# DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins

arXiv: https://arxiv.org/abs/1611.05777 · HTML: https://mlsanity.com/paper/1611.05777/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2016.

## Built on by (most cited first, 7 of 7)

- [To Transformers and Beyond: Large Language Models for the Genome](https://mlsanity.com/paper/2311.07621/) (2023)
- [Protein-RNA interaction prediction with deep learning: Structure matters](https://mlsanity.com/paper/2107.12243/) (2021)
- [DNA-GCN: Graph convolutional networks for predicting DNA-protein binding](https://mlsanity.com/paper/2106.01836/) (2021)
- [GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning](https://mlsanity.com/paper/2407.16940/) (2024)
- [Predicting Transcription Factor Binding Sites using Transformer based Capsule Network](https://mlsanity.com/paper/2310.15202/) (2023)
- [Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium](https://mlsanity.com/paper/2502.06693/) (2025)
- [ResCap-DBP: A Lightweight Residual-Capsule Network for Accurate DNA-Binding Protein Prediction Using Global ProteinBERT Embeddings](https://mlsanity.com/paper/2507.20426/) (2025)

## Abstract (arXiv)

Transcription factors (TFs) are macromolecules that bind to textitcis-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several textitin vivo and textitin vitro techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.