ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins

Abstract

Motivation The interaction between proteins and nucleic acids plays a crucial role in gene regulation and cell function. Determining the binding preferences of nucleic acid-binding proteins (NBPs), namely RNA-binding proteins (RBPs) and transcription factors (TFs), is the key to decipher the protein-nucleic acids interaction code. Today, available NBP binding data from in vivo or in vitro experiments are still limited, which leaves a large portion of NBPs uncovered. Unfortunately, existing computational methods that model the NBP binding preferences are mostly protein-specific: they need the experimental data for a specific protein in interest, and thus only focus on experimentally characterized NBPs. The binding preferences of experimentally unexplored NBPs remain largely unknown. Results Here, we introduce ProbeRating, a nucleic acid recommender system that utilizes techniques from deep learning and word embeddings of natural language processing. ProbeRating is developed to predict binding profiles for unexplored or poorly studied NBPs by exploiting their homologs NBPs which currently have available binding data. Requiring only sequence information as input, ProbeRating adapts FastText from Facebook AI Research to extract biological features. It then builds a neural network based recommender system. We evaluate the performance of ProbeRating on two different tasks: one for RBP and one for TF. As a result, ProbeRating outperforms previous methods on both tasks. The results show that ProbeRating can be a useful tool to study the binding mechanism for the many NBPs that lack direct experimental evidence. Availability The source code is freely available at < https://github.com/syang11/ProbeRating >. Supplementary information Supplementary data are available at Bioinformatics online.

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

“…Language retrieval systems typically compare input vectors with a database of key vectors, each derived from a neural network, using approximate nearest neighbor search techniques (Karpukhin et al, 2020). In biology, fields of research employing dualbranch neural networks include (1) drug-target interaction (Lee et al, 2019), (2) single-cell multi-omics analysis (Lance et al, 2022), and (3) transcription factor binding prediction (Yang et al, 2020), among countless others.…”

Representation learning applications in biological sequence analysis

“…ProbeRating is a neural network-based recommender system utilizing word embeddings in NLP to infer binding profiles for unexplored nucleic acid-binding proteins (NBPs) [61] . ProbeRating achieves this goal using a two-stage framework.…”

Representation learning applications in biological sequence analysis

“…ProbeRating is a neural network-based recommender system utilizing word embeddings in NLP to infer binding profiles for unexplored nucleic acid-binding proteins (NBPs) [55]. ProbeRating achieves this goal using a two-stage framework.…”