UDSMProt: universal deep sequence models for protein classification

By Nils Strodthoff, Patrick Wagner, Markus A. Wenzel, Wojciech Samek. 2019

194 citations recorded by the source corpus.

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@hhi.fraunhofer.de

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021, methodology): “…data as input, and apply learning algorithms from the ﬁeld of natural language processing (Rao et al., 2019; Alley et al., 2019; Min et al., 2020; Strodthoff et al., 2020), 1D convolutional neural networks (Kulmanov et al., 2017; Kulmanov & Hoehndorf, 2019), or use structural information during…”
ProteinBERT: a universal deep-learning model of protein sequence and function (2021, background): “A few recent studies have pretrained deep neural language models on protein sequences (e.g. ESM [20], TAPE-Transformer [21], UDSMProt [22], UniRep [23]) [24–26].”