RNA Secondary Structure Predictor

Predict RNA folding patterns from nucleotide sequences using thermodynamic models.

Prediction Method: Minimum free energy (MFE) algorithm based on thermodynamic parameters

Predicts the most stable RNA secondary structure by minimizing the free energy of folding

Enter RNA sequence (only A, U, G, C characters). Maximum length: 500 nucleotides.
Folding temperature (default: 37°C)
Select prediction algorithm
Predicting structure... This may take a moment.

Understanding RNA Secondary Structure

RNA secondary structure refers to the base-pairing interactions within an RNA molecule, which determine its three-dimensional shape and biological function.

RNA Structural Elements:

  • Stems: Double-stranded regions formed by complementary base pairing
  • Loops: Unpaired nucleotide regions that connect stems
  • Hairpins: Loop regions at the end of a stem
  • Bulges: Unpaired nucleotides on one strand within a stem
  • Internal Loops: Unpaired nucleotides on both strands within a stem

Base Pairing Rules

Base Pair Type Strength Frequency
G-C Watson-Crick Strong (3 H-bonds) Most stable
A-U Watson-Crick Medium (2 H-bonds) Common
G-U Wobble Weak (2 H-bonds) Less common

Prediction Algorithms

RNA secondary structure prediction algorithms use thermodynamic models to find the structure with the lowest free energy (most stable).

1

Minimum Free Energy (MFE): Finds the structure with the lowest free energy using dynamic programming

2

Partition Function: Calculates equilibrium probabilities of all possible structures

3

Centroid Structure: Finds the structure with maximum expected accuracy

Biological Significance

1

Ribozymes: Catalytic RNA molecules whose function depends on specific structures

2

Riboswitches: Regulatory elements that change structure in response to metabolites

3

microRNAs: Small RNAs that form hairpin structures during processing

4

Ribosomal RNA: Complex structures essential for protein synthesis

5

Viral RNA: Structures that regulate viral replication and gene expression

Applications

  • Functional RNA Discovery: Identifying non-coding RNAs with regulatory functions
  • Drug Design: Targeting RNA structures with small molecules
  • Vaccine Development: Designing RNA vaccines with optimized stability
  • Synthetic Biology: Engineering RNA devices and sensors
  • Evolutionary Studies: Comparing RNA structures across species

Limitations: Computational predictions have limitations. Experimental validation (e.g., SHAPE, chemical probing) is often needed for accurate structure determination. Prediction accuracy decreases with sequence length.

Frequently Asked Questions

Prediction accuracy varies but is generally around 70-80% for short sequences under optimal conditions. Accuracy depends on sequence length, GC content, and the presence of pseudoknots (which most algorithms cannot predict). Experimental data can significantly improve prediction accuracy.

Dot-bracket notation is a simple text representation of RNA secondary structure. Paired nucleotides are represented by matching parentheses '(' and ')', while unpaired nucleotides are represented by dots '.'. For example, "(((...)))" represents a three-base-pair stem with a three-nucleotide loop.

Minimum Free Energy (MFE) prediction assumes that RNA folds into a single structure with the lowest free energy. However, RNA molecules often exist as ensembles of structures. MFE also cannot predict pseudoknots (complex tertiary interactions) and may not account for kinetic trapping or co-transcriptional folding.

Temperature significantly affects RNA stability. Higher temperatures destabilize base pairs, leading to more open structures. Most RNA molecules function at physiological temperatures (37°C for humans). Some thermophilic organisms have RNAs with higher thermal stability due to increased GC content.

Standard prediction algorithms only handle standard nucleotides (A, U, G, C). Modified nucleotides (e.g., pseudouridine, m6A) have different base-pairing properties and are not accounted for in most prediction tools. Specialized algorithms or manual adjustments are needed for sequences with modified nucleotides.