UDSMProt: universal deep sequence models for protein classification.

ABSTRACT:

Motivation

Inferring the properties of a protein from its amino acid sequence is one of the key problems in bioinformatics. Most state-of-the-art approaches for protein classification are tailored to single classification tasks and rely on handcrafted features, such as position-specific-scoring matrices from expensive database searches. We argue that this level of performance can be reached or even be surpassed by learning a task-agnostic representation once, using self-supervised language modeling, and transferring it to specific tasks by a simple fine-tuning step.

Results

We put forward a universal deep sequence model that is pre-trained on unlabeled protein sequences from Swiss-Prot and fine-tuned on protein classification tasks. We apply it to three prototypical tasks, n

SUBMITTER: Strodthoff N 

PROVIDER: S-EPMC7178389 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature