Protein-RNA interaction prediction method based on multi-modal deep learning

By integrating RNA sequences and tertiary structures through the multimodal deep learning method NeuroPRIS, the problems of data heterogeneity and dynamic conformational changes in protein-RNA interaction prediction are solved, high-precision binding site prediction and cell line-specific analysis are achieved, and target identification in cancer research is promoted.

CN120766764APending Publication Date: 2025-10-10SHANDONG FIRST MEDICAL UNIV & SHANDONG ACADEMY OF MEDICAL SCI
0 Cites 0 Cited by

Patent Information

Application Number
CN202510876080.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-27
Publication Date
2025-10-10

AI Technical Summary

Technical Problem

Existing technologies for predicting protein-RNA interactions suffer from the problems of one-sided molecular modeling that ignores interaction cooperativity, data heterogeneity, and computational complexity, and traditional methods cannot effectively handle long-range dependencies and dynamic conformational changes.

Method used

The multimodal deep learning method NeuroPRIS is used to integrate RNA sequence data and tertiary structure information. Features are extracted through the T-net architecture, combined with BiLSTM to capture time dependencies, construct a cell line-specific RNA structure similarity matrix, reveal the RNA-RBP interaction rules, and predict binding sites.

Benefits of technology

It improves the accuracy of protein-RNA interaction predictions, reveals unique binding patterns in different cell lines, discovers the core structure and translation relationship of RNA-RBP interactions, provides interpretable binding spectra, and identifies potential targets for cancer research.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of multi-modal RNA feature fusion and high-confidence data set construction, and particularly discloses a protein-RNA interaction prediction method based on multi-modal deep learning, which models and predicts an RBP binding mode, classifies cell line specific RBP-RNA interaction by applying a computing technology, and provides a method for predicting protein-RNA interaction based on multi-modal deep learning. An interpretable group of binding specificity between different cell lines is determined; establishing a cell line specific RNA structure similarity matrix by adopting a structure comparison and clustering technology; predicting the exact nucleotide binding positions of these sites as markers RBP; mRNA (messenger ribonucleic acid) and lincRNA are used as controls; the positive correlation between the RBP-RNA interaction and RNA translation, especially the positive correlation between the RBP-RNA interaction and the RNA translation, is established, a theory and a strategy are provided for cancer targeted therapy, the solution of data heterogeneity and the calculation complexity are reduced, and the prediction efficiency of the protein-RNA interaction is improved.
Need to check novelty before this filing date? Find Prior Art