Protein secondary structure prediction

What is protein secondary structure prediction?

Protein secondary structure prediction is the computational process of assigning local structural states—commonly alpha helix, beta strand, and coil—to residues from an amino-acid sequence. It differs from full three-dimensional structure prediction, while DSSP assigns secondary structure after coordinates already exist.

Sequence predictors estimate local backbone organization rather than atomic coordinates. Their state alphabets, confidence values, and treatment of turns differ, so reported helix and strand fractions are method-dependent.

This workflow keeps sequence-based prediction separate from coordinate-derived assignment: Chou–Fasman predicts from sequence, ESMFold supplies a three-dimensional hypothesis, and DSSP assigns states from that model for comparison.

When to use protein secondary structure prediction

• Rapid sequence annotation is needed. Use local-state predictions for construct planning and fold-context checks.
• A proposed fold needs comparison. Compare sequence-derived states with assignments from a coordinate model.
• Residue boundaries matter. Inspect helix and strand transitions rather than only global percentages.

Benefits of protein secondary structure prediction

• Fast residue-level annotation. It supplies local structural context before a full structure exists.
• Sequence and coordinate evidence can be compared. The two branches use different inputs.
• Output is easy to inspect. State assignments can be reviewed position by position.

Primary limitations

• Tertiary contacts are not defined. Local states do not give complete coordinates.
• State alphabets differ. Methods can label turns and coils differently.
• Coordinate assignments inherit model error. A predicted structure can be wrong even when DSSP assignment is internally consistent.

Prediction and coordinate assignment

Propensity methods such as Chou–Fasman are interpretable baselines, while modern predictors learn sequence context from large protein datasets. Neither is a complete structural model because distant contacts fall outside the local-state output.

DSSP is not a sequence predictor: it calculates hydrogen-bond and backbone patterns from coordinates. Comparing it with sequence-based prediction is useful because the evidence differs.

How to run protein secondary structure prediction online

1. Prepare one protein sequence. Remove non-protein symbols and unresolved gaps.
2. Run Chou–Fasman. Inspect residue-level helix, sheet, turn, and coil propensities.
3. Generate a comparison model. Run ESMFold from the same sequence.
4. Assign coordinate-derived states. Pass the PDB to DSSP.
5. Review disagreement. Compare boundaries, low-confidence regions, and overall composition before export.

How protein secondary structure prediction works

Run Chou–Fasman from sequence, predict a 3D model with ESMFold, and assign its secondary structure with DSSP.

1. Validate the sequence. Paste a protein sequence and remove non-protein symbols or unresolved gaps.
2. Predict local states. Run Chou–Fasman to obtain residue-level helix, sheet, turn, and coil propensities.
3. Generate a 3D hypothesis. Run ESMFold from the same sequence to create an independent three-dimensional model.
4. Assign states from coordinates. Pass the ESMFold PDB to DSSP for coordinate-based secondary-structure assignment.
5. Review disagreements. Compare boundaries, low-confidence regions, and overall composition before exporting results.

Inputs and outputs

Check formats before running, then inspect and download the result from every workflow step.

Inputs

Research input

FASTAPDBmmCIF

One clean protein sequence with an unambiguous residue order.

Outputs

Prediction and review outputs

PDBmmCIFCSVJSON

Per-residue secondary-structure predictions, a predicted PDB structure, and DSSP helix, strand, turn, and coil assignments.