Evaluating Protein Transfer Learning with TAPE

TL;DR

This paper introduces TAPE, a benchmark suite of five biologically relevant semi-supervised tasks for protein modeling, demonstrating that self-supervised pretraining improves performance but still lags behind traditional methods, highlighting opportunities for better architectures.

Contribution

The paper presents TAPE, a standardized benchmark for evaluating protein embeddings across diverse tasks, and provides a comprehensive analysis of semi-supervised learning approaches in protein modeling.

Findings

Self-supervised pretraining improves performance on all tasks.

Pretraining can more than double model performance.

Current learned features often underperform compared to traditional methods.

Abstract

Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques. To facilitate progress in this field, we introduce the Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. We curate tasks into specific training, validation, and test splits to ensure that each task tests biologically relevant generalization that transfers to real-life scenarios. We benchmark a range of approaches to semi-supervised protein representation learning, which span recent work as well as canonical sequence learning techniques. We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases. Despite this increase, in several cases features learned by self-supervised pretraining still lag behind features extracted by state-of-the-art non-neural techniques. This gap in performance suggests a huge opportunity for innovative architecture design and improved modeling paradigms that better capture the signal in biological sequences. TAPE will help the machine learning community focus effort on scientifically relevant problems. Toward this end, all data and code used to run these experiments are available at https://github.com/songlab-cal/tape.