RNAeval calculates the free energy of an RNA secondary structure given a sequence. Unlike structure prediction tools that find the optimal fold, RNAeval evaluates whether a proposed or known structure is thermodynamically favorable for a specific sequence.
This makes RNAeval essential for validating experimental structures, comparing alternative conformations, and verifying that designed sequences actually fold into their intended structures. The tool uses the same nearest-neighbor thermodynamic model as RNAfold, ensuring consistent energy calculations across the ViennaRNA suite.
How does RNAeval work?
RNAeval decomposes an RNA structure into its constituent loops and sums their energy contributions using experimentally measured nearest-neighbor parameters. Each structural element (stacked base pairs, hairpin loops, internal loops, bulges, and multi-branch loops) contributes to the total free energy based on its sequence context.
Nearest-neighbor model
The nearest-neighbor model treats RNA stability as the sum of contributions from adjacent base pair stacks rather than individual pairs. A GC pair followed by an AU pair has different stability than two isolated pairs because stacking interactions between adjacent bases contribute significantly to the overall energy.
The default parameters come from the Turner 2004 dataset, derived from optical melting experiments on synthetic RNA oligomers. These parameters capture:
Base pair stacking: Energy from adjacent Watson-Crick or wobble pairs
Loop penalties: Entropic cost of closing hairpin, internal, and bulge loops
Dangling ends: Stabilization from unpaired nucleotides adjacent to helices
Terminal mismatches: Contributions from unpaired bases in loop closures
Temperature dependence
Free energy varies with temperature according to thermodynamic principles. At the default 37 degrees C (physiological temperature), the Turner parameters directly apply. At other temperatures, RNAeval rescales enthalpy and entropy contributions to compute adjusted free energies.
Higher temperatures destabilize structures (less negative free energy) because the entropic penalty of ordered structures becomes more significant. Lower temperatures favor more stable, ordered conformations.
How to use RNAeval online
Provide an RNA sequence and a secondary structure in dot-bracket notation, and RNAeval returns the free energy of that specific fold. This is useful for comparing alternative conformations, validating designed sequences, or checking whether an experimentally determined structure is thermodynamically favorable.
Inputs
Input
Description
RNA Sequences
RNA sequence in FASTA format or plain text. Supports A, U, G, C nucleotides.
Secondary Structure
Bracket notation representing the structure. Matching parentheses indicate base pairs; dots indicate unpaired positions.
The structure must match the sequence length exactly. Each opening parenthesis pairs with the corresponding closing parenthesis, counting from left to right. For example, (((...))) describes a hairpin with a 3-bp stem and 3-nt loop.
Settings
Setting
Description
Temperature
Folding temperature in degrees Celsius (0-100, default 37). Affects free energy calculations through thermodynamic rescaling.
Results
RNAeval returns the calculated free energy in kcal/mol for each sequence-structure pair.
Output
Description
Sequence ID
Identifier from FASTA header or auto-generated
Length
Number of nucleotides
Structure
The evaluated secondary structure
Energy
Free energy in kcal/mol. More negative values indicate more stable structures.
Interpreting energy values
Energy (kcal/mol)
Interpretation
< -20
Very stable structure, typical for longer RNAs with extensive base pairing
-10 to -20
Moderately stable, common for functional RNA elements
-5 to -10
Weakly stable, may sample alternative conformations
> -5
Marginally stable, structure may not persist at physiological conditions
These thresholds are approximate and depend heavily on sequence length. A 100-nt RNA with -15 kcal/mol is less stable per nucleotide than a 30-nt RNA with the same energy.
Applications
Structure validation
After predicting structures with RNAfold or obtaining them experimentally, RNAeval confirms whether the sequence energetically supports that fold. Large energy differences between predicted and evaluated structures suggest the sequence may not stably adopt the proposed conformation.
Comparing conformations
RNA molecules often exist in equilibrium between multiple structures. By evaluating different conformations for the same sequence, researchers can estimate population distributions. The Boltzmann distribution relates energy differences to relative populations:
N2N1=e
A 1.4 kcal/mol difference at 37 degrees C corresponds to roughly a 10-fold population difference.
Validating designed sequences
After using RNAinverse to design sequences for target structures, RNAeval verifies the designed sequence actually has favorable energy for the intended fold. Combined with RNAfold to check the minimum free energy structure, this confirms successful design.
Limitations
RNAeval assumes the provided structure is valid and evaluable. It does not check for impossible base pairs (like two positions too close to pair) or validate that the structure represents a physically realizable fold.
The nearest-neighbor model has known limitations for non-canonical interactions, modified nucleotides, and certain structural motifs. Predictions for structures with extensive non-Watson-Crick pairs may be less accurate.
Energy calculations do not account for cellular context, ionic conditions beyond standard assumptions, or interactions with proteins or other molecules that may stabilize or destabilize specific conformations.
