Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach.

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TitleInclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach.
Publication TypeJournal Article
Year of Publication2013
AuthorsE Raman, P, Yu, W, Lakkaraju, SK, Mackerell, AD
JournalJ Chem Inf Model
Date Published2013 Dec 23
KeywordsBinding, Competitive, Catalytic Domain, Factor Xa, HIV Protease, Humans, Hydrogen Bonding, Ligands, Molecular Docking Simulation, Molecular Dynamics Simulation, Monte Carlo Method, p38 Mitogen-Activated Protein Kinases, Probability, Protein Binding, Ribonuclease, Pancreatic, Small Molecule Libraries, Static Electricity, Structure-Activity Relationship, Thermodynamics

The site identification by ligand competitive saturation (SILCS) method identifies the location and approximate affinities of small molecular fragments on a target macromolecular surface by performing molecular dynamics (MD) simulations of the target in an aqueous solution of small molecules representative of different chemical functional groups. In this study, we introduce a set of small molecules to map potential interactions made by neutral hydrogen bond donors and acceptors and charged donor and acceptor fragments in addition to nonpolar fragments. The affinity pattern is obtained in the form of discretized probability or, equivalently, free energy maps, called FragMaps, which can be visualized with the target surface. We performed SILCS simulations for four proteins for which structural and thermodynamic data is available for multiple diverse ligands. Good overlap is shown between high affinity regions identified by the FragMaps and the crystallographic positions of ligand functional groups with similar chemical functionality, thus demonstrating the validity of the qualitative information obtained from the simulations. To test the ability of FragMaps in providing quantitative predictions, we calculate the previously introduced ligand grid free energy (LGFE) metric and observe its correspondence with experimentally measured binding affinity. LGFE is computed for different conformational ensembles and improvement in prediction is shown with increasing ligand conformational sampling. Ensemble generation includes a Monte Carlo sampling approach that uses the GFE FragMaps directly as the energy function. The results show that some but not all experimental trends are predicted and warrant improvements in the scoring methodology. In addition, the potential utility of atom-based free energy contributions to the LGFE scores and the use of multiple ligands in SILCS to identify displaceable water molecules during ligand design are discussed.

Alternate JournalJ Chem Inf Model
PubMed ID24245913
PubMed Central IDPMC3947602
Grant ListR01 CA107331 / CA / NCI NIH HHS / United States
CA107331 / CA / NCI NIH HHS / United States