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.
Abstract
Abstract Protein–nucleic acid interactions (PNIs) are central to fundamental biological processes, and mutations can disrupt these interactions by altering local structural features and binding free energy. Here, we present DeepPNI, 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. The model was developed using a comprehensive dataset of 1754 mutations spanning protein–DNA and protein–RNA complexes, representing one of the largest curated datasets for PNI binding free energy prediction. Structural features were encoded using an edge-aware relational graph convolutional network, while sequence features were represented using the Evolutionary Scale Modeling 2 protein language model. Despite the increased dataset size and heterogeneity, DeepPNI achieved an overall Pearson correlation coefficient of 0.76 in five-fold cross-validation. Consistent performance was observed across protein–DNA and protein–RNA subsets, datasets grouped by experimental temperature, and external blind test datasets, suggesting robustness against dataset heterogeneity. DeepPNI is freely available as a web server at https://research.iitbhilai.ac.in/molinfo/deeppni.
RNA-binding proteins (RBPs) are essential across biology, from viruses to complex multicellular organisms. They regulate gene expression and cellular responses, making RNA recognition central to understanding health and disease. Biochemical, biophysical, and structural studies have defined core principles of RNA binding, but recent RNA interactome surveys have expanded the RBP repertoire and revealed many noncanonical RNA-binding regions. This diversity demands highly scalable predictive methods. Here, we review machine learning predictors built on protein language models and structure-aware representations. 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
A hybrid ensemble framework integrating XGBoost, convolutional neural networks (CNN), and graph neural networks (GNN) trained on a curated SKEMPI v2.0 dataset provides a robust and practical tool for ΔΔG prediction with potential applications in protein engineering and rational mutation design.
Sowmya Hari, R. Babu· Computational biology and ch...· 0 citations
A neural network-based pipeline that integrates amino acid sequences with structural features is developed and provides a modular prototype for follow-up, more extensive protein modeling, including larger proteins and sequence of variable sizes.
Carl David Jasper Causin, M. Fyta· APL Machine Learning· 0 citations
Accurate protein function prediction (PFP) is essential for understanding biological systems. However, structure-based graph neural networks often rely on fixed-distance contact maps, which may inadequately capture continuous, multi-scale spatial topologies, while the long-tail distribution of Gene Ontology (GO) labels may bias prediction toward frequent functions. We propose DHST, a deep hybrid structure–topology framework that integrates sequence semantics from a pretrained protein language model with local structural information learned by a residual graph convolutional network. DHST further introduces site-specific persistent homology to encode multi-scale topological invariants and a topology-guided residue-wise gated fusion module to modulate structure–semantics representations using local topological embeddings. The fused residue features are aggregated through dual-path pooling, and a weighted binary cross-entropy loss is used to mitigate the adverse effects of label imbalance. On the PDB dataset, DHST achieved area under the precision–recall curve (AUPR) scores of 0.779, 0.481, and 0.557 for molecular function (MF), biological process (BP), and cellular component (CC), respectively; on the AF2 dataset, the corresponding scores were 0.729, 0.390, and 0.459. The model also demonstrated robust generalization to low-homology proteins and maintained strong predictive performance across GO terms with different levels of functional specificity. Ablation results supported the contributions of the main components.
Bin Lu, Fujun Xiang, Hai-Long Wang et al.· Applied Sciences· 0 citations
Accurately predicting the effects of mutations on protein-RNA binding is crucial for elucidating disease mechanisms. Yet, exhaustively exploring the space of all possible variants is prohibitively expensive, motivating computational methods that can quantify mutation-induced changes in binding affinity (aka ΔΔG) accurately and efficiently. We present iSCALE, an interpretable and generalizable deep learning method that adopts an implicit Spatial Coupling-Aware Ligand Encoding strategy to predict mutation-induced binding affinity changes. By injecting this implicit multiscale encoding scheme into a bidirectional state space modeling architecture, iSCALE learns a generalizable multiscale coupling pattern that achieves superior performances on not only the protein-RNA binding ΔΔG, but also the protein stability ΔΔG and protein-protein binding ΔΔG predictions. Detailed analyses demonstrate that the model attention scores align well with structural characteristics. In addition, iSCALE shows good discriminative ability when predicting close samples such as complexes of same mutation but with different ligands or the same complex but with different mutation sites. In summary, 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.
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.