Article ; Online: HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence
Molecular Cell. 20232023 July 07, July 07, v. 83, no. 14 p.2595-2611.e11
2023
Abstract: RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains ( ... ...
| Abstract | RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier (HydRA), which leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNA-binding capacity with unparalleled specificity and sensitivity using support vector machines (SVMs), convolutional neural networks (CNNs), and Transformer-based protein language models. Occlusion mapping by HydRA robustly detects known RBDs and predicts hundreds of uncharacterized RNA-binding associated domains. Enhanced CLIP (eCLIP) for HydRA-predicted RBP candidates reveals transcriptome-wide RNA targets and confirms RNA-binding activity for HydRA-predicted RNA-binding associated domains. HydRA accelerates construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains. |
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| Keywords | Hydra ; RNA ; amino acid sequences ; gene expression ; humans ; metabolism ; support vector machines ; RNA-binding proteins ; machine learning ; RNA-binding domains ; protein-protein interaction network |
| Language | English |
| Dates of publication | 2023-0707 |
| Size | p. 2595-2611.e11. |
| Publishing place | Elsevier Inc. |
| Document type | Article ; Online |
| ZDB-ID | 1415236-8 |
| ISSN | 1097-4164 ; 1097-2765 |
| ISSN (online) | 1097-4164 |
| ISSN | 1097-2765 |
| DOI | 10.1016/j.molcel.2023.06.019 |
| Database | NAL-Catalogue (AGRICOLA) |
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