Document webProteinMPNN browser model assets

README.md

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+---
+license: mit
+library_name: onnxruntime
+tags:
+  - browser
+  - onnx
+  - wasm
+  - webgpu
+  - protein-design
+  - proteinmpnn
+---
+
+# MurrellLab webports
+
+Browser-ready model assets for unofficial web ports maintained by MurrellLab.
+This repository stores model files that are too large or unsuitable for the
+ordinary Git history of the corresponding software repositories.
+
+## webProteinMPNN
+
+`webProteinMPNN/` contains nine ONNX model bundles converted from the official
+ProteinMPNN checkpoints. They support ProteinMPNN, solubleMPNN, and CA-only
+ProteinMPNN inference through ONNX Runtime Web using WebAssembly or WebGPU.
+
+These models were trained and released by the original ProteinMPNN authors.
+MurrellLab converted the checkpoints into a browser-oriented graph format; it
+did not train the underlying models. The source checkpoint SHA-256, upstream
+commit, exported-file hashes, tensor dimensions, and atom mode are recorded in
+each `manifest.json`. A collection-wide index is available at
+`webProteinMPNN/catalog.json`.
+
+Code, usage documentation, and validation details are maintained at
+https://github.com/MurrellGroup/webProteinMPNN.
+
+## License and attribution
+
+The upstream ProteinMPNN code and checkpoints are MIT licensed. The upstream
+notice is preserved in `webProteinMPNN/LICENSE.ProteinMPNN`, with additional
+provenance in `webProteinMPNN/THIRD_PARTY_NOTICES.md`. Please cite:
+
+> Dauparas J, Anishchenko I, Bennett N, et al. Robust deep learning-based
+> protein sequence design using ProteinMPNN. Science. 2022;378(6615):49-56.
+> https://doi.org/10.1126/science.add2187
+
+## Intended use and limitations
+
+The bundles are intended for research use in protein sequence design from
+protein backbone or CA-only coordinates. They preserve the behavior and
+limitations of the corresponding upstream checkpoint; conversion does not
+constitute new training or biological validation. Generated sequences require
+independent computational and experimental evaluation. These files are not a
+hosted inference API and are not suitable for clinical decision-making.
+

webProteinMPNN/LICENSE.ProteinMPNN

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+MIT License
+
+Copyright (c) 2022 Justas Dauparas
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

webProteinMPNN/README.md

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+# webProteinMPNN model assets
+
+This directory contains browser-oriented ONNX conversions of the official
+ProteinMPNN checkpoint families used by
+https://github.com/MurrellGroup/webProteinMPNN.
+
+## Layout
+
+Each model directory contains:
+
+- `manifest.json`: dimensions, atom mode, filenames, SHA-256 integrity hashes,
+  upstream commit, and source checkpoint SHA-256.
+- `encoder.onnx`: structure encoder with dynamic residue and neighbor axes.
+- `decoder-step.onnx`: incremental autoregressive decoder.
+- `decoder-full.onnx`: parallel decoder used for scoring and probabilities.
+- `embedding.bin`: little-endian float32 sequence-token embedding table.
+
+Available families and checkpoints are listed in `catalog.json`. ProteinMPNN
+and solubleMPNN models consume N/CA/C/O backbone coordinates; CA models consume
+CA-only coordinates. All bundles use the alphabet `ACDEFGHIKLMNPQRSTVWYX`.
+
+The conversions were produced by `proteinmpnn-web/0.1.0` from upstream commit
+`8907e6671bfbfc92303b5f79c4b5e6ce47cdef57`. They are designed for ONNX
+Runtime Web 1.24.3 and can run with its WASM or WebGPU execution provider.
+
+## Attribution
+
+The original models are ProteinMPNN by Dauparas et al., not models trained by
+MurrellLab. See `LICENSE.ProteinMPNN` and `THIRD_PARTY_NOTICES.md` in this
+directory, and the repository model card for intended use and limitations.
+

webProteinMPNN/THIRD_PARTY_NOTICES.md

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+# Third-party notices
+
+The source `vendor/protein_mpnn_utils.py`, reference script `vendor/protein_mpnn_run.py`, official checkpoint weights, and example PDB fixture are from [dauparas/ProteinMPNN](https://github.com/dauparas/ProteinMPNN), commit `8907e6671bfbfc92303b5f79c4b5e6ce47cdef57`. The original MIT notice is reproduced in `vendor/LICENSE.ProteinMPNN` and applies to those upstream materials and derived portions of the exporter/sampling implementation. Converted model bundles preserve their checkpoint SHA-256 in each manifest.
+
+ProteinMPNN citation: Dauparas J. et al. Robust deep learning–based protein sequence design using ProteinMPNN. *Science* 378, 49–56 (2022). DOI: [10.1126/science.add2187](https://doi.org/10.1126/science.add2187).
+
+ONNX Runtime Web is distributed under the MIT license by Microsoft and its contributors. It is an npm dependency, with its own notices in `node_modules/onnxruntime-web`. Development/export tools also use PyTorch (BSD-style), NumPy (BSD), ONNX (Apache-2.0), TypeScript (Apache-2.0), esbuild (MIT), and Playwright (Apache-2.0). Their code is not vendored in the release archives.

webProteinMPNN/catalog.json

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+{
+  "schema": 1,
+  "collection": "webProteinMPNN",
+  "packageVersion": "0.1.0",
+  "created": "2026-09-08",
+  "sourceRepository": "https://github.com/MurrellGroup/webProteinMPNN",
+  "upstream": {
+    "repository": "https://github.com/dauparas/ProteinMPNN",
+    "commit": "8907e6671bfbfc92303b5f79c4b5e6ce47cdef57"
+  },
+  "runtime": {
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+  },
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