AIMC Topic: Protein Binding

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Efficient Design of Affilin Protein Binders for HER3.

International journal of molecular sciences
Engineered scaffold-based proteins that bind to concrete targets with high affinity offer significant advantages over traditional antibodies in theranostic applications. Their development often relies on display methods, where large libraries of vari...

SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.

BMC biology
BACKGROUND: Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to ...

Discovery, Biological Evaluation and Binding Mode Investigation of Novel Butyrylcholinesterase Inhibitors Through Hybrid Virtual Screening.

Molecules (Basel, Switzerland)
Butyrylcholinesterase (BChE), plays a critical role in alleviating the symptoms of Alzheimer's disease (AD) by regulating acetylcholine levels, emerging as an attractive target for AD treatment. This study employed a quantitative structure-activity r...

Molecular Association Assay Systems for Imaging Protein-Protein Interactions in Mammalian Cells.

Biosensors
Molecular imaging probes play a pivotal role in assaying molecular events in various physiological systems. In this study, we demonstrate a new genre of bioluminescent probes for imaging protein-protein interactions (PPIs) in mammalian cells, named t...

Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcit...

MVSF-AB: accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been w...

Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.

Briefings in bioinformatics
In protein-protein interaction site (PPIS) prediction, existing machine learning models struggle with small datasets, limiting their predictive accuracy for unseen proteins. Additionally, class imbalance in protein complexes, where binding residues c...

LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt.

Briefings in bioinformatics
Identification of T cell receptor (TCR) specificities for antigens from large-scale single-cell or bulk TCR repertoire data plays a vital role in disease diagnosis and immunotherapy. In silico prediction models have emerged in recent years. However, ...

ESMpHLA: Evolutionary Scale Model-Based Deep Learning Prediction of HLA Class I Binding Peptides.

HLA
The recognition of endogenous peptides by HLA class I plays a crucial role in CD8+ T cell immune responses and human adaptive cell immune. Thus, the prediction of HLA class I-peptide binding affinities is always the core issue for the research of imm...

Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.

PLoS computational biology
With a burgeoning number of artificial intelligence (AI) applications in various fields, biomolecular science has also given a big welcome to advanced AI techniques in recent years. In this broad field, scoring a protein-ligand binding structure to o...