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LCS-TA to identify similar fragments in RNA 3D structures.

Jakub WiedemannTomasz ZokMaciej MilostanMarta Szachniuk
Published in: BMC bioinformatics (2017)
The presented approach ties torsion-angle-based method of structure analysis with the idea of local similarity identification by handling continuous 3D structure segments. The first method, implemented in MCQ4Structures, has been successfully utilized in RNA-Puzzles initiative. The second one, originally applied in Euclidean space, is a component of LGA (Local-Global Alignment) algorithm commonly used in assessing protein models submitted to CASP. This unique combination of concepts implemented in LCS-TA provides a new perspective on structure quality assessment in local and quantitative aspect. A series of computational experiments show the first results of applying our method to comparison of RNA 3D models. LCS-TA can be used for identifying strengths and weaknesses in the prediction of RNA tertiary structures.
Keyphrases
  • high resolution
  • nucleic acid
  • machine learning
  • deep learning
  • small molecule
  • binding protein
  • neural network