mosaic

name	mosaic
description	Multi-objective, gradient-based protein binder design with Mosaic. Use this skill when: (1) Composing several structure or sequence models into one design objective, (2) Optimizing binders against a custom loss rather than a fixed pipeline, (3) Wanting gradient descent over sequence space in the style of ColabDesign, RSO, or BindCraft but with interchangeable predictors, (4) Letting the optimizer choose the epitope instead of fixing hotspots. For an end-to-end binder pipeline with default filters, use bindcraft. For all-atom diffusion design, use boltzgen. For backbone-only generation, use rfdiffusion.
license	MIT
category	design-tools
tags	["design","gradient-optimization","multi-objective","jax","binder"]

Mosaic Multi-Objective Design

Mosaic (Escalante Bio) is a JAX framework for "functional, multi-objective protein design using continuous relaxation." It optimizes a soft sequence by gradient descent over a continuous relaxation of sequence space, in the lineage of ColabDesign, RSO, and BindCraft, with one key difference: it composes multiple learned objectives from different models in a single differentiable loss.

When Mosaic fits

Mosaic is a framework for custom objectives, not a one-click method. The README is explicit: it "may require substantial hand-holding (tuning learning rates, etc), often produces proteins that fail simple in-silico tests, [and] should be combined with standard filtering methods." Reach for it when a fixed pipeline cannot express the objective you need. For a turnkey binder run, use bindcraft instead.

Prerequisites

Requirement	Minimum	Recommended
Python	3.11+	3.11
Framework	JAX with CUDA or TPU	JAX CUDA 12
GPU VRAM	24GB	48GB+ (depends on predictors used)

JIT compilation makes the first call to any loss slow; later calls are fast.

Install

Mosaic runs locally on a JAX GPU or TPU build. It has no CLI and no Modal integration; you drive it through the marimo notebooks or the Python API.

git clone https://github.com/escalante-bio/mosaic && cd mosaic
uv sync --group jax-cuda      # or --group jax-tpu / --group jax-cpu
uv add jax[cuda12]            # may be needed for a GPU build
uv run marimo edit examples/example_notebook.py

Ready-made examples include esmfold_minibinder.py, esmfold_vhh.py, boltzgen_pipeline.py, and batched_protenix.py.

Core idea

A design objective is built from LossTerm objects that you add and scale with plain Python arithmetic, then hand to an optimizer.

import mosaic.losses.structure_prediction as sp

# Compose a loss from interface, confidence, and inverse-folding terms
design_loss = (
    sp.BinderTargetContact()
    + sp.WithinBinderContact()
    + 0.05 * sp.TargetBinderPAE()
    + 0.05 * sp.BinderTargetPAE()
    + 0.025 * sp.IPTMLoss()
    + 0.1 * sp.PLDDTLoss()
)

Loss terms can wrap one model used several ways (for example a structure predictor scoring both the binder-target complex and the binder as a monomer). Composing different architectures also lowers the chance of finding adversarial sequences that fool a single predictor.

What you can compose

Category	Options
Structure predictors	AF2, Boltz-1, Boltz-2, Protenix, OpenFold3, ESMFold2
Generative / design	BoltzGen, Proteina-Complexa
Inverse folding	ProteinMPNN, SolubleMPNN, AbMPNN
Language models	ESM-2, ESM-C, AbLang, trigram
Property heads	Stability (megascale-trained)

Optimizers

Optimizer	Use
simplex_APGM	Default; proximal gradient / mirror descent on the probability simplex
batched_simplex_APGM	The same, vmapped over many designs
gradient_MCMC	Discrete moves for fine-tuning a sequence

A reasonable simplex_APGM step size is about 0.1 * sqrt(binder_length).

Worked example: ranking with ipSAE

The published Nipah competition recipe optimizes a design loss on Boltz-2, then ranks candidates with a separate multi-sample loss built from ipTM and ipSAE. The multi-sample loss is a method on the Boltz2 model, not a free function:

from mosaic.models.boltz2 import Boltz2

boltz2 = Boltz2()
ranking_loss = boltz2.build_multisample_loss(
    loss=1.00 * sp.IPTMLoss()
    + 0.5 * sp.TargetBinderIPSAE()
    + 0.5 * sp.BinderTargetIPSAE(),
    features=design_features,
    num_samples=6,
    recycling_steps=3,
)

On the Adaptyv Nipah de novo target, this recipe produced 8 binders out of 9 tested designs at nanomolar affinity, the highest hit-rate of any method on that target in the public results. That is a small, expert-tuned sample on one hard target, not a guarantee across targets, so treat Mosaic as a high-ceiling option that rewards careful objective design rather than a turnkey default.

Two practices from that work are worth carrying over:

• Let the optimizer choose the epitope. Asking for a binder, without fixing hotspots, can find a better interface than a manually chosen one.
• Match filter stringency to assay throughput. With high-throughput testing, filter lightly to keep diversity rather than applying heavy consensus filters that can reject good binders.

Decision tree

Should I use Mosaic?
│
├─ Need a custom objective across multiple models? → Mosaic
├─ Want one-click binders with default filters?    → bindcraft
├─ Want all-atom diffusion design?                  → boltzgen
└─ Want backbone-only diversity?                    → rfdiffusion + proteinmpnn

Cost

Adaptyv's own tests of these models showed Mosaic costing about $0.55 per accepted design, averaged across 7 targets, among the cheapest per design of the methods tested. That is compute only; the setup and tuning effort is the real cost of using Mosaic.

Troubleshooting

Issue	Cause	Fix
Designs fail simple in-silico checks	Under-constrained objective	Add inverse-folding and confidence terms; filter with protein-qc
Optimization unstable	Step size too large	Lower the simplex_APGM step size
First call very slow	JIT compilation	Expected; reuse the compiled loss across designs
OOM with large predictors	Several models in one loss	Use smaller predictors or a larger GPU

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Next: Validate designs with boltz or chai, rank with ipsae, then filter with protein-qc.