PIWI-interacting RNAs (piRNAs) are key regulators in multiple disease processes, yet predicting their associations with diseases remains difficult due to data sparsity and feature complexity. To address this challenge, a multimodal deep learning framework, PDFBG, was proposed to formulate piRNA-disease association prediction as a three-class classification problem (positive, negative, and unrelated). PDFBG constructed a network using the secondary structure of piRNA and employed the BERT language model to generate feature embeddings for each nucleotide. These embed dings are then fused with topologi-cal structure information via a Graph Convolutional Network (GCN). In terms of diseases, this study constructed disease fea-ture embeddings based on MeSH semantic similarity, Gaussian interaction profile (GIP) kernel and disease-gene associations. Finally, an MLP classifier was used for end - to-end prediction. Experiments on benchmark datasets shown that PDFBG outper-forms state-of-the-art methods, achieving an accuracy of 0.9649 and AUC of 0.9761. Ablation studies confirmed the critical role of RNA-BERT and structural fusion. This work provided a robust and interpretable tool for exploring complex piRNA- disease relationships, while establishing a new paradigm for piRNA research. The code and dataset can be obtained from https://github.com/He-EugeneIPDFBG.

Accurately predicting protein complexes from protein interaction networks is a significant area of study in systems biology, as they are involved in many essential cellular processes. Gaining a more complete understanding of cellular functions will require a comprehensive map of human protein complexes. Unfortunately, we still lack a comprehensive map of experimentally identified protein complexes, thereby only a partial understanding of the composition and function of human protein complexes. To close this gap, we design a computational framework to accurate predict human protein complexes by integrating different protein interaction networks. To predict protein complexes, we design a complex score to control the generation of human complexes by using the seed diffusion strategy on a weighted protein interaction network. Compared with the existing methods, our method achieves promising performance in the human protein interaction network. Further, we also predict 303 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affected human protein complexes. These results suggest that our method is not only a computing framework on which to integrate additional new data sources, but also a valuable resource for exploring biologically meaningful complexes and helping new biological findings.

MOTIVATION: Protein complexes are groups of polypeptide chains linked by non-covalent protein-protein interactions, which play important roles in biological systems and perform numerous functions, including DNA transcription, mRNA translation, and signal transduction. In the past decade, a number of computational methods have been developed to identify protein complexes from protein interaction networks by mining dense subnetworks or subgraphs. RESULTS: In this article, different from the existing works, we propose a novel approach for this task based on generative adversarial networks, which is called PCGAN, meaning identifying Protein Complexes by GAN. With the help of some real complexes as training samples, our method can learn a model to generate new complexes from a protein interaction network. To effectively support model training and testing, we construct two more comprehensive and reliable protein interaction networks and a larger gold standard complex set by merging existing ones of the same organism (including human and yeast). Extensive comparison studies indicate that our method is superior to existing protein complex identification methods in terms of various performance metrics. Furthermore, functional enrichment analysis shows that the identified complexes are of high biological significance, which indicates that these generated protein complexes are very possibly real complexes. AVAILABILITY AND IMPLEMENTATION: https://github.com/yul-pan/PCGAN.

BACKGROUND: Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods. RESULTS: Here, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones. CONCLUSIONS: PreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .

Motivation: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Results: Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. Availability and implementation: The PrabHot webserver is freely available at http://denglab.org/PrabHot/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.