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Related Experiment Video

Updated: Jun 30, 2026

Engineering and Evolution of Synthetic Adeno-Associated Virus AAV Gene Therapy Vectors via DNA Family Shuffling

Engineering and Evolution of Synthetic Adeno-Associated Virus AAV Gene Therapy Vectors via DNA Family Shuffling

Published on: April 2, 2012

Steering Protein Generative Models at Test-Time for Guided AAV2 Capsid Design.

Ben Viggiano¹, Wenhui Sophia Lu², Xiaowei Zhang³

¹Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

Summary

This summary is machine-generated.

ProVADA+ efficiently designs proteins with desired functions using a novel adaptive masking technique. This framework guides generative models without retraining, accelerating the creation of novel Adeno-Associated Virus 2 capsids.

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Published on: October 18, 2022

Area of Science:

Protein engineering
Computational biology
Bioinformatics

Background:

Protein generative models offer new engineering possibilities.
Steering models for specific functionalities, especially with black-box fitness functions, is challenging.

Purpose of the Study:

To present ProVADA+, a model-agnostic framework for guiding pretrained protein generative models.
To enable the design of proteins with specific, desired functionalities without retraining.

Main Methods:

ProVADA+ employs a reinforcement learning-based adaptive masking technique (MADA-DUCB) for accelerated convergence.
The framework couples a ProteinMPNN generative prior with a fine-tuned Adeno-Associated Virus 2 (AAV2) viability oracle.

Main Results:

ProVADA+ successfully navigated a rugged fitness landscape for designing novel AAV2 capsids.
The method generated novel candidates with a mean viral selection score of 2.72, identifying highly viable variants.
The generated variants maintained sequence diversity relative to the wildtype.

Conclusions:

ProVADA+ is a powerful and efficient framework for accelerating protein design.
The approach effectively designs proteins with complex, user-defined properties, overcoming limitations of traditional methods.