ProbeRating

ProbeRating predicts binding profiles of nucleic acid-binding proteins (NBPs), including RNA-binding proteins (RBPs) and transcription factors (TFs), to infer sequence-binding preferences and inform studies of protein–nucleic acid interactions.

Key Features:

• Deep learning and NLP: Uses deep learning combined with natural language processing techniques to model NBP–nucleic acid interactions.
• Word embeddings (FastText): Adapts FastText word embeddings from Facebook AI Research to extract biological features from sequence information.
• Neural network recommender: Implements a neural network-based recommender system to predict binding profiles for NBPs.
• Homology exploitation: Leverages available data from homologous proteins to infer binding preferences of unexplored or poorly studied NBPs.
• Task evaluation: Has been evaluated on distinct tasks focusing on RBPs and TFs to assess predictive performance.

Scientific Applications:

• Predicting NBP binding profiles: Infers binding profiles for NBPs that lack direct in vivo or in vitro experimental data.
• RBP and TF preference inference: Provides predicted sequence-binding preferences for RNA-binding proteins and transcription factors.
• Characterizing unstudied proteins: Enables study of binding mechanisms and interaction codes for previously uncharacterized NBPs using homologous data.

Methodology:

Applies FastText word embeddings to sequence data and trains a neural network-based recommender using deep learning and NLP approaches, with evaluation performed on separate RBP and TF prediction tasks while exploiting homologous protein data.