feat: update solublempnn docs

source/rest-api/embeddings.rst

@@ -24,6 +24,8 @@ Currently, we support the following models:
 
 - **ProteinMPNN**: A deep learning model for sequence design conditioned on a fixed protein backbone. It predicts amino acid sequences likely to fold into a given 3D structure by modeling residue–residue interactions through message passing on the protein’s spatial graph. Used for structure-based protein design, stability optimization, and inverse folding tasks. `GitHub link <https://github.com/dauparas/ProteinMPNN>`__, `Reference <https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1>`__. Licensed under `MIT <https://choosealicense.com/licenses/mit/>`__.
 
+- **SolubleMPNN**: A variant of ProteinMPNN trained with weights tuned for soluble protein targets. Uses the same inverse-folding pipeline as ProteinMPNN; `soluble_model` is implied by the route, and `model_name` is restricted to the soluble-compatible weights (`v_48_010`, `v_48_020`). `GitHub link <https://github.com/dauparas/ProteinMPNN>`__, `Reference <https://doi.org/10.1126/science.add2187>`__. Licensed under `MIT <https://choosealicense.com/licenses/mit/>`__.
+
 - **ESM-IF1**: An inverse-folding model pretrained on PDB and ~12 million AlphaFold-predicted structures. Given a backbone structure, it scores and generates amino acid sequences predicted to fold into it, making it another option for structure-based design and inverse folding alongside ProteinMPNN. `GitHub link <https://github.com/facebookresearch/esm>`__, `Reference <https://www.biorxiv.org/content/10.1101/2022.04.10.487779>`__. Licensed under `MIT <https://choosealicense.com/licenses/mit/>`__.
 
 - **AbLang2**: An antibody-specific language model that provides residue-level and sequence-level representations for both heavy and light chains. `GitHub link <https://github.com/oxpig/AbLang2>`__, `Reference <https://doi.org/10.1101/2024.02.02.578678>`__. Licensed under `BSD-3 <https://choosealicense.com/licenses/bsd-3-clause/>`__.