Best Free Protein Structure Prediction Tools in 2026: The Complete List

AlphaFold2 is the benchmark against which every other structure prediction tool is measured. Its accuracy for single protein chains with detectable evolutionary relatives is unmatched — predictions routinely fall within 1–2 Å of experimental crystal structures for well-folded, globular proteins. It won CASP14 by a margin that changed the field and has since become the de facto standard in structural biology.

ColabFold is the most practical way to run AlphaFold2 predictions for free. Developed by the Steinegger lab, it wraps AlphaFold2 in a Google Colab notebook that runs in your browser — no installation, no HPC account needed. It uses a faster MSA pipeline (MMseqs2 instead of jackhmmer) that’s nearly as accurate as the original while reducing MSA generation time from hours to minutes. For most academic researchers, ColabFold is indistinguishable from the full AlphaFold2 pipeline in practice.

How to access

For proteins already in UniProt: go to alphafold.ebi.ac.uk, search your protein name or UniProt accession, download the PDB file and the accompanying JSON confidence file. For novel sequences: go to Google Colab, search “AlphaFold2 ColabFold” to find the notebook, paste your FASTA sequence, and run. The notebook handles everything else automatically. A dedicated ColabFold tutorial on this site walks through every step in detail.

ESMFold is the speed tool of the structure prediction world. Rather than building a multiple sequence alignment from evolutionary relatives, it uses a massive protein language model (ESM-2, trained on 250 million sequences) to extract structural information directly from the input sequence. The result: predictions in seconds rather than the minutes-to-hours required by AlphaFold2.

For typical well-studied proteins, ESMFold is meaningfully less accurate than AlphaFold2 — the MSA contains evolutionary co-variation signals that a language model cannot fully replace. But for proteins with very few or no detectable homologs, where AlphaFold2 also struggles, ESMFold often performs comparably. And for applications requiring thousands of predictions — proteome-wide studies, variant screening, pipeline integration — ESMFold’s speed makes it the only practical choice.

The ESM Metagenomic Atlas provides pre-computed ESMFold models for over 700 million metagenomic sequences — a scale of coverage that would be computationally prohibitive with any MSA-based method.

How to access

Go to esmatlas.com and paste your sequence into the fold sequence box. Results return in seconds. For batch predictions via API: send a POST request to https://esmatlas.com/api/fold with your FASTA sequence as the request body. The API returns a PDB-format structure directly in the response — easily integrated into Python pipelines with a few lines of code.

RoseTTAFold2 is the Baker Lab’s deep learning structure prediction model — a direct competitor to AlphaFold2 in accuracy terms, and meaningfully different in one important practical respect: it is completely open source under a permissive license with no commercial use restrictions. If you or your institution has any commercial application in mind, RoseTTAFold2 is the fully open alternative to AlphaFold2 for single-chain prediction.

Accuracy-wise, RoseTTAFold2 is competitive with AlphaFold2 for most proteins — some benchmarks favor one, some favor the other, with the gap small enough that the choice often comes down to licensing and workflow preferences rather than meaningful accuracy differences. The Baker Lab has also released RoseTTAFold2NA, which extends the model to predict complexes involving nucleic acids — a capability that, unlike AlphaFold3, comes with a fully open license.

Swiss-Model is the world’s most widely used homology modeling server, and for good reason: it is the simplest route from sequence to model. Paste a sequence, click submit, and within a few minutes you receive a 3D model along with quality scores (QMEAN, QMEANDisCo) and a report identifying which template was used. No MSA setup, no command line, no coding — just a web form.

The key distinction from AlphaFold2 is method: Swiss-Model builds models by copying the backbone coordinates from a structurally similar template in the PDB, then optimizing side chains and loops. This means Swiss-Model works best when your target protein has a close homolog with a known structure — typically at sequence identity above 30–40%. Below that threshold, template quality degrades and deep learning methods like AlphaFold2 become significantly more reliable.

Swiss-Model remains highly useful in 2026 despite AlphaFold’s dominance for one specific reason: it lets you explicitly choose your template. If you want a model in the active conformation, or based on a specific ligand-bound structure, Swiss-Model lets you specify that template. AlphaFold2 doesn’t give you that control — it predicts whatever conformation its training data implied was most probable.

I-TASSER (Iterative Threading ASSEmbly Refinement) has been one of the top-performing servers in CASP competitions for over a decade. Its approach is hybrid: it uses multiple threading algorithms to identify structural templates, assembles models from template fragments, and then refines them with energy minimization. Unlike the other tools on this list, I-TASSER goes beyond structure to also predict biological function — gene ontology annotations, enzyme commission numbers, and binding site predictions come alongside the structural model.

In the post-AlphaFold era, I-TASSER’s structural accuracy is generally lower than AlphaFold2 for proteins with good MSA coverage. But its function prediction pipeline — BioLiP-based ligand binding site prediction, COFACTOR for GO annotations — has no direct equivalent among the other free tools. If you need both a structure and automated functional annotation in a single server submission, I-TASSER remains the most comprehensive free option available.

Note: access to the I-TASSER server is free for academic users but requires registration. The Zhang Lab also maintains AlphaFold2-based refinement pipelines that use I-TASSER’s refinement engine on top of AlphaFold2 predictions — worth exploring if you want the best of both approaches.