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Related Experiment Video

Updated: May 23, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Co-design protein sequence and structure in discrete space via generative flow.

Sen Yang¹, Lingli Ju², Peng Cheng¹

¹Bioinformatics Center of AMMS, Beijing, 100039, China.

Bioinformatics (Oxford, England)

|April 30, 2025

Summary

CoFlow, a novel discrete model, enables joint protein sequence and structure co-design. This generative approach significantly enhances design consistency and performance across various protein engineering tasks.

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Area of Science:

Computational Biology
Protein Engineering
Artificial Intelligence

Background:

Generative models show potential in de novo protein design.
Existing methods often isolate sequence or structure, limiting design optimization.
Joint protein sequence and structure co-design is an underexplored area.

Purpose of the Study:

To introduce CoFlow, a discrete model for joint protein sequence and structure co-design.
To address the limitations of traditional protein design approaches.
To facilitate exploration of the protein sequence-structure landscape.

Main Methods:

CoFlow utilizes a joint discrete flow architecture.
A multi-modal protein masked language model is integrated for co-design in the discrete space.
The model handles de novo design and designs with constraints.

Main Results:

CoFlow outperforms existing protein design methods across multiple metrics.
Achieves approximately eight times higher consistency than ESM3 in unconditional generation.
Demonstrates competitive performance in conditional tasks like motif-scaffolding, protein folding, and inverse folding.

Conclusions:

CoFlow represents a significant advancement in joint protein sequence and structure co-design.
The model's ability to integrate sequence and structure offers enhanced design capabilities.
CoFlow shows promise for various protein engineering applications.