Tooluniverse Enzyme Design

De Novo Enzyme Designer

Design enzymes for a named reaction by scaffolding a catalytic motif, then triage the output with filters derived from the reaction's mechanism. Built around AlphaProtein Novo (AP Novo), whose own finding is that the filters, not the generator, decided success.

KEY PRINCIPLES:

1. Motif-first — the input motif dominates the outcome more than any model choice
2. Mechanism-inspired metrics — catalytic geometry beats generic confidence
3. Multi-state — fold every state along the mechanism, require agreement across them
4. Backbone quality is an ensemble property — score a backbone by how many resequences of it pass
5. Report the funnel, not the survivors — know which filter killed what
6. In-silico pass is not activity — grade designs, never promise them
7. English-first queries — always use English terms in tool calls

De novo enzyme design starts with chemistry, not protein. You must be able to draw the transition state or a covalent intermediate as 3D coordinates — catalytic side-chain atoms plus ligand — before any model is useful. Everything downstream is scaffolding that geometry and then asking whether a predictor still believes it.

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

When to Use

Apply when the user asks to:

• Design an enzyme for a specific reaction or substrate
• Scaffold a catalytic motif / active site into a new protein
• Filter, score, rank or triage an existing set of enzyme designs
• Decide which designs are worth ordering, and why
• Optimize an already-active design (recycled scaffold / partial diffusion)

Not this skill: binder or interface design with no catalytic chemistry — use tooluniverse-protein-therapeutic-design or tooluniverse-binder-discovery. Structure prediction alone — use tooluniverse-protein-structure-prediction.

What AP Novo is, exactly

A conditional joint sequence–structure diffusion model (147.5 M parameters, 32 blocks) that co-generates backbone coordinates and sequence, conditioned on a fixed catalytic motif and fixed ligand atoms. Because it co-generates sequence, the LigandMPNN resequencing step is optional rather than required.

Pipeline, four stages, each independently runnable:

run_pipeline.py runs all four from one manifest (JSON).

In ToolUniverse the apnovo remote provider covers three of those four, not all of them. It serves motif validation, generation and resequencing; it has no folding operation, because AlphaFold 3 weights carry terms separate from AP Novo's. That is not a gap you can route around inside the provider: upstream's evaluation refuses a campaign it finds no folding seed for, so apnovo_evaluate_designs also cannot run until the designs have been folded somewhere. Phase 5 below is therefore the step you have to arrange yourself — with a structure-prediction provider that has settled its own weights question — and Phases 6 and 7 depend on its output. skills/setup-apnovo-remote-tool/SKILL.md covers installation, weights and terms.

Licence, before you design anything. The AP Novo generator weights are available for non-commercial use only (universities, non-profits, research institutes, educational, journalism, government). Output carries the AP Novo Output Terms of Use and must keep its notice when distributed, and any publication must cite the paper. The prohibited-use policy forbids motifs, ligands or substrates derived from or targeting toxins, venoms, virulence factors, regulated pathogens, or chemical/biological weapons precursors, and forbids circumventing gene-synthesis screening. Read src/tooluniverse/remote/apnovo/README.md before a campaign, not after.

Workflow Overview

Phase 1: Define the reaction and build the motif
  Mechanism -> transition state / intermediate -> 3D motif CIF + ligand

Phase 2: Validate the motif spec          <- do this BEFORE spending GPU
  motif_str parses, segments resolve, ligand chain present

Phase 3: Generate scaffolds
  1000 denoising steps, indexed or unindexed, N designs per motif

Phase 4: Resequence (optional)
  LigandMPNN T=0.1; or keep AP Novo's own sequence

Phase 5: Fold every mechanistic state
  apo + each intermediate, multiple seeds per state

Phase 6: Evaluate and filter             <- the funnel; report it
  generic metrics -> catalytic geometry -> multi-state agreement

Phase 7: Backbone-ensemble scoring (MR / MPD)
  the single highest-value filter in the paper

Phase 8: Triage, novelty check, report

Phase 1 — Define the reaction and build the motif

You need, as 3D coordinates in one CIF: the catalytic side-chain atoms and the ligand representing a transition state or covalent intermediate, in the conformation you want the enzyme to hold.

Decide four things, each of which changes the manifest:

Sources for motif geometry, in order of preference: a crystal structure of a natural enzyme with the right machinery (search the PDB), a DFT-derived theozyme, or RDKit-generated ligand conformers placed against a natural triad. The published campaigns used all three.

The motif is the dominant variable. In one matched comparison, an Asp/Glu catalytic base gave a 37% hit rate (32/86) where a His base gave 10% (9/87) — same pipeline, same filters. If a campaign fails, re-examine the motif before re-tuning filters.

Phase 2 — Validate the motif spec first

motif_str is the field that silently wastes GPU-days. Its grammar:

A1,5-10,A5-6,3/B1
└┬┘ └┬─┘ └─┬┘ │ └┬┘
 │   │     │  │  └── chain B residue 1: the ligand, fixed
 │   │     │  └───── 3 generated residues
 │   │     └──────── scaffold input chain A residues 5-6, fixed
 │   └────────────── 5 to 10 generated residues (sampled once per design)
 └────────────────── scaffold input chain A residue 1, fixed

| before the segment list randomises segment order: "A1,A5-6|{},5-10,{},3/B1". is_author_naming: true means the numbers are the author numbering PyMOL and papers use; the PDB web viewer shows internal numbering instead.

Validate before generating — the apnovo_validate_motif operation parses the CIF and the motif string with upstream's own MotifSpec and reports the resolved segment lengths, which atoms are fixed, and the ligand chains. It needs no weights and no GPU. A malformed motif_str caught here costs seconds; caught in Phase 6 it costs the campaign.

Phase 3 — Generate scaffolds

Defaults that matter: num_sampling_steps: 1000 (the published campaigns' value; conclusions were checked to hold at 200), and num_designs per motif in the hundreds — the authors flag sampling cost as a real limitation, since many samples are needed per motif to find one that passes.

Length sampling, as published: intervening segments 2–80 residues and termini 10–100 (Kemp eliminase); intervening 2–70, termini 5–100, total restricted to 100–250 (serine esterase); designable segments 1–278 either side of a heme-ligating His, total 60–280 (nitrene transferase).

Phase 4 — Resequence, or don't

resequence.enabled: false folds AP Novo's co-generated sequence directly. That is a legitimate choice: AP Novo sequences had similar in-silico pass rates and comparable measured activity to LigandMPNN sequences on two reactions. When used, LigandMPNN ran at temperature 0.1 with weights v_32_030_25, one sequence per design in the standard pipeline.

Requiring a design to pass filters under both sequence models is itself a filter: it raised retrospective hit rates 29%→45% (Kemp) and 5%→15% (serine esterase).

Phase 5 — Fold every mechanistic state

Enumerate the states your mechanism passes through and fold each. For a serine hydrolase that is five: apo, es (Michaelis, non-covalent), ti1 (first tetrahedral intermediate, covalent, sp³), aei (acyl-enzyme, covalent, sp²), ti2 (second tetrahedral intermediate). Each state is a manifest entry naming its ligand and its protein–ligand bond.

Seeds: 7 per state (Kemp), 10 (serine esterase / DEHP). Each AF3 trunk seed internally samples 5 structures and returns the top-ranked, so one trunk seed yields one prediction.

AF3 vs AF3-LA. Stock AF3 mishandles leaving atoms, producing ~180° bond angles at Ser Oγ for covalent tetrahedral intermediates where ~109° is correct. AF3-LA is a fine-tune that fixes this and is what the serine esterase and DEHP campaigns used. If your mechanism has covalent intermediates, this matters; if it does not, stock AF3 was used for Kemp, carbene and nitrene round 1.

Phase 6 — Evaluate and filter

See FILTERING.md for the metric namespace, the funnel implementation, and all five published threshold sets. The short version:

• metrics are named <state>/<metric>/<seed_or_aggregate>, plus sampled/* (diffusion output) and sequence/* (sequence sanity)
• filter on seed_0 first, then on aggregates across seeds
• report the cumulative funnel, so it is visible which filter is doing the work

Phase 7 — Backbone-ensemble scoring (MR / MPD)

This is the highest-value step in the published pipeline, and the most general idea in it.

Multi-Resequence (MR): hold the backbone fixed, generate N sequences for it, and score the backbone by how many of them pass your filter set. 10 resequences, pass = 10/10 (serine esterase); 7, pass = 6/7 then 7/7 (Kemp).

Multi-Partial-Diffusion (MPD): same idea one level out — re-noise the design by 575 of 1000 steps (≈88–90% sequence identity to the parent, ≈0.5 Å backbone RMSD), generate 10, require 9/10 to pass.

Measured effect — this is why it is Phase 7 and not an afterthought:

Why it generalises: the MR pass rate estimates the resequencer-accessible volume of functional sequence space for that backbone. It needs no new reaction knowledge beyond the filter set you already wrote — which is also its limit: MR is not reaction-agnostic, because it depends on having reaction-specific metrics first.

Phase 8 — Triage, novelty check, report

Before claiming novelty, check it. Define a natural-topology flag the way the authors did: TM-score > 0.45 and scaffold-aligned catalytic-triad RMSD < 5 Å to any annotated enzyme of the same class in the PDB. In the published serine esterases, 16.4% of designs met that definition — and six of the top seven by catalytic efficiency did. Report the best design and the best non-natural-topology design separately, as the paper does.

Report, per design: the metrics that passed, the metrics that failed, the funnel position, max TM-score and max sequence identity to anything natural, and an explicit statement that in-silico pass rate is a weak proxy for activity. See CAVEATS.md.

Reference files

• FILTERING.md — metric namespace, funnel code, all five published threshold sets, how to build a set for a new reaction
• CAVEATS.md — what this pipeline does not do, the published failure modes, and the licence terms