OVO, an open-source ecosystem for de novo protein design
OVO (pronounced "oh-voh") consolidates models, workflows, data management, and interactive visualization into a scalable, high-performance, infrastructure-agnostic platform for de novo protein design. OVO features Nextflow-based workflow orchestration, a storage layer, and both command-line and web interfaces that democratize scaffold design, binder design and diversification, and validation workflows.
Ovo, an Open-Source Ecosystem for De Novo Protein Design, David Prihoda, Marco Ancona, Tereza Calounova, Adam Kral, Lukas Polak, Hugo Hrban, Nicholas J. Dickens, Danny Asher Bitton bioRxiv 2025.11.27.691041; doi: https://doi.org/10.1101/2025.11.27.691041
🐣 Getting started
To get started with OVO, please refer to the User Guide.
To preview the OVO web app (without the ability to submit jobs), see the OVO Demo Server.
▶️ Demo video
https://github.com/user-attachments/assets/7b339fa6-c6de-467d-90d0-5cd15f83c498
🧬 Methods & Acknowledgments
We gratefully acknowledge the authors and developers of the following methods and tools available from OVO:
| Method Name | Description | Reference / Paper | Link |
|---|---|---|---|
| RFdiffusion | Diffusion-based protein structure generation | Watson et al. 2023 | GitHub |
| ProteinMPNN | Protein sequence design for fixed backbones | Dauparas et al. 2022 | GitHub |
| LigandMPNN | Atomic context-conditioned protein sequence design | Dauparas et al. 2025 | GitHub |
| PyRosetta FastRelax | Binder sequence design protocol | Bennett et al. 2023 | GitHub |
| AlphaFold2 / ColabDesign | Deep learning-based protein structure prediction | Jumper et al. 2021 | GitHub (AlphaFold2), GitHub (ColabDesign) |
| BindCraft | Binder design using AF2 backpropagation | Pacesa et al. 2024 | GitHub |
| Boltz | Deep learning-based protein structure prediction | Wohlwend et al. 2024, Passaro et al. 2025 | GitHub |
| ESM-1v | Protein language model for variant effect | Meier et al. 2021 | GitHub |
| ESM-IF | Inverse folding with protein language models | Hsu et al. 2022 | GitHub |
| DSSP | Secondary structure assignment | Kabsch & Sander 1983, Joosten et al. 2010 | GitHub |
| PEP-Patch | Protein surface patches analysis | Kufareva et al. 2023 | GitHub |
| Protein-Sol | Protein solubility prediction | Hebditch et al. 2017 | Web |
🛠️ Development
OVO is an open-source project and we welcome contributions from the community.
Please refer to the Developer Guide for information on how to contribute to OVO.
Metadata
Release files for ovo 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| ovo-1.1.0.tar.gz | 3.0 MB | Without provenance | Details for ovo-1.1.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| ovo-1.1.0-py3-none-any.whl | Python 3 | none | any | Details for ovo-1.1.0-py3-none-any.whl |
Total release size: 6.1 MB
Release files / ovo-1.1.0.tar.gz
| Download URL | ovo-1.1.0.tar.gz |
|---|---|
| Size | 3.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
106e1f2af643b67d2e9858ad81e31030f2ec3c95fca133d37e7a7b684095ddfe
|
|
BLAKE2b-256 checksum How to use checksums |
c0d85ce25ee2e17baeac1f91b1ba658c62cc3cfc9790a4ab23f5c342cfacd2b3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.15
|
Release files / ovo-1.1.0-py3-none-any.whl
| Download URL | ovo-1.1.0-py3-none-any.whl |
|---|---|
| Size | 3.2 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
9bae1cf9c0b6699670920da66ec1481d9cd9ae8a7dfab55c2989376c55e9e3de
|
|
BLAKE2b-256 checksum How to use checksums |
68dc831097ff1464eb9656f3e205ed62306bb84b29e90ebfa45845885144b467
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.15
|
Security for ovo 1.1.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.