# Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing

arXiv: https://arxiv.org/abs/2402.01829 · HTML: https://mlsanity.com/paper/2402.01829/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Deep Learning in Bioinformatics](https://mlsanity.com/paper/1603.06430/) (2016)
- [A Comparison of Word Embeddings for the Biomedical Natural Language Processing](https://mlsanity.com/paper/1802.00400/) (2018)

## Abstract (arXiv)

Predicting ATP-Protein Binding sites in genes is of great significance in the field of Biology and Medicine. The majority of research in this field has been conducted through time- and resource-intensive 'wet experiments' in laboratories. Over the years, researchers have been investigating computational methods computational methods to accomplish the same goals, utilising the strength of advanced Deep Learning and NLP algorithms. In this paper, we propose to develop methods to classify ATP-Protein binding sites. We conducted various experiments mainly using PSSMs and several word embeddings as features. We used 2D CNNs and LightGBM classifiers as our chief Deep Learning Algorithms. The MP3Vec and BERT models have also been subjected to testing in our study. The outcomes of our experiments demonstrated improvement over the state-of-the-art benchmarks.