AIMC Topic: Protein Interaction Mapping

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Multiplex mapping of protein-protein interaction interfaces.

Proceedings of the National Academy of Sciences of the United States of America
We describe peptide mapping through Split Antibiotic Resistance Complementation (SpARC-map), a method to identify the probable interface between two interacting proteins. Our method is based on in vivo affinity selection inside a bacterial host and u...

MPIDNN-GPPI: multi-protein language model with an improved deep neural network for generalized protein‒protein interaction prediction.

BMC genomics
Predicting protein‒protein interactions (PPIs) plays a crucial role in understanding biological processes. Although biological experimental methods can identify PPIs, they are costly, time-consuming, labor-intensive, and often lack stability. In cont...

Predicting protein-protein interactions in the human proteome.

Science (New York, N.Y.)
Protein-protein interactions (PPIs) are essential for biological function. Coevolutionary analysis and deep-learning (DL)-based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches hav...

DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion.

BMC bioinformatics
BACKGROUND: The identification of protein-protein interaction (PPI) plays a crucial role in understanding the mechanisms of complex biological processes. Current research in predicting PPI has shown remarkable progress by integrating protein informat...

Mapping Context-Aware Phosphosite Regulation of Protein-Protein Interactions Using Deep Learning and Pan-Cancer Proteomics.

Journal of chemical information and modeling
Phosphorylation dynamically orchestrates the protein-protein interaction (PPI) network that governs cellular signaling, and its dysregulation frequently drives malignant transformation and neurodegeneration. We present PhosPPI-SEQ, an interpretable d...

SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions.

Genome biology
Intrinsically disordered proteins and regions (IDRs) lack stable 3D structures, posing challenges for interaction prediction. We present SpatPPI, a geometric deep learning model tailored for IDPPI prediction. SpatPPI leverages structural cues from fo...

PPAP: A Protein-protein Affinity Predictor Incorporating Interfacial Contact-Aware Attention.

Journal of chemical information and modeling
Protein-protein interactions (PPIs) play fundamental roles in biological processes and therapeutic development. Accurately predicting PPI binding affinity is critical for understanding interaction mechanisms and guiding protein engineering. Recent ad...

ProteinWeaver: A webtool to visualize ontology-annotated protein networks.

PloS one
Molecular interaction networks are a vital tool for studying biological systems. While many tools exist that visualize a protein or a pathway within a network, no tool provides the ability for a researcher to consider a protein's position in a networ...

Mapping a interactome by crosslinking mass spectrometry and machine learning.

mBio
, a widespread human parasite, persists in hosts through complex molecular interactions. Protein-protein interactions (PPIs) underpin essential biological processes, including parasite-host interactions and cellular invasion. Herein, we utilized adva...

MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.

BMC biology
BACKGROUND: Protein-protein interactions (PPIs) play a critical role in essential biological processes such as signal transduction, enzyme activity regulation, cytoskeletal structure, immune responses, and gene regulation. However, current methods ma...