solublempnn

To design soluble protein sequences for E. coli expression, you can use SolubleMPNN to generate solubility-optimized sequences for a given backbone. It applies soluble model weights to enhance expression yields and reduce protein aggregation.

Yes, SolubleMPNN supports ligand-aware LigandMPNN workflows alongside standard ProteinMPNN design. This allows you to generate sequences optimized for solubility while accounting for specific ligand interactions.

Aggregation reduction during sequence design works by applying soluble model weights to optimize amino acid selection for a given backbone. This generates sequences with improved folding propensity and reduced aggregation tendency for experimental testing.

You do not strictly need a local ProteinMPNN setup to run solubility optimization. The skill supports running through Modal's soluble design workflow, or alternatively, you can use a local ProteinMPNN installation with the soluble model weights.

The best way to evaluate protein solubility after sequence design is to generate multiple sequences per backbone, assess their solubility and folding propensity computationally, and select the top candidates for experimental testing.