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Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention.

BIOCOMPUTING 2022, PSB 2022(2022)

Univ Calif Berkeley | Univ Washington | Cold Spring Harbor Lab | Harvard Univ

Cited 2|Views31
Abstract
The established approach to unsupervised protein contact prediction estimates coevolving positions using undirected graphical models. This approach trains a Potts model on a Multiple Sequence Alignment. Increasingly large Transformers are being pretrained on unlabeled, unaligned protein sequence databases and showing competitive performance on protein contact prediction. We argue that attention is a principled model of protein interactions, grounded in real properties of protein family data. We introduce an energy-based attention layer, factored attention, which, in a certain limit, recovers a Potts model, and use it to contrast Potts and Transformers. We show that the Transformer leverages hierarchical signal in protein family databases not captured by single-layer models. This raises the exciting possibility for the development of powerful structured models of protein family databases.
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Key words
Contact Prediction,Representation Learning,Language Modeling,Attention,Transformer,BERT,Markov Random Fields,Potts Models,Self-supervised learning
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要点】:该论文通过简化注意力机制的视角解释Potts和Transformer蛋白质模型,提出一种基于能量的注意力层,对比这两种模型在无监督蛋白质接触预测任务中的表现,并突显Transformer模型在利用蛋白质家族数据库中分层信号方面的优势。

方法】:研究者引入了一种能量基础的注意力层——分解注意力,这种注意力层在某种极限情况下可以恢复Potts模型,并将之用于比较Potts和Transformer模型。

实验】:通过在多个序列对齐上训练Potts模型,以及使用未标记、未对齐的蛋白质序列数据库预训练越来越大型的Transformer模型,实验表明Transformer模型在蛋白质接触预测上展现出竞争力,且能够捕捉到单层模型无法获得的蛋白质家族数据库中的层次信号。这一发现为开发强大的蛋白质家族数据库结构模型奠定了基础。