AIMC Topic: Protein Binding

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HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders.

Methods (San Diego, Calif.)
HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limite...

Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding...

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

PloS one
The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agoni...

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Scientific reports
Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based a...

Exploring the potential of structure-based deep learning approaches for T cell receptor design.

PLoS computational biology
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optim...

Capture of RNA-binding proteins across mouse tissues using HARD-AP.

Nature communications
RNA-binding proteins (RBPs) modulate all aspects of RNA metabolism, but a comprehensive picture of RBP expression across tissues is lacking. Here, we describe our development of the method we call HARD-AP that robustly retrieves RBPs and tightly asso...

Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Journal of computational chemistry
Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking...

GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins.

Advanced materials (Deerfield Beach, Fla.)
Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies criti...

Protein interactions in human pathogens revealed through deep learning.

Nature microbiology
Identification of bacterial protein-protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lit...

SSR-DTA: Substructure-aware multi-layer graph neural networks for drug-target binding affinity prediction.

Artificial intelligence in medicine
Accurate prediction of drug-target binding affinity (DTA) is essential in the field of drug discovery. Recently, scientists have been attempting to utilize artificial intelligence prediction to screen out a significant number of ineffective compounds...