AIMC Topic: Protein Binding

Clear Filters Showing 61 to 70 of 954 articles

An iterative strategy to design 4-1BB agonist nanobodies de novo with generative AI models.

Scientific reports
The 4-1BB receptor, a key member of the tumor necrosis factor receptor (TNFR) family, represents a highly promising target for cancer immunotherapy. In this study, we developed a novel in silico pipeline to design VHH domain antibodies targeting 4-1B...

CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches.

Journal of computer-aided molecular design
Identifying orthosteric binding sites and predicting small molecule affinities remains a key challenge in virtual screening. While blind docking explores the entire protein surface, its precision is hindered by the vast search space. Cavity detection...

Inhibiting heme piracy by pathogenic Escherichia coli using de novo-designed proteins.

Nature communications
Iron is an essential nutrient for most bacteria and is often growth-limiting during infection, due to the host sequestering free iron as part of the innate immune response. To obtain the iron required for growth, many bacterial pathogens encode trans...

Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.

Journal of computer-aided molecular design
Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors tha...

Combining the NanaPPI Toolbox and AI-Driven Virtual Inhibitor Screening for the p53-MDM2 Interaction.

Analytical chemistry
High-throughput screening for inhibitors of protein-protein interactions (PPIs) provides vital information for therapeutic intervention in diseases driven by aberrant PPIs. Traditionally, the discovery of PPI inhibitors involves sequential steps: in ...

Evaluation of Small-Molecule Binding Site Prediction Methods on Membrane-Embedded Protein Interfaces.

Journal of chemical information and modeling
Increasing structural and biophysical evidence suggests that many drug molecules bind to the protein-membrane interface region in membrane protein structures. An important starting point for drug discovery is the determination of a ligand's binding s...

Exploring Protein-Protein Docking Tools: Comprehensive Insights into Traditional and Deep-Learning Approaches.

Journal of chemical information and modeling
Protein-protein interactions are crucial for numerous biological activities such as signaling, enzyme catalysis, gene expression regulation, cell adhesion, immune response, and drug action. Structural characterization of these interactions can elucid...

EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions.

Journal of chemical information and modeling
Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representations, they critically overlook th...

Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids.

Nature communications
Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infection is essential for developing effective COVID-19 antivirals. However, how the ancestral virus and its variants remodel virus-host protein assemblies i...

Charting γ-secretase substrates by explainable AI.

Nature communications
Proteases recognize substrates by decoding sequence information-an essential cellular process elusive when recognition motifs are absent. Here, we unravel this problem for γ-secretase, an intramembrane-cleaving protease associated with Alzheimer's di...