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Improving RNA nearest neighbor parameters for helices by going beyond the two-state model
Author(s) -
Aleksandar Spasic,
Kyle D. Berger,
Jonathan L. Chen,
Matthew G. Seetin,
Douglas H. Turner,
David H. Mathews
Publication year - 2018
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gky270
Subject(s) - k nearest neighbors algorithm , gibbs free energy , protein folding , entropy (arrow of time) , folding (dsp implementation) , physics , thermodynamics , statistical physics , computer science , artificial intelligence , nuclear magnetic resonance , electrical engineering , engineering
RNA folding free energy change nearest neighbor parameters are widely used to predict folding stabilities of secondary structures. They were determined by linear regression to datasets of optical melting experiments on small model systems. Traditionally, the optical melting experiments are analyzed assuming a two-state model, i.e. a structure is either complete or denatured. Experimental evidence, however, shows that structures exist in an ensemble of conformations. Partition functions calculated with existing nearest neighbor parameters predict that secondary structures can be partially denatured, which also directly conflicts with the two-state model. Here, a new approach for determining RNA nearest neighbor parameters is presented. Available optical melting data for 34 Watson-Crick helices were fit directly to a partition function model that allows an ensemble of conformations. Fitting parameters were the enthalpy and entropy changes for helix initiation, terminal AU pairs, stacks of Watson-Crick pairs and disordered internal loops. The resulting set of nearest neighbor parameters shows a 38.5% improvement in the sum of residuals in fitting the experimental melting curves compared to the current literature set.

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