Functional Protein Design with Local Domain Alignment
CoRR(2024)
摘要
The core challenge of de novo protein design lies in creating proteins with
specific functions or properties, guided by certain conditions. Current models
explore to generate protein using structural and evolutionary guidance, which
only provide indirect conditions concerning functions and properties. However,
textual annotations of proteins, especially the annotations for protein
domains, which directly describe the protein's high-level functionalities,
properties, and their correlation with target amino acid sequences, remain
unexplored in the context of protein design tasks. In this paper, we propose
Protein-Annotation Alignment Generation (PAAG), a multi-modality protein design
framework that integrates the textual annotations extracted from protein
database for controllable generation in sequence space. Specifically, within a
multi-level alignment module, PAAG can explicitly generate proteins containing
specific domains conditioned on the corresponding domain annotations, and can
even design novel proteins with flexible combinations of different kinds of
annotations. Our experimental results underscore the superiority of the aligned
protein representations from PAAG over 7 prediction tasks. Furthermore, PAAG
demonstrates a nearly sixfold increase in generation success rate (24.7
4.7
comparison to the existing model.
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