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PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization
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DOI：10.1002/advs.202502723.png)
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Abstract
En 中文
The diffusion model has grasped enormous attention in the computer vision field and emerged as a promising algorithm in protein design for precise structure and sequence generation. Here PRO-LDM is introduced: a modular multi-tasking framework combining design fidelity and computational efficiency, by integrating the diffusion model in latent space. The model learns biological representations at local and global levels, to design natural-like species with enhanced diversity, or optimize protein properties and functions. Its modular nature also enables the integration with alternative pre-trained encoders for enhanced generalization capability. Outlier design can be implemented by adjusting the classifier-free guidance that enables PRO-LDM to sample vastly different regions in the latent space. The approach is demonstrated in generating a novel green-fluorescence-protein variant with notably enhanced fluorescence in multiple working scenarios along with increased solubility and stability. The model provides a versatile tool to effectively extract physicochemical and evolutionary information in sequences for designing new proteins with optimized performances.
Keywords:
latent diffusion models
conditional generation
protein sequence design
functional optimization
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