A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity.

ABSTRACT: Deep learning-based methods for generating functional proteins address the growing need for novel biocatalysts, allowing for precise tailoring of functionalities to meet specific requirements. This advancement leads to the development of highly efficient and specialized proteins with diverse applications across scientific, technological, and biomedical fields. This study establishes a pipeline for protein sequence generation with a conditional protein diffusion model, namely CPDiffusion, to create diverse sequences of proteins with enhanced functions. CPDiffusion accommodates protein-specific conditions, such as secondary structures and highly conserved amino acids. Without relying on extensive training data, CPDiffusion effectively captures highly conserved residues and sequence features

SUBMITTER: Zhou B 

PROVIDER: S-EPMC11385924 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature