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
RGLLA-PPIS, a novel multimodal prediction model that integrates retrieval-augmented learning and residual GNNs for PPIS identification, outperforms several state-of-the-art baselines in both accuracy and robustness and demonstrates its potential to guide real-world protein engineering tasks.
Jia Mi, Ya-Wen Liu, Chong Chu et al.· IEEE Transactions on Neural...· 0 citations
It is 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.
Enikő Zakar-Polyák, C. Kerepesi· bioRxiv· 0 citations
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