BERTology Meets Biology: Interpreting Attention in Protein Language\n Models

2020/06/26 by Jesse Vig, Vig, Jesse, Ali Madani +9 · 15 citations
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Biomolecules (q-bio.BM) #Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #I.2 #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Protein Structure and Dynamics

paper · pdf · doi:10.48550/arxiv.2006.15222

openalex publication_date 2020/06/26 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

Transformer architectures have proven to learn useful representations for\nprotein classification and generation tasks. However, these representations\npresent challenges in interpretability. In this work, we demonstrate a set of\nmethods for analyzing protein Transformer models through the lens of attention.\nWe show that attention: (1) captures the folding structure of proteins,\nconnecting amino acids that are far apart in the underlying sequence, but\nspatially close in the three-dimensional structure, (2) targets binding sites,\na key functional component of proteins, and (3) focuses on progressively more\ncomplex biophysical properties with increasing layer depth. We find this\nbehavior to be consistent across three Transformer architectures (BERT, ALBERT,\nXLNet) and two distinct protein datasets. We also present a three-dimensional\nvisualization of the interaction between attention and protein structure. Code\nfor visualization and analysis is available at\nhttps://github.com/salesforce/provis.\n

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