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Protein Interaction Mapping

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Growing ecosystem of deep learning methods for modeling protein-protein interactions.

Protein engineering, design & selection : PEDS
Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a...

ISPRED-SEQ: Deep Neural Networks and Embeddings for Predicting Interaction Sites in Protein Sequences.

Journal of molecular biology
The knowledge of protein-protein interaction sites (PPIs) is crucial for protein functional annotation. Here we address the problem focusing on the prediction of putative PPIs considering as input protein sequences. The issue is important given the h...

Spatom: a graph neural network for structure-based protein-protein interaction site prediction.

Briefings in bioinformatics
Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI site prediction. This framework first defines a weighted digraph for a protein structure to precisely c...

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negat...

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted...

DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.

BMC bioinformatics
PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventi...

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions.

Journal of chemical information and modeling
Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts thei...

Revolutionizing protein-protein interaction prediction with deep learning.

Current opinion in structural biology
Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning...

Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology.

The protein journal
Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontolo...

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

IEEE journal of biomedical and health informatics
Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the e...