ToGo-WF: prediction of RNA tertiary structures and RNA–RNA/protein interactions using the KNIME workflow
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.
KeywordsRNA 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).
- 20.De Leonardis E, Lutz B, Ratz S, Cocco S, Monasson R, Schug A, Weigt M (2015) Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction. Nucleic Acids Res 43:10444–10455Google Scholar
- 25.Cruz JA, Blanchet M-F, Boniecki M, Bujnicki JM, Chen S-J, Cao S, Das R, Ding F, Dokholyan NV, Flores SC, Huang L, Lavender Ca, Lisi V, Major F, Mikolajczak K, Patel DJ, Philips A, Puton T, Santalucia J, Sijenyi F, Hermann T, Rother K, Rother M, Serganov A, Skorupski M, Soltysinski T, Sripakdeevong P, Tuszynska I, Weeks KM, Waldsich C, Wildauer M, Leontis NB, Westhof E (2012) RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. RNA (New York, NY) 18:610–625CrossRefGoogle Scholar
- 38.Berthold MR, Cebron N, Dill F, Gabriel TR, Ktter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: The Konstanz Information Miner. Data Anal Mach Learn Appl:319–326Google Scholar
- 41.Trott O, Olson AJ (2010) Software news and update autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461Google Scholar
- 45.Case DABR, Cerutti DS, Cheatham TE, Darden TA, Duke RE, Giese TJ, Gohlke H, Goetz AW, Homeyer N, Izadi S, Janowski P, Kaus J, Kovalenko A, Lee TS, LeGrand S, Li P, Lin C, Luchko T, Luo R, Madej B, Mermelstein D, Merz KM, Monard G, Nguyen H, Nguyen HT, Omelyan I, Onufriev A, Roe DR, Roitberg A, Sagui C, Simmerling CL, Botello-Smith WM, Swails J, Walker RC, Wang J, Wolf RM, Wu X, Xiao L, Kollman PA (2016) AMBER 2016. University of California, San FranciscoGoogle Scholar
- 54.Nomura Y, Sugiyama S, Sakamoto T, Miyakawa S, Adachi H, Takano K, Murakami S, Inoue T, Mori Y, Nakamura Y, Matsumura H (2010) Conformational plasticity of RNA for target recognition as revealed by the 2.15 A crystal structure of a human IgG-aptamer complex. Nucleic Acids Res 38:7822–7829CrossRefGoogle Scholar
- 55.Adams D, Gonzalez-Duarte A, O’Riordan WD, Yang CC, Ueda M, Kristen AV, Tournev I, Schmidt HH, Coelho T, Berk JL, Lin KP, Vita G, Attarian S, Plante-Bordeneuve V, Mezei MM, Campistol JM, Buades J, Brannagan TH, Kim BJ, Oh J, Parman Y, Sekijima Y, Hawkins PN, Solomon SD, Polydefkis M, Dyck PJ, Gandhi PJ, Goyal S, Chen J, Strahs AL, Nochur SV, Sweetser MT, Garg PP, Vaishnaw AK, Gollob JA, Suhr OB (2018) Patisiran, an RNAi therapeutic, for hereditary transthyretin amyloidosis. New Engl J Med 379:11–21CrossRefGoogle Scholar