ProteinMPNN is now available as a one-click web tool at tools.ranomics.com/tools/mpnn. Upload a backbone structure, configure sampling temperature and the number of sequences, and download a FASTA. No GPU, no conda environment, no command line required.
This post covers what the tool does, when to use it, how to use it, and what it costs.
What ProteinMPNN does (two sentences)
ProteinMPNN solves the inverse folding problem: given a backbone structure, it predicts which amino acid sequences are geometrically and thermodynamically compatible with that geometry. It outputs a probability distribution over the 20 amino acids at each position conditioned on the full backbone, from which sequences are sampled at a user-specified temperature.
For a full treatment of the model architecture, the temperature parameter, and how fixed-position constraints work, see the companion article: ProteinMPNN and the sequence design problem.
When to use this tool vs. structure prediction tools
ProteinMPNN and AlphaFold/ColabFold/Boltz-2 operate on opposite directions of the sequence-structure map. They are not alternatives — they are sequential steps in the same pipeline.
Use ProteinMPNN immediately after a backbone generative step (RFdiffusion, BindCraft backbone output, or any designed PDB) to produce candidate sequences. ProteinMPNN takes backbone coordinates as input and returns sequences. It does not evaluate binding.
Use structure prediction (AlphaFold, ESMFold, ColabFold, Boltz-2) after ProteinMPNN to validate that the designed sequences actually fold into the intended backbone. The standard self-consistency check is: fold the designed sequence with ESMFold or ColabFold, align the result to the input backbone, and filter on RMSD. Candidates above ~2 Å are discarded.
Use complex structure prediction (AlphaFold 3, Boltz-2 in complex mode) as a separate downstream step to evaluate whether the designed binder actually engages the target. ProteinMPNN does not model the binding partner. A sequence that passes self-consistency may still produce a poor binding interface — complex prediction is the filter for that.
The practical pipeline order: RFdiffusion → ProteinMPNN → self-consistency filter → complex prediction → synthesis.
Tool walkthrough
The interface at tools.ranomics.com/tools/mpnn requires a free account.
Step 1: Upload a backbone. Accepted formats are PDB and mmCIF. The backbone must contain at least one protein chain with full backbone atom records (N, CA, C, O). Side-chain coordinates are not used. If your structure has multiple chains, the tool will design sequences for all chains by default — specify which chains to design if you want to hold some fixed.
Step 2: Set sampling temperature. The default is 0.3. For most binder design workflows, running at two temperatures (0.2 and 0.5) and pooling the outputs provides conservative high-confidence predictions alongside a diversity buffer. The tool accepts any value between 0.05 and 1.0.
Step 3: Set the number of sequences. The default is 8 per backbone. For production runs feeding into a downstream filter step, 8–16 sequences per backbone is standard. For exploratory work on a single scaffold, 50–100 sequences is reasonable.
Step 4: Download output. Results are returned as a FASTA file with one sequence per record, annotated with the backbone filename, temperature, and ProteinMPNN’s per-sequence score. The score is a log-probability averaged over all positions — lower (more negative) is better. Sort by score before passing sequences to the next filter step.
A concrete example from binder design
In a typical RFdiffusion binder design run, 10,000–30,000 backbone structures are generated against a target hotspot. After initial geometry filtering (diffusion confidence, clash score), a subset of 5,000–10,000 backbones advances to sequence design. Running ProteinMPNN at T = 0.2 and T = 0.5 on each backbone, with 8 sequences per backbone per temperature, produces a pool of 80,000–160,000 candidate sequences. That pool is then filtered by self-consistency (ESMFold RMSD), pLDDT on the designed chain, and interface quality from complex prediction, converging to a synthesis set of 500–2,000 sequences.
The ProteinMPNN step in this pipeline takes minutes on CPU, not hours. It is not the computational bottleneck. Running more sequences per backbone is cheap relative to the structure prediction steps that follow.
Pricing
Each ProteinMPNN run costs around $0.05 of compute on the Ranomics tools hub, depending on backbone size. New accounts get $5 of compute credit on signup, no credit card required. That covers roughly 100 MPNN runs.
After the trial credit, top up your wallet in any amount from $20 and pay per run. See tools.ranomics.com for current pricing.
Run ProteinMPNN now: tools.ranomics.com/tools/mpnn — $5 free on signup, no credit card.
Related Ranomics services
- ProteinMPNN tool: One-click sequence design on the Ranomics tools hub. Around $0.05 per run, $5 free on signup.
- Binder Pilot: Full binder design campaign — RFdiffusion + ProteinMPNN + experimental validation. Target structure in, ranked hit list out.
- Epitope Scout: Free surface epitope identification — identify which patches on your target are worth designing against before running any sequence design.