# Transmembrane protein structure prediction Predict membrane-protein folds and examine hydrophobic segments, topology context, confidence, and explicit membrane-orientation limitations. [Open this workflow](https://proteiniq.io/app/workflows/templates?template=transmembrane-protein-structure-prediction)[Contact sales](https://proteiniq.io/contact) ## More protein structure prediction workflows ## Tools for transmembrane protein structure prediction Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow. [![Hydropathy plot](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fhydropathy-plot.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Hydropathy plot Map Kyte–Doolittle hydrophobic segments](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 Inspect alternative residue-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 Predict membrane-protein 3D structure](https://proteiniq.io/app/alphafold-2)[![ESMFold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fesmfold.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ESMFold Generate a fast single-sequence comparison model](https://proteiniq.io/app/esmfold)[![ESMFold2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fesmfold-2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ESMFold2 Generate an alternative language-model prediction](https://proteiniq.io/app/esmfold-2)[![OpenFold3](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fopenfold-3.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### OpenFold3 Predict alternative all-atom structures](https://proteiniq.io/app/openfold-3)[![Boltz-2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fboltz-2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Boltz-2 Predict membrane-protein complexes or cofactors](https://proteiniq.io/app/boltz-2)[![DSSP](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fdssp.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### DSSP Assign helices and strands from coordinates](https://proteiniq.io/app/dssp)[![SASA calculator](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fsasa-calculator.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### SASA calculator Inspect predicted solvent exposure](https://proteiniq.io/app/sasa-calculator)[![PDBFixer](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fpdb-fixer-featured.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PDBFixer Prepare selected coordinate models](https://proteiniq.io/app/pdb-fixer)[![MolProbity](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fmolprobity.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### MolProbity Review stereochemical quality](https://proteiniq.io/app/molprobity)[![GROMACS](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fgmx-mmpbsa.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### GROMACS Run downstream membrane molecular dynamics](https://proteiniq.io/app/gromacs) ## What is transmembrane protein structure prediction? Transmembrane protein structure prediction is the computational process of building the three-dimensional fold of a protein embedded in or associated with a membrane. It differs from topology prediction, which identifies likely membrane-spanning segments and orientation without necessarily producing atomic coordinates. General protein predictors can model many membrane-protein folds, but they do not automatically establish membrane placement, lipid composition, oligomeric state, or an active conformation. Hydropathy provides useful sequence context, not a dedicated topology result. ProteinIQ combines general three-dimensional prediction with Kyte–Doolittle hydropathy analysis. Interpret this as fold prediction plus membrane-context review, not as a substitute for a specialist topology predictor or membrane-embedded simulation. ### When to use transmembrane protein structure prediction Use membrane-protein prediction with a topology question in mind: - A sequence has candidate membrane-spanning segments. - You need a structural model for mutational or construct planning. - Topology evidence can be compared with independently predicted folds. ### Strengths and limitations Hydropathy and structure prediction offer complementary views of the same sequence. A predicted model can suggest helix packing and accessible loops, while the sequence profile helps flag possible membrane-spanning regions. The calculations may omit lipids, oligomeric partners and the correct membrane orientation. A hydrophobic stretch is not automatically a transmembrane helix, and local structural confidence does not establish topology or transport function. ## Membrane-context interpretation Sustained hydrophobic segments support transmembrane-helix hypotheses, but signal peptides, re-entrant loops, amphipathic helices, and beta barrels complicate simple thresholds. Add dedicated topology evidence before assigning orientation. Compare predicted helices and hydrophobic segments while remembering that a soluble-looking model can still be wrong in membrane context. Place and equilibrate a selected structure in an appropriate explicit membrane for downstream simulation. ## Applications of transmembrane protein structure prediction A membrane-protein model can help place helices, plan constructs and map residues when an experimental structure is unavailable. Hydropathy gives a sequence-level clue to membrane-spanning segments that can be compared with predicted coordinates. Interpretation depends on topology, oligomeric state and the lipid environment. A model made without the relevant membrane or partner can position loops and helices incorrectly even when its local fold looks regular. ## How to run transmembrane protein structure prediction online 1. **Prepare the sequence.** Keep signal peptides, transmembrane segments and construct boundaries explicit. 2. **Map hydrophobic regions.** Run [Hydropathy Plot](https://proteiniq.io/app/hydropathy-plot) to identify candidate membrane-spanning stretches; it does not predict a three-dimensional fold. 3. **Build alternative models.** Run [AlphaFold2](https://proteiniq.io/app/alphafold-2) on the same sequence. 4. **Compare a second predictor.** Review [ESMFold](https://proteiniq.io/app/esmfold) coordinates and confidence separately. 5. **Check membrane context.** Compare helix placement with topology evidence, oligomerization data and known lipid-facing residues before using the model. ## How to interpret transmembrane protein structure prediction results Hydropathy and structure confidence answer different questions. A hydrophobic stretch may be a membrane helix, signal peptide or buried region; a high-confidence helix does not by itself define its orientation in a bilayer. Use the model to guide structural or mutational follow-up. It does not confirm membrane insertion, assembly, transport activity or function. ## How transmembrane protein structure prediction works Predict the fold with AlphaFold2 and ESMFold, then compare predicted helices with hydrophobic segments from the Hydropathy Plot tool. 1. **Check the full-length sequence.** Provide the biologically relevant full-length sequence and confirm signal-peptide handling. 2. **Map hydrophobic segments.** Run Hydropathy Plot to identify sustained hydrophobic regions as membrane-context evidence. 3. **Predict alternative 3D models.** Run [AlphaFold2](https://proteiniq.io/app/alphafold-2) and ESMFold from the same sequence. 4. **Review topology consistency.** Compare predicted helices, confidence, domain arrangement, and hydrophobic-segment positions. 5. **Validate membrane context.** Add specialist topology or experimental evidence before membrane placement, docking, or simulation. ## Inputs and outputs Check formats before running, then inspect and download the result from every workflow step. ### Inputs #### Research input `FASTA``PDB``mmCIF` A full-length protein sequence, retaining signal peptides or terminal regions when biologically relevant. ### Outputs #### Prediction and review outputs `PDB``mmCIF``CSV``JSON` A Kyte–Doolittle hydropathy profile plus predicted PDB structures and model-native confidence. ## Frequently asked questions AI answers from our docs. 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