Deep Recurrent Neural Network for Protein Function Prediction from Sequence

By X. Liu. 2017

98 citations recorded by the source corpus.

As high-throughput biological sequencing becomes faster and cheaper, the need to extract useful information from sequencing becomes ever more paramount, often limited by low-throughput experimental characterizations. For proteins, accurate prediction of their functions directly from their primary amino-acid sequences has been a long standing challenge. Here, machine learning using artificial recurrent neural networks (RNN) was applied towards classification of protein function directly from primary sequence without sequence alignment, heuristic scoring or feature engineering. The RNN models containing long-short-term-memory (LSTM) units trained on public, annotated datasets from UniProt achieved high performance for in-class prediction of four important protein functions tested, particularly compared to other machine learning algorithms using sequence-derived protein features. RNN models were used also for out-of-class predictions of phylogenetically distinct protein families with similar functions, including proteins of the CRISPR-associated nuclease, ferritin-like iron storage and cytochrome P450 families. Applying the trained RNN models on the partially unannotated UniRef100 database predicted not only candidates validated by existing annotations but also currently unannotated sequences. Some RNN predictions for the ferritin-like iron sequestering function were experimentally validated, even though their sequences differ significantly from known, characterized proteins and from each other and cannot be easily predicted using popular bioinformatics methods. As sequencing and experimental characterization data increases rapidly, the machine-learning approach based on RNN could be useful for discovery and prediction of homologues for a wide range of protein functions.

Subfields: Molecular Biology

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Citations in the corpus

Deep Semantic Protein Representation for Annotation, Discovery, and Engineering (2018, background): “Several groups have deployed machine learning methods to identify specific functions of proteins 15–19 , however there have been only limited attempts to build a single classifier that assigns multiple features to a given peptide sequence 20–23 .”
Generative Recurrent Networks for De Novo Drug Design (2017, methodology): “[6] In the field of molecular informatics, RNNs based on LSTMs have been used to predict protein function from sequence [7] and successfully predict aqueous solubility of drug-like compounds.”
DeepDTA: deep drug–target binding affinity prediction (2018, background): “structive peptide design [41] and function prediction [39] that utilize amino-acid sequences.”
A Survey of Deep Learning for Scientific Discovery (2020, background): “Scientiﬁc Examples: RNNs have found several scientiﬁc applications for data with sequential structure, such as in genomics and proteomics [175, 132, 111].”