BERTology Meets Biology: Interpreting Attention in Protein Language Models (Paper Explained)

Proteins are the workhorses of almost all cellular functions and a core component of life. But despite their versatility, all proteins are built as sequences of the same 20 amino acids. These sequences can be analyzed with tools from NLP. This paper investigates the attention mechanism of a BERT model that has been trained on protein sequence data and discovers that the language model has implicitly learned non-trivial higher-order biological properties of proteins. OUTLINE: 0:00 - Intro & Overview 1:40 - From DNA to Proteins 5:20 - BERT for Amino Acid Sequences 8:50 - The Structure of Proteins 12:40 - Investigating Biological Properties by Inspecting BERT 17:45 - Amino Acid Substitution 24:55 - Contact Maps 30:15 - Binding Sites 33:45 - Linear Probes 35:25 - Conclusion & Comments Paper: https://ift.tt/2VGc5y6 Code: https://ift.tt/3idS1N9 My Video on BERT: https://youtu.be/-9evrZnBorM My Video on Attention: https://youtu.be/iDulhoQ2pro Abstract: Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and functional properties of proteins. We show that attention (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We also present a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with known biological processes and provide a tool to aid discovery in protein engineering and synthetic biology. The code for visualization and analysis is available at this https URL. Authors: Jesse Vig, Ali Madani, Lav R. Varshney, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB