Journal of Computer-Aided Molecular Design

, Volume 33, Issue 5, pp 497–507 | Cite as

ToGo-WF: prediction of RNA tertiary structures and RNA–RNA/protein interactions using the KNIME workflow

  • Satoshi YamasakiEmail author
  • Takayuki Amemiya
  • Yukimitsu Yabuki
  • Katsuhisa Horimoto
  • Kazuhiko FukuiEmail author


Recent progress in molecular biology has revealed that many non-coding RNAs regulate gene expression or catalyze biochemical reactions in tumors, viruses and several other diseases. The tertiary structure of RNA molecules and RNA–RNA/protein interaction sites are of increasing importance as potential targets for new medicines that treat a broad array of human diseases. Current RNA drugs are split into two groups: antisense RNA molecules and aptamers. In this report, we present a novel workflow to predict RNA tertiary structures and RNA–RNA/protein interactions using the KNIME environment, which enabled us to assemble a combination of RNA-related analytical tools and databases. In this study, three analytical workflows for comprehensive structural analysis of RNA are introduced: (1) prediction of the tertiary structure of RNA; (2) prediction of the structure of RNA–RNA complexes and analysis of their interactions; and (3) prediction of the structure of RNA–protein complexes and analysis of their interactions. In an RNA–protein case study, we modeled the tertiary structure of pegaptanib, an aptamer drug, and performed docking calculations of the pegaptanib-vascular endothelial growth factor complex using a fragment of the interaction site of the aptamer. We also present molecular dynamics simulations of the RNA–protein complex to evaluate the affinity of the complex by mutating bases at the interaction interface. The results provide valuable information for designing novel features of aptamer-protein complexes.


RNA RNA–protein Tertiary structure Workflow Aptamer Nucleic acid drug 



K.F. thanks Mr. Hiroshi Kouno for searching the literature of nucleic acid-based drugs to construct the database and docking simulations. This research was partially supported by the Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number JP17am0101001. The workflow was initially developed as a part of the Life-Science Database Integration Project: Core Technology Development Program at the Japan Science and Technology Agency (JST).

Supplementary material

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Molecular Profiling for Drug Discovery Research Center (molprof)National Institute of Advanced Industrial Science and Technology (AIST)TokyoJapan
  2. 2.Laboratory of Molecular Medicine, Human Genome CenterThe Institute of Medical Science, The University of Tokyo (IMSUT)TokyoJapan
  3. 3.IMSBIO Co., LtdTokyoJapan

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