Protein function prediction is currently achieved by encoding its sequence or
structure, where the sequence-to-function transcendence and high-quality
structural data scarcity lead to obvious performance bottlenecks. Protein
domains are "building blocks" of proteins that are functionally independent,
and their combinations determine the diverse biological functions. However,
most existing studies have yet to thoroughly explore the intricate functional
information contained in the protein domains. To fill this gap, we propose a
synergistic integration approach for a function-aware domain representation,
and a domain-joint contrastive learning strategy to distinguish different
protein functions while aligning the modalities. Specifically, we associate
domains with the GO terms as function priors to pre-train domain embeddings.
Furthermore, we partition proteins into multiple sub-views based on continuous
joint domains for contrastive training under the supervision of a novel triplet
InfoNCE loss. Our approach significantly and comprehensively outperforms the
state-of-the-art methods on various benchmarks, and clearly differentiates
proteins carrying distinct functions compared to the competitor.

我们提出了一种融合功能感知域表示和域关联对比学习策略的协同集成方法，通过将域与 GO 项作为功能先验与域嵌入进行预训练，然后基于连续关联的域将蛋白质划分成多个子视图并使用新型三元组 InfoNCE 损失进行对比训练，明显且全面地超越了各种基准方法，在区分携带不同功能的蛋白质方面与竞争者有明显差异。

ProtFAD：引入功能感知领域作为蛋白质功能认知的隐性机制

ProtFAD: Introducing function-aware domains as implicit modality towards  protein function perception

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% vs
4.7% in zinc finger, and 54.3% vs 8.7% in the immunoglobulin domain) in
comparison to the existing model.

利用从蛋白质数据库中提取的文本注释进行生成以及多模态对齐，PAAG（Protein-Annotation Alignment Generation）是一个多模态蛋白质设计框架，可以显式地在序列空间中生成带有特定领域条件的蛋白质，并且可以设计具有不同类型注释的新型蛋白质。与现有模型相比，PAAG 的实验结果在 7 个预测任务中显示出对齐蛋白质表示的优越性，并且在生成成功率方面显著提高（锌指蛋白为 24.7％对比 4.7％，免疫球蛋白结构域为 54.3％对比 8.7％）。