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A pipeline for computational design of novel RNA-like topologies.
Jain, Swati; Laederach, Alain; Ramos, Silvia B V; Schlick, Tamar.
Affiliation
  • Jain S; Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA.
  • Laederach A; Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Ramos SBV; Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
  • Schlick T; Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA.
Nucleic Acids Res ; 46(14): 7040-7051, 2018 08 21.
Article in En | MEDLINE | ID: mdl-30137633
ABSTRACT
Designing novel RNA topologies is a challenge, with important therapeutic and industrial applications. We describe a computational pipeline for design of novel RNA topologies based on our coarse-grained RNA-As-Graphs (RAG) framework. RAG represents RNA structures as tree graphs and describes RNA secondary (2D) structure topologies (currently up to 13 vertices, ≈260 nucleotides). We have previously identified novel graph topologies that are RNA-like among these. Here we describe a systematic design pipeline and illustrate design for six broad design problems using recently developed tools for graph-partitioning and fragment assembly (F-RAG). Following partitioning of the target graph, corresponding atomic fragments from our RAG-3D database are combined using F-RAG, and the candidate atomic models are scored using a knowledge-based potential developed for 3D structure prediction. The sequences of the top scoring models are screened further using available tools for 2D structure prediction. The results indicate that our modular approach based on RNA-like topologies rather than specific 2D structures allows for greater flexibility in the design process, and generates a large number of candidate sequences quickly. Experimental structure probing using SHAPE-MaP for two sequences agree with our predictions and suggest that our combined tools yield excellent candidates for further sequence and experimental screening.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: RNA / Computer-Aided Design / Computational Biology / Nucleic Acid Conformation Type of study: Prognostic_studies Language: En Year: 2018 Type: Article

Full text: 1 Database: MEDLINE Main subject: RNA / Computer-Aided Design / Computational Biology / Nucleic Acid Conformation Type of study: Prognostic_studies Language: En Year: 2018 Type: Article