# Deep Recurrent Neural Network for Protein Function Prediction from Sequence

arXiv: https://arxiv.org/abs/1701.08318 · HTML: https://mlsanity.com/paper/1701.08318/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers](https://mlsanity.com/paper/2307.14367/) (2023)
- [Retrieved Sequence Augmentation for Protein Representation Learning](https://mlsanity.com/paper/2302.12563/) (2023)
- [T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification](https://mlsanity.com/paper/2304.13145/) (2023)
- [Binary classification of proteins by a Machine Learning approach](https://mlsanity.com/paper/2111.01975/) (2021)
- [Using Deep Learning Sequence Models to Identify SARS-CoV-2 Divergence](https://mlsanity.com/paper/2111.06593/) (2021)
- [Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics](https://mlsanity.com/paper/2207.05682/) (2022)
- [Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs](https://mlsanity.com/paper/2406.14142/) (2024)
- [Autoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion](https://mlsanity.com/paper/2408.06391/) (2024)

## Abstract (arXiv)

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.