| --- |
| tags: |
| - Biology |
| - Protein |
| - Structure Prediction |
| license: agpl-3.0 |
| library_name: ofoldx |
| pipeline_tag: other |
| --- |
| |
| # AlphaFold 2 |
|
|
| Pre-trained model for protein structure prediction from amino-acid sequences, multiple-sequence alignments, and optional structural templates. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of the [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2) by John Jumper, et al. |
|
|
| The OFFICIAL repository of AlphaFold 2 is at [google-deepmind/alphafold](https://github.com/google-deepmind/alphafold). |
|
|
| **The team releasing AlphaFold 2 did not write this model card for this model so this model card has been written by the OFoldX team.** |
|
|
| ## Model Details |
|
|
| AlphaFold 2 jointly updates MSA and residue-pair representations with an Evoformer, then uses an |
| invariant-point-attention structure module to predict backbone frames, side-chain angles, atomic coordinates, and |
| confidence. AlphaFold-Multimer extends the model with paired MSAs and chain-relative features for protein complexes. |
|
|
| Please refer to the [Training Details](#training-details) section for more information on the training process. |
|
|
| ### Variants |
|
|
| - **[oteam/alphafold2](https://huggingface.co/oteam/alphafold2)**: Monomer structure prediction with pLDDT confidence. |
| - **[oteam/alphafold2-ptm](https://huggingface.co/oteam/alphafold2-ptm)**: Monomer prediction with PAE and pTM confidence. |
| - **[oteam/alphafold2-multimer](https://huggingface.co/oteam/alphafold2-multimer)**: Multi-chain protein complex prediction. |
| - **[oteam/alphafold2-casp14](https://huggingface.co/oteam/alphafold2-casp14)**: The five-model CASP14 protocol with eight input ensembles per checkpoint. |
| - **[oteam/alphafold2-initial-guess](https://huggingface.co/oteam/alphafold2-initial-guess)**: The `model_1_ptm` initial-guess evaluator used by PXDesign. |
|
|
| ### Model Specification |
|
|
| | Variant | Evoformer Blocks | Extra-MSA Blocks | Structure Blocks | MSA Size | Pair Size | Single Size | Recycling Steps | Num Parameters (M) | |
| | ----------- | ---------------- | ---------------- | ---------------- | -------- | --------- | ----------- | --------------- | ------------------ | |
| | Monomer | 48 | 4 | 8 | 256 | 128 | 384 | 4 | 93.229 | |
| | Monomer pTM | 48 | 4 | 8 | 256 | 128 | 384 | 4 | 93.237 | |
| | Multimer | 48 | 4 | 8 | 256 | 128 | 384 | 21 | 93.224 | |
|
|
| ### Links |
|
|
| - **Checkpoint**: `model_1`, converted from [official parameter archive](https://storage.googleapis.com/alphafold/alphafold_params_2022-12-06.tar) |
| - **Upstream license**: [CC BY 4.0](LICENSE) |
| - **Upstream attribution**: AlphaFold 2 parameters © 2022 DeepMind Technologies Limited; [parameter license](https://github.com/google-deepmind/alphafold#model-parameters) |
| - **Upstream source revision**: `c77e5d2a8961d1a353632c462914ff0a32a950f6` |
| - **Code**: [ofoldx.models.alphafold2](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/models/alphafold2) |
| - **Paper**: [Highly accurate protein structure prediction with AlphaFold](https://doi.org/10.1038/s41586-021-03819-2); [Protein complex prediction with AlphaFold-Multimer](https://doi.org/10.1101/2021.10.04.463034) |
| - **Developed by**: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, |
| Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, |
| Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, |
| Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, |
| Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis |
| Hassabis |
| - **Model type**: MSA- and template-conditioned protein structure predictor with an Evoformer trunk and |
| invariant-point-attention structure module |
| - **Original Repository**: [google-deepmind/alphafold](https://github.com/google-deepmind/alphafold) |
|
|
| ## Usage |
|
|
| The model file depends on the [`ofoldx`](https://ofoldx.valhalla.technology/) library. You can install it using pip: |
|
|
| ```bash |
| pip install ofoldx |
| ``` |
|
|
| ### Direct Use |
|
|
| #### Structure Prediction |
|
|
| You can use this model directly with a pipeline for protein structure prediction: |
|
|
| ```python |
| import ofoldx |
| from ofoldx.io import read_structure |
| |
| structure, report = read_structure("input.cif") |
| pipeline = ofoldx.pipeline("fold", model="oteam/alphafold2") |
| result = pipeline(structure, seed=7) |
| ``` |
|
|
| The result contains predicted structures and confidence values. Each prediction uses a new coordinate frame; |
| unpredicted atoms, including ligands, hydrogens, and terminal OXT, have unavailable coordinates. |
|
|
| ### Downstream Use |
|
|
| #### CASP14 Ensemble |
|
|
| You can use the five-model ensemble to rank predictions and optionally relax the best structure: |
|
|
| ```python |
| import ofoldx |
| from ofoldx.io import read_structure |
| |
| structure, report = read_structure("input.cif") |
| pipeline = ofoldx.pipeline("fold", model="oteam/alphafold2-casp14") |
| result = pipeline(structure, num_samples=1, models_to_relax="best", platform="CPU", seed=7) |
| best = result.best |
| ``` |
|
|
| Each checkpoint evaluates eight input ensembles from shared preparation. `result.ranked` retains checkpoint names, |
| sample indices, and relaxation results; equal mean-pLDDT scores retain generation order. Amber relaxation requires |
| [OpenMM](https://docs.openmm.org/latest/userguide/application/01_getting_started.html) and |
| [PDBFixer](https://github.com/openmm/pdbfixer), installed in the same environment: |
|
|
| ```bash |
| conda install -c conda-forge openmm pdbfixer |
| ``` |
|
|
| Relaxation requires a closed, standard linear protein graph. Relaxed coordinates replace experimental coordinate |
| metadata; source identity and independent chemical facts are retained. Ordinary unrelaxed prediction needs neither |
| additional package. |
|
|
| #### Initial-Guess Evaluation |
|
|
| You can evaluate a sequence-assigned target-binder complex using its existing coordinates as the initial guess: |
|
|
| ```python |
| import ofoldx |
| from ofoldx.io import read_structure |
| |
| structure, report = read_structure("target_binder.cif") |
| pipeline = ofoldx.pipeline("fold", model="oteam/alphafold2-initial-guess") |
| result = pipeline(structure, binder_instance_id="binder", num_recycles=4, seed=7) |
| ``` |
|
|
| The input must contain the named binder instance. The initial guess seeds recycled atom37 positions; the dedicated |
| processor retains the target-binder template masking used by PXDesign. |
|
|
| See the [pipeline guide](https://github.com/OTeam-AI4S/OFoldX/blob/main/ofoldx/pipelines/README.md) for staged execution and composition. |
|
|
| ## Training Details |
|
|
| AlphaFold 2 is trained for protein structure prediction with structural and masked-MSA objectives. |
|
|
| ### Training Data |
|
|
| The AlphaFold 2 and AlphaFold-Multimer papers describe the upstream protein structure, sequence, and template corpora. |
| OFoldX does not redistribute those corpora or databases. |
|
|
| ### Training Procedure |
|
|
| The upstream models use recycling and structural and auxiliary objectives. The pTM variants were fine-tuned with a |
| predicted-aligned-error head; AlphaFold-Multimer adds paired-MSA and chain-relative features and interface confidence. |
|
|
| OFoldX exposes a differentiable final recycle and a masked-MSA reconstruction objective for that recycle. This does not |
| reproduce the complete upstream structural and auxiliary-loss training program. MSA, template, and sequence-database |
| generation remain caller-owned. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{jumper2021highly, |
| author = {Jumper, John and Evans, Richard and Pritzel, Alexander and others}, |
| title = {Highly accurate protein structure prediction with AlphaFold}, |
| journal = {Nature}, |
| volume = {596}, |
| pages = {583--589}, |
| year = {2021}, |
| doi = {10.1038/s41586-021-03819-2} |
| } |
| ``` |
|
|
| For AlphaFold-Multimer predictions, also cite |
| [Protein complex prediction with AlphaFold-Multimer](https://doi.org/10.1101/2021.10.04.463034). |
|
|
| > [!NOTE] |
| > The artifacts distributed in this repository are part of the OFoldX project. |
| > If OFoldX supports your research, please cite the OFoldX project as follows: |
|
|
| ```bibtex |
| @software{chen2026ofoldx, |
| author = {Chen, Zhiyuan}, |
| title = {OFoldX}, |
| year = {2026}, |
| url = {https://github.com/OTeam-AI4S/OFoldX}, |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Please use GitHub issues of [OFoldX](https://github.com/OTeam-AI4S/OFoldX/issues) for any questions or comments on the model implementation or model card. |
|
|
| Please contact the authors of the [AlphaFold 2 paper](https://doi.org/10.1038/s41586-021-03819-2) for questions or comments on the paper/model. |
|
|
| ## License |
|
|
| The model weights, implementation, configuration, and documentation distributed by OFoldX are licensed under the |
| [GNU Affero General Public License](license.md). |
|
|
| For additional terms and clarifications, please refer to our |
| [License FAQ](license-faq.md). |
|
|
| ```spdx |
| SPDX-License-Identifier: AGPL-3.0-or-later |
| ``` |
|
|
