Colabind: A Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes.

Binding site prediction is a crucial step in understanding protein-ligand and protein-protein interactions (PPIs) with broad implications in drug discovery and bioinformatics. This study introduces Colabind, a robust, versatile, and user-friendly cloud-based approach that employs coarse-grained molecular dynamics simulations in the presence of molecular probes, mimicking fragments of drug-like compounds. Our method has demonstrated high effectiveness when validated across a diverse range of biological targets spanning various protein classes, successfully identifying orthosteric binding sites, as well as known druggable allosteric or PPI sites, in both experimentally determined and AI-predicted protein structures, consistently placing them among the top-ranked sites.

Nearly complete structure of bacteriophage DT57C reveals architecture of head-to-tail interface and lateral tail fibers.

The T5 family of viruses are tailed bacteriophages characterized by a long non-contractile tail. The bacteriophage DT57C is closely related to the paradigmal T5 phage, though it recognizes a different receptor (BtuB) and features highly divergent lateral tail fibers (LTF). Considerable portions of T5-like phages remain structurally uncharacterized.

Structural insights into the effects of glycerol on ligand binding to cytochrome P450.

New antitubercular drugs are vital due to the spread of resistant strains. Carbethoxyhexyl imidazole (CHImi) inhibits cytochrome P450 CYP124, which is a steroid-metabolizing enzyme that is important for the survival of Mycobacterium tuberculosis in macrophages. The available crystal structure of the CYP124-CHImi complex reveals two glycerol molecules in the active site.