Pre-trained protein models (PTPMs) represent a protein with one fixed
embedding and thus are not capable for diverse tasks. For example, protein
structures can shift, namely protein folding, between several conformations in
various biological processes. To enable PTPMs to produce task-aware
representations, we propose to learn interpretable, pluggable and extensible
protein prompts as a way of injecting task-related knowledge into PTPMs. In
this regard, prior PTPM optimization with the masked language modeling task can
be interpreted as learning a sequence prompt (Seq prompt) that enables PTPMs to
capture the sequential dependency between amino acids. To incorporate
conformational knowledge to PTPMs, we propose an interaction-conformation
prompt (IC prompt) that is learned through back-propagation with the
protein-protein interaction task. As an instantiation, we present a
conformation-aware pre-trained protein model that learns both sequence and
interaction-conformation prompts in a multi-task setting. We conduct
comprehensive experiments on nine protein datasets. Results confirm our
expectation that using the sequence prompt does not hurt PTPMs' performance on
sequence-related tasks while incorporating the interaction-conformation prompt
significantly improves PTPMs' performance on tasks where conformational
knowledge counts. We also show the learned prompts can be combined and extended
to deal with new complex tasks.

通过学习可解释、可插入、可扩展的蛋白质提示，将任务相关知识注入预训练蛋白质模型（PTPMs），从而使得 PTPMs 能够生成任务感知的表示，进而在多任务设置下，学习同时序列与交互构象提示的构象感知预训练蛋白质模型，实验证明这种方法显著提高了 PTPMs 在需要构象知识的任务上的性能。