Title: Deep Recurrent Neural Network for Protein Function Prediction from Sequence,
Abstract: 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.