Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

Abstract

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 A for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.

Long-range Electrostatic Complementarity Governs Substrate Recognition by Human Chymotrypsin C, a Key Regulator of Digestive Enzyme Activation

“…Substrates were docked into the binding site of CTRC using Glide extra precision (XP) (Glide, version 5.6, Schrödinger, LLC); molecular conformations were sampled using methods described previously (32). A structure-based pharmacophore score was generated from the optimized, best scoring pose for each substrate ligand based on the descriptors from the Glide XP score using an established approach (31,33,34). The energetic value assigned to each pharmacophore feature was calculated using Phase (Phase, version 3.2, Schrödinger, LLC) as the sum of the Glide XP contributions of the atoms comprising the site.…”

“…The energetic value assigned to each pharmacophore feature was calculated using Phase (Phase, version 3.2, Schrödinger, LLC) as the sum of the Glide XP contributions of the atoms comprising the site. Overall dockings at the active site were quantified and ranked on the basis of these energetic terms (33,34). To account for protein flexibility and lessen the effects of minor steric clashes, excluded volumes spheres corresponding to 80% of the van der Waals atomic radii were created for all CTRC atoms within 6 Å of each substrate or mutagenized residue modeled.…”

“…To account for protein flexibility and lessen the effects of minor steric clashes, excluded volumes spheres corresponding to 80% of the van der Waals atomic radii were created for all CTRC atoms within 6 Å of each substrate or mutagenized residue modeled. A minimum of two poses per substrate, chosen for a combination of best-scoring features, was selected for visual and energetic comparison (31,33,34).…”

Energy Based Pharmacophore Modelling and Docking Studies for Determining Potent Inhibitor Against M2 Proton Channel of Influenza Virus

“…This scoring function aids to accurately characterize protein-ligand interactions, resulting in improved database screening enrichments. 24 Subsequently, a virtual screening process is used in accordance with the molecular docking which has acquired the position of a dynamic and cost-effective technology in finding out novel drug like compounds or so called "hits' in the pharmaceutical industry. 25 Moreover, the technological advances in this screening process allows chemists to promptly screen large databases for effective therapeutics.…”

Molecular dynamic simulations and structure-based pharmacophore development for farnesyltransferase inhibitors discovery

“…The ligand-based drug design approaches are used when 3D structure of the protein is not available; however, it considers compounds (especially active compounds) those have activity on the particular target. The QSAR, pharmacophore analyses are best-suited techniques applied for ligand-based analysis [19][20][21][22][23][24][25][26] . In the present investigation, we have applied both structure-and ligand-based drug design approaches to generate structure-based pharmacophore models to identify the FTase inhibitors from data set of natural compounds.…”