Ovo, an open-source ecosystem for de novo protein design

Area of Science:

• Computational Biology
• Protein Engineering
• Bioinformatics

Background:

• The field of de novo protein design is rapidly advancing with new models and pipelines.
• Current tools are fragmented, difficult to install, and require significant computational expertise.
• Managing sequences, structures, and metrics for testing and analysis presents a major challenge.

Purpose of the Study:

• To introduce Ovo, an open-source de novo protein design ecosystem.
• To consolidate models, workflows, data management, and visualization into a scalable platform.
• To lower engineering barriers and democratize protein design for experts and non-technical users.

Main Methods:

• Ovo utilizes Nextflow for workflow orchestration and includes a storage layer.
• It offers both command-line and graphical interfaces for various design tasks.
• The ProteinQC module computes sequence and structure descriptors for design contextualization.

Main Results:

• Ovo consolidates fragmented tools into a unified, scalable, and infrastructure-agnostic platform.
• It democratizes scaffold design, binder design, diversification, and validation workflows.
• The platform facilitates large-scale protein design and analysis through its integrated features.

Conclusions:

• Ovo lowers barriers to entry for de novo protein design, enabling broader user access.
• Its plugin architecture allows community contributions, accelerating method adoption and benchmarking.
• Ovo has the potential to accelerate de novo protein design and advance therapeutics and biotechnology discovery.