# Protein secondary structure prediction Predict residue-level helices, strands, and coils from sequence, then compare them with coordinate-derived assignments. [Open this workflow](https://proteiniq.io/app/workflows/templates?template=protein-secondary-structure-prediction)[Contact sales](https://proteiniq.io/contact) ## More protein structure prediction workflows ## Tools for protein secondary structure prediction Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow. [![Chou-Fasman](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fchou-fasman-featured.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Chou-Fasman Predict secondary structure directly from sequence](https://proteiniq.io/app/chou-fasman)[![ESMFold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fesmfold.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ESMFold Generate a single-sequence 3D comparison model](https://proteiniq.io/app/esmfold)[![DSSP](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fdssp.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### DSSP Assign secondary structure from coordinates](https://proteiniq.io/app/dssp)[![Hydropathy plot](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fhydropathy-plot.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Hydropathy plot Add hydrophobic-segment context](https://proteiniq.io/app/hydropathy-plot)[![Protein scale profiler](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fprotein-scale.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Protein scale profiler Map sequence-derived property scales](https://proteiniq.io/app/protein-scale)[![AlphaFold2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Falphafold-2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### AlphaFold2 Generate an alternative 3D model](https://proteiniq.io/app/alphafold-2)[![ESMFold2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fesmfold-2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ESMFold2 Generate a language-model structure prediction](https://proteiniq.io/app/esmfold-2)[![MolProbity](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fmolprobity.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### MolProbity Assess model stereochemistry](https://proteiniq.io/app/molprobity)[![Ramachandran plot](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Framachandran-plot.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Ramachandran plot Review backbone conformations](https://proteiniq.io/app/ramachandran-plot)[![USAlign](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fusalign.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### USAlign Compare alternative 3D models](https://proteiniq.io/app/usalign)[![SASA calculator](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fsasa-calculator.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### SASA calculator Relate predicted states to solvent exposure](https://proteiniq.io/app/sasa-calculator)[![PDBsum](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fpdbsum.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PDBsum Summarize coordinate-derived structural features](https://proteiniq.io/app/pdbsum) ## 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 Use local-state prediction when the question concerns helices, sheets or turns: - You want a first map of structured regions from one sequence. - A predicted 3D model needs a coordinate-based secondary-structure assignment. - Related sequences disagree at a helix or sheet boundary. ### Strengths and limitations Sequence propensities provide a quick local hypothesis before a reliable fold is available. Coordinate assignment with DSSP offers a separate description of a specific 3D model, allowing position-by-position comparison rather than one global score. Neither method alone establishes a native fold. Sequence propensity ignores long-range contacts, while DSSP describes whatever coordinates it receives, including erroneous predicted ones. Disordered or flexible regions may not have one persistent state. ## 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. ## Applications of protein secondary structure prediction Sequence-level helix and sheet predictions can help plan constructs or compare a family of related sequences before a full structure is available. They are local-state hypotheses, not a three-dimensional fold. When a model structure exists, coordinate-derived assignments provide a useful comparison. Disagreement can identify flexible, poorly modeled or sequence-sensitive regions, but it should not be turned into a universal accuracy number. ## How to run protein secondary structure prediction online 1. **Prepare the sequence.** Supply a reviewed [FASTA](https://proteiniq.io/guides/how-to-make-a-fasta-file) with the intended construct boundaries and no unsupported characters. 2. **Run the sequence method.** Use [Chou–Fasman](https://proteiniq.io/app/chou-fasman) for local helix, sheet and turn propensities. 3. **Build a comparison structure.** Run [ESMFold](https://proteiniq.io/app/esmfold) from the same sequence and retain its local confidence. 4. **Assign structure-derived states.** Send the predicted coordinates to [DSSP](https://proteiniq.io/app/dssp); it classifies that model rather than independently predicting from sequence. 5. **Compare position by position.** Align the state strings and inspect disagreement around termini, loops and low-confidence regions. ## How to interpret protein secondary structure prediction results Chou–Fasman propensities and DSSP assignments come from different evidence. A DSSP helix in a predicted model is conditional on that model being right, so agreement between the two is not independent experimental confirmation. Use the states to prioritize structural questions, then verify consequential local features with experimental structures or other evidence. Secondary-structure prediction does not establish folding, binding or function. ## 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](https://proteiniq.io/app/esmfold) from the same sequence to create an independent three-dimensional model. 4. **Assign states from coordinates.** Pass the ESMFold PDB to [DSSP](https://proteiniq.io/app/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 `FASTA``PDB``mmCIF` One clean protein sequence with an unambiguous residue order. ### Outputs #### Prediction and review outputs `PDB``mmCIF``CSV``JSON` Per-residue secondary-structure predictions, a predicted PDB structure, and DSSP helix, strand, turn, and coil assignments. ## Frequently asked questions AI answers from our docs. 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