Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function.

ABSTRACT:

Motivation

Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available.

Results

We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly ne

SUBMITTER: Villegas-Morcillo A 

PROVIDER: S-EPMC8055213 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature