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Protein Interaction Mapping

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BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction.

Journal of molecular biology
Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating ...

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

Journal of molecular biology
Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, ...

SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.

BMC genomics
BACKGROUND: Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs info...

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Nucleic acids research
Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational appro...

Hierarchical graph learning for protein-protein interaction.

Nature communications
Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and und...

Cracking the code of cellular protein-protein interactions: Alphafold and whole-cell crosslinking to the rescue.

Molecular systems biology
Integration of experimental and computational methods is crucial to better understanding protein-protein interactions (PPIs), ideally in their cellular context. In their recent work, Rappsilber and colleagues (O'Reilly et al, 2023) identified bacteri...

Machine learning on protein-protein interaction prediction: models, challenges and trends.

Briefings in bioinformatics
Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilit...

Recent advances in predicting and modeling protein-protein interactions.

Trends in biochemical sciences
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial...

Graph-BERT and language model-based framework for protein-protein interaction identification.

Scientific reports
Identification of protein-protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniq...

DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions.

IEEE transactions on nanobioscience
Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the in...