Learning protein sequence embeddings using information from structure

TL;DR

This paper introduces a novel representation learning framework that generates protein sequence embeddings encoding structural information, improving structural similarity prediction and downstream tasks like transmembrane domain prediction.

Contribution

The authors develop a multi-task LSTM-based approach that learns position-specific protein embeddings from sequence and structural similarity data, outperforming existing methods.

Findings

Outperforms other sequence-based methods in structural similarity prediction.

Embeddings transfer effectively to improve transmembrane domain prediction.

Uses a novel soft symmetric alignment measure for sequence similarity.

Abstract

Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins. We newly approach this problem through the lens of representation learning. We introduce a framework that maps any protein sequence to a sequence of vector embeddings --- one per amino acid position --- that encode structural information. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global structural similarity between proteins and (ii) pairwise residue contact maps for individual proteins. To enable learning from structural similarity information, we define a novel similarity measure between arbitrary-length sequences of vector embeddings based on a soft symmetric alignment (SSA) between them. Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences. We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal. Finally, we demonstrate that our learned embeddings can be transferred to other protein sequence problems, improving the state-of-the-art in transmembrane domain prediction.