Bingxin Zhou, Lirong Zheng, Banghao Wu, Kai Yi, Bozitao Zhong, Yang Tan, Qian Liu, Pietro Liò, Liang Hong
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引用次数: 0
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 for specific protein families. We applied CPDiffusion to generate artificial sequences of Argonaute (Ago) proteins based on the backbone structures of wild-type (WT) Kurthia massiliensis Ago (KmAgo) and Pyrococcus furiosus Ago (PfAgo), which are complex multi-domain programmable endonucleases. The generated sequences deviate by up to nearly 400 amino acids from their WT templates. Experimental tests demonstrated that the majority of the generated proteins for both KmAgo and PfAgo show unambiguous activity in DNA cleavage, with many of them exhibiting superior activity as compared to the WT. These findings underscore CPDiffusion’s remarkable success rate in generating novel sequences for proteins with complex structures and functions in a single step, leading to enhanced activity. This approach facilitates the design of enzymes with multi-domain molecular structures and intricate functions through in silico generation and screening, all accomplished without the need for supervision from labeled data.
Cell DiscoveryBiochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
24.20
自引率
0.60%
发文量
120
审稿时长
20 weeks
期刊介绍:
Cell Discovery is a cutting-edge, open access journal published by Springer Nature in collaboration with the Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences (CAS). Our aim is to provide a dynamic and accessible platform for scientists to showcase their exceptional original research.
Cell Discovery covers a wide range of topics within the fields of molecular and cell biology. We eagerly publish results of great significance and that are of broad interest to the scientific community. With an international authorship and a focus on basic life sciences, our journal is a valued member of Springer Nature's prestigious Molecular Cell Biology journals.
In summary, Cell Discovery offers a fresh approach to scholarly publishing, enabling scientists from around the world to share their exceptional findings in molecular and cell biology.