This review focuses on three closely related tasks of proteome-wide PPI prediction, PPI interface prediction, and PPI co-complex structure prediction, and discusses how emerging concepts and computation approaches have evolved to shape these fields.
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
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
An innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions and gives a better understanding of the structural processes that control PPI.
Oras A. Hussein, E. Al-Shamery· Journal of Intelligent Infor...· 0 citations
DCAPPI (Dual Cross-Attention network for Protein-Protein Interaction prediction), a novel framework leveraging dual cross-attention modules for hierarchical feature fusion at both intra- and inter-protein levels, achieves superior performance over state-of-the-art methods on benchmark datasets.
Shuai Lu, Yuguang Li, Zhen Tian et al.· Computational and Structural...· 0 citations
Accurate prediction of protein-protein interaction sites (PPISs) plays a crucial role in understanding protein function, elucidating disease mechanisms, and facilitating drug target discovery. Although conventional approaches based on sequence or structural features have shown promising results, they still face several challenges. These challenges include oversmoothing in deep graph neural networks (GNNs) and poor generalization to domain-specific data. To address these issues, we propose RGLLA-PPIS, a novel multimodal prediction model that integrates retrieval-augmented learning and residual GNNs for PPIS identification. In RGLLA-PPIS, protein graphs are constructed by combining AlphaFold3 (AF3)-predicted protein structures with multiple sequence-derived features. To effectively capture both local and global spatial dependencies, the model employs equivariant GNN (EGNN) and GCN modules with residual connections, which help alleviate the oversmoothing problem and preserve node-level variability. Moreover, during prediction, we used the retrieval-augmented knowledge provided by the pretrained protein language model (PLM) Evolla and ChatGPT-4o to construct semantic priors to supplement potential functional site information and enhance the generalization capacity of the prediction model. Extensive experiments on benchmark datasets show that RGLLA-PPIS outperforms several state-of-the-art baselines in both accuracy and robustness. Furthermore, comparison with wet-lab results on a domain-specific protein system reveals a strong correspondence between experimental functional sites and the high-probability regions predicted by RGLLA-PPIS. This demonstrates the model's potential to guide real-world protein engineering tasks. The source code can be found at: https://github.com/MiJia-ID/RGLLA-PPIS.
Jia Mi, Ya-Wen Liu, Chong Chu et al.· IEEE Transactions on Neural...· 0 citations
Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.
Enikő Zakar-Polyák, C. Kerepesi· bioRxiv· 0 citations
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