Text-to-protein generative model designing de novo sequences from plain-language function descriptions, with designs confirmed by crystallography.
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Most computational protein design starts from a scaffold. The designer picks a sequence, backbone, or motif that plausibly supports the target function, then optimizes around it — a workflow confined to whatever neighborhood of protein space the starting point occupies, and one that takes expertise to choose that starting point well.
MP4 — the Molecular Programming model, version 4 — from 310 AI removes that step. It is a transformer trained to map a natural-language prompt directly to an amino acid sequence, with no sequence or structural input required. A prompt can specify the desired function, the source organism, physical properties, and optional sequence hints; the model returns a sequence intended to satisfy them. The approach sits alongside ESM-3, Pinal, and ProteinDT in the text-conditioned design family, but MP4 consumes text alone at inference rather than routing through structure generation or controlled-tag conditioning.
What makes the preprint notable is how far the validation goes. Rather than stopping at in-silico metrics, the authors cloned 94 of 96 benchmark designs, expressed them, measured thermostability, solved two crystal structures at 1.30 Å and 1.77 Å, and demonstrated ATP binding and hydrolysis for designs prompted as ATPases. One of those structures has no close match in the Protein Data Bank.
MP4 is a multi-layer transformer with multi-context sub-models that process different facets of the prompt features, an encoder aggregating them into a context-aware latent representation, a decoder, and the multi-task heads. Training used more than 3.2 billion datapoints from repositories including UniProt — 1.3 billion for sequences shorter than 300 residues and 2.3 billion for sequences shorter than 500 — over a unified 138,000-token vocabulary spanning natural-language descriptors and protein sequences, at roughly 3,800 AMD Instinct GPU-hours. Internal layer configurations, hyperparameters, and optimization strategies are proprietary and undisclosed.
The benchmark drew more than 1,000 prompts covering enzymatic activities, binding partners, and subcellular localization, from which 96 sequences were selected for novelty (under 50% identity to the NR database) and diversity. Against ESM-3 (fed InterPro classifications from the same prompts, without templates) and Pinal (fed the prompts as text), MP4 scored better on amino acid composition similarity to UniProt, on k=2 repetitiveness, on median ESMFold pLDDT, and on a recall-style overlap between prompt terms and predicted annotations. The comparison is not symmetric: Pinal builds structural templates internally, so it receives more starting information than MP4 or ESM-3, and the authors left that step enabled. Of the two crystallized designs, the 120-residue M1X0B overlays a known response-regulator fold at 0.938 Å RMSD, while the 77-residue homodimer MIYEI diverges from its closest deposited structures by 7.25 Å and 5.53 Å RMSD — a new fold.
MP4 targets the earliest stage of a design campaign, where a researcher knows the function they want but not which scaffold to build on. Prompting for an activity, a ligand, or a localization returns candidate sequences that can go straight to synthesis and screening, lowering the expertise barrier for teams without a structural biology background and widening the sequence space explored by those who have one. The reported ATPase work illustrates the intended loop: prompt, filter computationally, express, and assay.
The preprint has not been peer reviewed, and the authors are explicit that functional coverage, controllability, and interpretability remain open problems. Neither code nor weights are public — 310 AI cites commercial confidentiality and directs researchers to contact the company, with hosted inference offered through its browser-based 310 Copilot platform. What the work does establish is that a text-only interface can produce proteins that express, fold, crystallize, and catalyze, placing MP4's designed ATPases — three of them with directly measured hydrolysis — among the small canon of machine-learning-designed enzymes that includes pGAN59, FastPETase, ProGen's L056, and the PLACER-designed serine hydrolase momi120_102.
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