Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites.
Omeir KhanGeorge JonesMaria LazouDiane Joseph-McCarthyDima KozakovDmitri BeglovSandor VajdaPublished in: Journal of chemical information and modeling (2024)
The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.
Keyphrases
- drug discovery
- molecular docking
- protein protein
- molecular dynamics
- high resolution
- healthcare
- small molecule
- high density
- living cells
- primary care
- randomized controlled trial
- single cell
- binding protein
- transcription factor
- clinical practice
- mass spectrometry
- ionic liquid
- gene expression
- quantum dots
- quality improvement
- risk assessment
- fluorescence imaging
- molecular dynamics simulations
- social media
- photodynamic therapy
- bioinformatics analysis