Ovo, an Open-Source Ecosystem for De Novo Protein Design

Abstract The protein design field is rapidly advancing, with frequent emergence of new models and pipelines for designing de novo proteins with tailored properties and functions not found in nature. However, the current tool landscape is fragmented, tools are hard to install and deploy, and require significant computational expertise to integrate into end-to-end, scalable pipelines. A particular challenge is managing many sequences, structures, and metrics for downstream testing and retrospective analysis of input parameters. To address this need, we introduce Ovo, an open-source de novo protein design ecosystem that consolidates models, workflows, data management, and interactive visualization into a scalable, infrastructure-agnostic platform. Ovo features Nextflow-based workflow orchestration, a storage layer, and both command-line and graphical interfaces that democratize scaffold design, binder design and diversification, and validation workflows. Ovo’s novel ProteinQC module computes comprehensive sequence and structure descriptors, contextualizing designs against reference sets. Ovo plugins let the community add new workflows and user interfaces to accelerate adoption of emerging methods and facilitate community-driven benchmarking. Ovo lowers engineering barriers and demystifies the design process, allowing experts and non-technical users to design proteins at scale. With community-driven development, Ovo can accelerate de novo protein design and advance discovery in therapeutics and biotechnology. Competing Interest Statement All authors that are/were employees of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA may hold stocks and/or stock options in Merck & Co., Inc., Rahway, NJ, USA. Footnotes Data availability All code for this publication is available in the following GitHub repository: https://github.com/MSDLLCpapers/ovo. We also deployed a demo web application for demonstrating design visualization capabilities at https://ovo.dichlab.org. Documentation is available at https://ovo.dichlab.org/docs. De novo design workflow results presented in this publication are available through reproducible Jupyter notebooks at: https://github.com/MSDLLCpapers/ovo-examples.