The most reliable way to prepare a protein for complex structure prediction is to define the biological construct first, then preserve that exact construct through sequence cleaning, chain assembly, MSA preparation, template mapping and provenance recording. Do not trim a sequence merely because a region is disordered or predicted to be low confidence. Remove or retain signal peptides, propeptides, tags, linkers and truncations only when they match the molecule you intend to model.
This workflow applies to AlphaFold 3 JSON inputs, ColabFold FASTA or CSV jobs, and custom A3M alignments. It is designed for complexes, where an incorrect chain boundary or incorrectly paired MSA can produce a plausible-looking but biologically wrong model.
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1. Define the molecule before editing any letters
“Cleaning” is not simply deleting unusual characters. It is a construct-definition decision. Write down what the prediction should represent before opening a text editor:
- the full precursor or a processed mature chain;
- the canonical protein or a specific isoform;
- the experimentally expressed construct;
- a membrane, soluble or organelle-targeted form;
- a designed truncation;
- the expected stoichiometry, such as A:B, A2 or A2B2;
- any affinity tag, purification tag, linker, mutation or engineered cleavage site; and
- known or intended post-translational modifications.
Record the organism, gene or protein accession, isoform, source boundaries and construct boundaries in a manifest. This record lets you map every modelled residue back to the source sequence and explain why a segment was retained or removed.
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Precursor versus mature protein
A UniProt record may annotate a signal peptide, propeptide, transit peptide or other molecule-processing region. That annotation is evidence that processing occurs; it is not an automatic instruction to delete the region. Model the precursor when the biological question concerns synthesis, targeting or an unprocessed assembly. Model the mature chain when the complex exists after cleavage and the cleaved segment is absent from the assembled structure.
Experimental construct versus native protein
If the experiment used a tagged or truncated construct, the prediction should normally use that expressed sequence when the tag or boundary could influence folding, oligomerization or accessibility. A tag that is cleaved before the complex forms should be represented according to the post-cleavage construct. Keep both the native and engineered boundaries in your notes so the model cannot be mistaken for the natural protein.
Do not trim by confidence prediction
Low complexity, disorder or a predicted low-confidence tail can be biologically real. Removing it changes the molecule and may alter inter-chain contacts. Trim only when the construct, processing state or explicit modelling objective justifies the change. If uncertain, run clearly labelled alternatives rather than silently editing one sequence.
2. Choose an authoritative sequence and freeze it
Use a reviewed UniProtKB/Swiss-Prot record when it matches the target. If no reviewed record is appropriate, record the source database, accession, release or version, organism and evidence level. Download the exact FASTA instead of copying a sequence from a formatted paper or web page.
- Save the original FASTA unchanged in a read-only or otherwise protected location.
- Record the accession, isoform identifier, database release, retrieval date and organism.
- Compute a checksum for the original file and store it in the manifest.
- Create a separate cleaned derivative. Never overwrite the source file.
- Keep a residue map showing source positions, cleaned positions and any mature-chain renumbering.
A checksum does not prove that the sequence is biologically correct; it proves which exact file was used. That distinction matters when database records are updated or when collaborators exchange similarly named FASTA files.
3. Normalize the sequence text without changing its biology
Protein prediction tools expect a sequence string, not the layout of a document. Remove formatting artifacts while preserving every intended residue.
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Safe normalization steps
- Separate the FASTA header from the sequence. Treat the header as metadata, never as amino-acid input.
- Remove spaces, tabs, carriage returns and line-numbering copied from a document.
- Join wrapped FASTA lines into one logical sequence for validation; line wrapping can be restored later.
- Convert alphabetic characters to a consistent case only if the selected tool permits it. For a protein query, use the standard uppercase one-letter alphabet.
- Check the resulting string against the one-letter standard amino-acid alphabet expected by the tool.
- Stop on ambiguous or nonstandard symbols and resolve them manually from the biological record. Do not silently translate an unknown character into another residue.
AlphaFold 3 defines a protein sequence as a one-letter standard amino-acid string. Characters such as punctuation, copied residue numbers or an accidental nucleotide alphabet are input errors, not harmless decoration.
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Symbols such as an ambiguous residue code, a selenocysteine code, a modified-residue notation or a stop character require an explicit decision. Confirm whether the selected predictor supports that representation. If it does not, document the biologically justified replacement or model the relevant construct separately; never make an undocumented substitution simply to pass validation.
Length and identity checks
After normalization, calculate the sequence length and compare it with the expected accession and construct boundaries. A one-residue discrepancy can indicate a missing initiator methionine, an off-by-one cleavage boundary or a copied numbering line. Compare the cleaned query character-for-character with the sequence used to generate its MSA.
4. Handle signal peptides, propeptides, tags and linkers explicitly
Processing and engineering segments are the most common source of a biologically incorrect “clean” sequence. Keep an annotation table with the original interval, decision, justification and new numbering.
| Segment | Retain when | Remove when | Documentation to keep |
|---|---|---|---|
| Signal peptide | The precursor or targeting state is the object of prediction, or the segment remains present in the assembly. | The target is the experimentally verified mature chain after secretion or cleavage. | Annotated source interval, cleavage boundary and mature-chain mapping. |
| Propeptide | The zymogen or precursor participates in the model. | The complex contains the activated chain and the propeptide is cleaved away. | Processing annotation and mature-chain residue range. |
| Transit peptide | The import or precursor state matters to the question. | The model represents the mature organelle-localized protein after removal. | Source coordinates and renumbering rule. |
| Purification or affinity tag | The expressed construct retains the tag during assembly or the tag may affect contacts. | The tag is removed before the biological complex forms. | Tag sequence, linker, cleavage site and expression construct. |
| Flexible linker | It is part of the expressed molecule or connects domains in the assembled construct. | It is a cloning scar absent from the molecule being modelled. | Exact linker sequence and reason for exclusion. |
When a segment is removed, preserve the source numbering in a mapping file. A mature chain can be numbered from one for convenience, but the mapping must state which precursor residues those numbers represent. This prevents incorrect interpretation of interface contacts and mutation positions.
5. Represent the complex with explicit chains and stoichiometry
Assign a unique chain ID to every distinct physical copy in the input representation. Stoichiometry is encoded by repeating IDs only when the same sequence is intentionally present in multiple copies. For example, a homodimer can use two chain entries with the same sequence and distinct chain identifiers, while a heterodimer uses one entry for each sequence.
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Build a chain manifest
- Chain ID: the identifier used in the input and output files.
- Source sequence: accession, isoform and cleaned sequence hash.
- Copy number: the number of physical copies represented.
- Assembly hypothesis: A:B, A2, A2B2 or another explicitly stated arrangement.
- Construct state: precursor, mature chain, tagged construct or truncation.
AlphaFold 3 documents an ID list such as ["A","B","C"] for homomeric copies. The important point is not the particular letters but the explicit correspondence between IDs, sequences and intended stoichiometry. Check that no accidental duplicate FASTA record creates an extra subunit and that no split record turns one chain into two.
When a multimer separates into individual chains
Chain separation can reflect a genuinely weak or transient interface, but it can also result from an incorrect construct, wrong stoichiometry, incompatible processing state or unpaired MSAs. Verify the biological assembly and input representation before interpreting confidence. A high-confidence monomer does not rescue a complex encoded with the wrong chain count.
6. Prepare and validate custom A3M MSAs
Custom alignments must preserve the query exactly. In A3M syntax, the first sequence is the ungapped query sequence. Lowercase letters represent insertions relative to the query; hyphens represent aligned gaps.
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A3M validation procedure
- Confirm that the first record is the exact cleaned query, with identical characters and order.
- Remove lowercase insertion characters conceptually and verify that the remaining query coordinates match the query length.
- Retain hyphens as gap characters in aligned rows.
- Check that every row has the same aligned length after insertion handling.
- Reject rows with shifted starts, copied numbering, embedded spaces or a different query sequence.
- Record the MSA file hash, generation method, database release and date.
The query-first rule is strict: AlphaFold 3 documentation states, “The first sequence is exactly equal to the query sequence.” ColabFold also documents the query-first convention. An alignment that looks visually plausible can still be unusable if its first row differs by one residue or contains an insertion in the wrong representation.
7. Pair MSAs deliberately for multimers
For a single chain, the main concern is whether homologs align correctly. For a multimer, the additional question is whether sequences in the same row are true biological counterparts from the same organism or genomic context. Independent per-chain MSAs can place unrelated homologs in one row, creating false co-evolutionary signals.
AlphaFold 3 documentation states that MSA pairing matters only when folding multiple chains. For complexes, it recommends manual pairing or the output of appropriate software, followed by supplying the paired result through the unpairedMsa field when exact placement is required.
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Pairing checklist
- Use organism identifiers consistently across chain alignments.
- Confirm that paired rows represent proteins from the same organism or the intended biological context.
- Remove or resolve duplicate identifiers according to a documented rule.
- Keep chain order stable from the pairing step through JSON or FASTA generation.
- Inspect row depth; a shallow but accurate paired set is preferable to a deep set of biologically mismatched rows.
- Record whether rows were paired manually, generated by software or supplied as unpaired data.
If exact pairing cannot be established, run a controlled unpaired workflow and label that choice. Do not present unpaired results as evidence of a specific interface.
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8. Use templates conservatively
A supplied template must be a single-chain mmCIF file with a correct mapping from query residues to template residues. Verify all of the following before including it:
- the template chain is the intended protein or domain;
- query and template residue indices use the expected zero-based convention;
- insertions and deletions are mapped explicitly;
- residue numbering is consistent despite gaps or author numbering;
- the template’s ligand or partner context is relevant to the modelling question; and
- the chain identity matches the sequence being modelled.
When mapping is uncertain, omit the template or run a template-free prediction. An incorrectly mapped template can impose a confident but wrong geometry. Keep the original mmCIF, mapping data and decision in the run manifest.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Choose an input workflow
AlphaFold 3 and ColabFold can represent the same biological hypothesis through different file conventions. Choose the workflow that gives you the required control over modifications, MSAs, templates and provenance.
| Decision axis | AlphaFold 3 workflow | ColabFold workflow |
|---|---|---|
| Primary input | JSON containing protein sequence, chain IDs and optional fields. | Supported FASTA or CSV syntax; AF3-compatible JSON can be exported with --af3-json. |
| Protein modifications | JSON schema provides fields for optional modifications. | Use the syntax and capabilities supported by the selected ColabFold interface, then verify the exported representation. |
| Multimer MSA control | Supports paired and unpaired MSA fields; exact pairing can be supplied through unpairedMsa. |
Supports custom A3M input and documented query-first handling; pairing must be checked before export. |
| Template representation | Single-chain mmCIF with query-to-template index mappings. | Use the workflow’s supported template path, and preserve any converted mappings when exporting. |
| Provenance outputs | Keep the exact JSON, MSA and template artifacts with the run record. | ColabFold produces predicted coordinates and A3M files; the A3M records sequences used by the run. |
Do not mix assumptions between formats. A sequence that is valid in FASTA may still be represented with the wrong chain IDs in JSON, and an MSA that is valid as a file may be attached to the wrong chain after conversion.
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Create one machine-readable manifest per prediction. At minimum, include:
- source database, accession, isoform, organism and release;
- original FASTA checksum and cleaned-sequence checksum;
- construct boundaries, processing decisions, tags, linkers and mutations;
- chain IDs, copy numbers and intended stoichiometry;
- exact input JSON, FASTA or CSV;
- each A3M file, its hash, pairing method and database release;
- template mmCIF files and residue-index mappings;
- tool and database versions;
- run date and execution environment; and
- the validation report and output file names.
ColabFold’s output includes predicted coordinates and A3M files. Preserve those files rather than saving only a rendered image or a final structure. The A3M provides a record of the sequences used in the run, which is essential when a database changes later.
11. Run a preflight validation before prediction
Use a machine-readable report and fail the job when any required check does not pass.
- Alphabet: every query character is an accepted one-letter amino-acid code.
- Length: each sequence length matches its documented construct boundaries.
- Identity: the MSA query is exactly the cleaned query.
- A3M shape: insertion handling leaves rectangular aligned rows with valid gaps.
- Chain count: the number of chain entries matches the biological assembly.
- Stoichiometry: repeated sequences represent intentional copies, not duplicate records.
- Chain IDs: IDs are unique where required and consistent across all files.
- Templates: each mmCIF is single-chain and every mapping is valid.
- Processing: signal peptides, propeptides, transit peptides, tags and linkers match the stated construct.
- Artifacts: hashes, versions and dates are present for all inputs.
Keep the report beside the run rather than relying on terminal output. A failed preflight is cheaper to fix than a completed prediction whose chain assignment cannot be reconstructed.
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12. Interpret complex predictions without excusing bad inputs
Review ranking and pTM-related confidence together with interface-specific measures available in the selected tool. For a complex, inspect whether the interface is consistent across ranked models, whether the chains occupy the intended stoichiometry and whether contacts occur in biologically plausible regions.
Confidence is not a substitute for construct validation. A model can score well while representing a precursor instead of a mature chain, using the wrong isoform, pairing unrelated homologs or omitting a required partner. First confirm that the input molecule and assembly are correct; only then use confidence measures to compare alternative models.
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Common failure branches
- Validation rejects the sequence: inspect copied numbering, punctuation, ambiguous symbols and accidental FASTA-header text.
- MSA rows have inconsistent lengths: reprocess lowercase insertions and gaps; verify that the first row is the exact query.
- The complex separates into chains: check stoichiometry, chain IDs, processing state and MSA pairing before changing model settings.
- Template mapping fails: verify zero-based indices, chain identity and insertion/deletion handling; omit the template if uncertainty remains.
- Results cannot be reproduced: restore the frozen FASTA, hashes, A3M, template, versions and run date from the manifest.
13. A practical end-to-end checklist
- Define the biological construct and assembly hypothesis.
- Freeze a source accession and original FASTA.
- Make a cleaned derivative without overwriting the source.
- Document every processing or engineering boundary.
- Validate alphabet, length and residue identity.
- Assign explicit chain IDs and copy numbers.
- Prepare A3M files with the exact query first.
- Pair multimer MSAs only when biological correspondence is established.
- Validate single-chain mmCIF templates and zero-based mappings.
- Generate the selected JSON, FASTA or CSV representation.
- Run preflight checks and save the report.
- Archive inputs, hashes, versions, dates and outputs together.
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