MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

Journal: IEEE journal of biomedical and health informatics
Published Date:

Abstract

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

Authors

  • Junkai Wang
  • Lijun Quan
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Zhi Jin
  • Hongjie Wu
    School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
  • Xuhao Ma
  • Xuejiao Wang
    School of Literature and Journalism, Sanjiang University, Nanjing, Jiangsu 210012, China.
  • Jingxin Xie
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Deng Pan
    Hefei National Laboratory for Physical Sciences at the Microscale, Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China.
  • Taoning Chen
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Tingfang Wu
    1 Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China.
  • Qiang Lyu
    Department of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.