RT Journal Article
SR Electronic
T1 UDSMProt: Universal Deep Sequence Models for Protein Classification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 704874
DO 10.1101/704874
A1 Nils Strodthoff
A1 Patrick Wagner
A1 Markus Wenzel
A1 Wojciech Samek
YR 2019
UL http://biorxiv.org/content/early/2019/09/04/704874.abstract
AB 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 tasks 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 finetuning step.Results We put forward a universal deep sequence model that is pretrained on unlabeled protein sequences from Swiss-Prot and finetuned on protein classification tasks. We apply it to three prototypical tasks, namely enzyme class prediction, gene ontology prediction and remote homology and fold detection. The proposed method performs on par with state-of-the-art algorithms that were tailored to these specific tasks or, for two out of three tasks, even outperforms them. These results stress the possibility of inferring protein properties from the sequence alone and, on more general grounds, the prospects of modern natural language processing methods in omics.Availability Source code is available under https://github.com/nstrodt/UDSMProt.Contact firstname.lastname{at}hhi.fraunhofer.de