This study reviews the methodological evolution from conventional machine learning to deep learning, graph neural networks and large-scale pre-trained language models, and compares their differences in data preparation, evaluation protocols and generalization behavior, and places particular emphasis on recent advances in structure-aware and condition-aware models.
Abstract
Protein-RNA interactions (RPIs) stand for the central process in post-transcriptional regulation and have catalyzed a fast proliferation of computational approaches in recent years. Adopting a task-oriented classification method, RPIs calculation prediction schemes proposed over the period 2010-2025 fall into five primary categories: RNA-binding protein (RBP) classification, RPIs prediction, binding site and binding profile modeling on RNA, residue-level RNA-binding interface prediction on proteins, and quantitative estimation of binding affinity and mutation effects. This study reviews the methodological evolution from conventional machine learning to deep learning, graph neural networks and large-scale pre-trained language models, and compares their differences in data preparation, evaluation protocols and generalization behavior. Particular emphasis is placed on recent advances in structure-aware and condition-aware models, as well as learning in low-data regimes. Finally, the study outlines practical recommendations for field-wide benchmarking and looks ahead to the integration with spatial omics and the development of dynamic, generative landscapes of RPIs to better empower biomedical research.
These approaches improve generalisability, reduce reliance on deep evolutionary information, and enable proteome-scale prediction of RNA-binding residues, providing a route to map and interpret the molecular logic of protein-RNA interactions.
Rozeena Arif, Alfredo Castello· Current Opinion in Structura...· 0 citations
Transcription Factor Binding Site (TFBS) prediction is central to understanding gene regulation and various biological processes. This study introduces HetMoE, a heterogeneous, embedding-gated Mixture-of-Experts for TFBS prediction. A gating network operates on the embeddings produced by a pool of complementary expert backbones (a modified-DeepBIND convolutional network, DeepSEA, and DanQ, with a fine-tuned DNABERT-6 genomic language model as an optional expert), so that models of different architectures are combined and weighted on a per-input basis. Models are trained against GC- and repeat-matched real genomic negatives, a fair protocol that avoids the dinucleotide-shuffle artifact, and evaluated with a balanced-test-set protocol (deterministic inference, B=1000 paired bootstrap and Analysis of Variance (ANOVA)) on in-distribution and out-of-distribution (OOD) factors. HetMoE attains the best in-distribution performance (mean Area Under the Curve (AUC) 0.881) and, on a held-out set stratified by DNA-binding-domain family, surpasses fine-tuned DNABERT-6 on the motif-bearing OOD mean across three random seeds (0.821±0.005 vs. 0.799±0.008, a gain present in every seed), most strongly on the sequence-specific and within-family factors. The advantage comes from the gating mechanism rather than from ensembling: input-dependent gating exceeds a static average of the same experts by 0.073 AUC and the best single expert by 0.088, and the configuration selected on in-distribution data is a pretraining-free pool of convolutional experts. We further show that the common dinucleotide-shuffle negative protocol inflates the apparent margin (to a mean of 0.864), which shows the importance of fair, genomically matched negatives. We also introduce an attribution method (ShiftSmooth) that improves interpretability by averaging the gradient over small shifts of the input sequence, giving more reliable attribution for motif discovery and localization than the Vanilla Gradient. Together these provide an efficient and interpretable approach to TFBS prediction that can support further study of genome regulation.
A. Tripathi, Ian E. Nielsen, Muhammad Umer et al.· Mathematics· 0 citations
The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible complex structures are available.
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
iSCALE serves as an effective in silico tool for large-scale protein-RNA binding ΔΔG prediction, which pushes the border of understanding in mutation-induced pathological outcomes.
Edge Generation-guided Relation-aware Learning (EGRL) is proposed, a novel framework with several key components: implicit meta-path learning to capture relational semantics without handcrafted paths; a multi-relation-aware attention mechanism for adaptive fusion of interaction patterns; a graph generator that predicts potential ("soft") edges to support cold-start nodes; and a multi-feature fusion predictor for final interaction scoring.
Danyu Li, Ling Zhou, Rubing Huang et al.· 0 citations
DeepPNI is a deep learning regression model that integrates sequence- and structure-based features to estimate mutation-induced changes in binding free energy in protein–nucleic acid complexes, developed using a comprehensive dataset of 1754 mutations spanning protein–DNA and protein–RNA complexes.
