Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models

• URL: http://arxiv.org/abs/2012.00195v1
• Date: Tue, 1 Dec 2020 01:01:34 GMT
• Title: Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
• Authors: Pascal Sturmfels, Jesse Vig, Ali Madani, Nazneen Fatema Rajani
• Abstract summary: Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks. We introduce a new pre-training task: directly predicting protein profiles derived from multiple sequence alignments. Our results suggest that protein sequence models may benefit from leveraging biologically-inspired inductive biases.
• Score: 11.483725773928382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization. Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks. However, the optimal pre-training strategy remains an open question. Instead of strictly borrowing from natural language processing (NLP) in the form of masked or autoregressive language modeling, we introduce a new pre-training task: directly predicting protein profiles derived from multiple sequence alignments. Using a set of five, standardized downstream tasks for protein models, we demonstrate that our pre-training task along with a multi-task objective outperforms masked language modeling alone on all five tasks. Our results suggest that protein sequence models may benefit from leveraging biologically-inspired inductive biases that go beyond existing language modeling techniques in NLP.

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