Designing AAV Capsid Protein with viability-guided Diffusion Model

Abstract

Adeno-associated virus (AAV) capsids have shown great promise as delivery vectors for gene therapy. However, the natural properties of AAV capsids impose significant limitations on their efficacy. While capsid library screening and directed evolution have facilitated the development of improved vectors, current methods for generating AAV libraries frequently yield nonviable variants that fail to assemble or package DNA. Here, we propose a viability-guided diffusion model AAVDiffusion for de novo viable AAV capsid design. AAVDiffusion iteratively denoises Gaussian vectors into vectors of AAV capsid protein sequences, yielding intermediate latent variables. By leveraging the continuous nature of these latent variables, AAVDiffusion integrates an additional viability classifier that applies gradient updates to enhance the generation of viable AAV sequences. Through extensive computational tests, AAVDiffusion exhibits superior performance in generating viable AAV sequences. Furthermore, 196 AAV candidates were identified through a selection workflow, with the potential to cross the blood-brain barrier, thereby offering safer and more effective gene therapies for brain diseases. AAVDiffusion offers a powerful and efficient computational method for designing viable AAV capsids, advancing the development of AAV vectors for gene therapy.