Annotation-guided Protein Design
with
Multi-Level Domain Alignment

KDD 2025

Chaohao Yuan2, Songyou Li4, Geyan Ye1, #, Yikun Zhang5, Long-Kai Huang1, Wenbing Huang4,
Wei Liu1, Jianhua Yao1, Yu Rong3, *
# Project Lead, * Corresponding Author
1Tencent AI Lab, 2Tsinghua University, 3DAMO Academy, Alibaba Group,
4Renmin University of China, 5Peking University
Paper arXiv Code Model Weights Datasets

Abstract

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 significant increase in generation success rate (24.7% vs 4.7% in zinc finger, and 54.3% vs 22.0% in the immunoglobulin domain) in comparison to the existing model.

We anticipate that PAAG will broaden the horizons of protein design by leveraging the knowledge from between textual annotation and proteins.

Problem Statement

Nerfies Portrait

(a) The example of property annotations (in bold) and domain annotations (in colors).

(b). The illustration of annotation-guided protein design with PAAG. Given the input of textual description within immunoglobulin domain annotation, PAAG can generate the proteins containing immunoglobulin domain.

Proposed Framework

Nerfies Portrait

The overall framework of PAAG. The same parameters share the same color. PAAG contains three modules. (1) Protein & Annotation Encoding module encode the input protein sequence & domains and corresponding annotations to the embeddings. (2) Multi-level alignment module projects the protein and annotation embeddings into and employs Annotation-Protein Contrasive (APC) loss, Annotation-Domain Contrasive (ADC) loss and Annotation-Protein Matching (APM) loss to align them in a same latent space. (3) Conditional Protein Decoding accepts the annotation embedding as input and generate the protein sequence.

BibTeX

@article{yuan2024functional,
  author    = {Yuan, Chaohao and Li, Songyou and Ye, Geyan and Zhang, Yikun and Huang, Long-Kai and Huang, Wenbing and Liu, Wei and Yao, Jianhua and Rong, Yu},
  title     = {Functional Protein Design with Local Domain Alignment},
  journal   = {arXiv preprint arXiv:2404.16866},
  year      = {2024},
}