rates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological net-works under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly uti-lized 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 thera-peutic 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.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).

Automated summary

Recent advances in RNA engineering have led to the development of RNA-based therapeutics for various applications. However, existing target screening tools often overlook noncoding RNAs and their regulatory relationships relevant to diseases. To address this, researchers have built the TREAT platform, which incorporates a large amount of regulatory relationships between coding and noncoding genes under different physiological conditions. TREAT utilizes graph representation learning with Random Walk Diffusions for disease-relevant target screening and offers design and optimization options for large RNAs or interfering RNAs.