A simple formula for dielectric polarisation energies: The Sheffield Solvation Model

Grant, Jennifer; Pickup, Barry T.; Sykes, Matthew J.; Kitchen, C. A.; Nicholls, Anthony

doi:10.1016/j.cplett.2007.05.008

Cited by 31 publications

(26 citation statements)

References 11 publications

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“…AFITT uses an 'adiabatic' method to find the best relative weight between these two components. It can be run without a solvent model or using either the Sheffield (Grant et al, 2007) or the Poisson-Boltzmann scheme (Grant et al, 2001) to model solvation effects. AFITT uses the Merck Molecular Mechanics Force Field (MMFF94; Halgren, 1996aHalgren, ,b,c,d, 1999Halgren & Nachbar, 1996).…”

Section: Methodsmentioning

confidence: 99%

Improved ligand geometries in crystallographic refinement usingAFITTinPHENIX

Janowski

Moriarty

Kelley

et al. 2016

Acta Crystallogr D Struct Biol

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Modern crystal structure refinement programs rely on geometry restraints to overcome the challenge of a low data-to-parameter ratio. While the classical Engh and Huber restraints work well for standard amino-acid residues, the chemical complexity of small-molecule ligands presents a particular challenge. Most current approaches either limit ligand restraints to those that can be readily described in the Crystallographic Information File (CIF) format, thus sacrificing chemical flexibility and energetic accuracy, or they employ protocols that substantially lengthen the refinement time, potentially hindering rapid automated refinement workflows. PHENIX-AFITT refinement uses a full molecular-mechanics force field for user-selected small-molecule ligands during refinement, eliminating the potentially difficult problem of finding or generating high-quality geometry restraints. It is fully integrated with a standard refinement protocol and requires practically no additional steps from the user, making it ideal for high-throughput workflows. PHENIX-AFITT refinements also handle multiple ligands in a single model, alternate conformations and covalently bound ligands. Here, the results of combining AFITT and the PHENIX software suite on a data set of 189 protein-ligand PDB structures are presented. Refinements using PHENIX-AFITT significantly reduce ligand conformational energy and lead to improved geometries without detriment to the fit to the experimental data. For the data presented, PHENIX-AFITT refinements result in more chemically accurate models for small-molecule ligands.

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Section: Methodsmentioning

confidence: 99%

Improved ligand geometries in crystallographic refinement usingAFITTinPHENIX

Janowski

Moriarty

Kelley

et al. 2016

Acta Crystallogr D Struct Biol

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“…Solvation entropy of a ligand is split into electrostatic and hydrophobic components. The hydrophobic part is estimated from scaled particle theory (SPT) [11], and the electrostatic part by the differentiation with respect to the temperature of the Sheffield expression [12] for the free energy of ligand solvation. The method used in the current study is a modification of the original approach [10] in two ways.…”

Section: Mmff/pbmentioning

confidence: 99%

Force-field and quantum-mechanical binding study of selected SAMPL3 host-guest complexes

Hamaguchi

Füsti-Molnár

Włodek

2012

J Comput Aided Mol Des

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A Merck molecular force field classical potential combined with Poisson-Boltzmann electrostatics (MMFF/PB) has been used to estimate the binding free energy of seven guest molecules (six tertiary amines and one primary amine) into a synthetic receptor (acyclic cucurbit[4]uril congener) and two benzimidazoles into cyclic cucurbit[7]uril (CB[7]) and cucurbit[8]uril (CB[8]) hosts. In addition, binding enthalpies for the benzimidazoles were calculated with density functional theory (DFT) using the B3LYP functional and a polarizable continuum model (PCM). Although in most cases the MMFF/PB approach returned reasonable agreements with the experiment (±2 kcal/mol), significant, much larger deviations were reported in the case of three host-guest pairs. All four binding enthalpy predictions with the DFT/PCM method suffered 70% or larger deviations from the calorimetry data. Results are discussed in terms of the molecular models used for guest-host complexation and the quality of the intermolecular potentials.

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“…This produced around 1150 distinct chemical structures. A 3D-structure of each molecule was produced in CORINA [20], which was then optimized using the MMFF94 Forcefield [21], and the Sheffield solvent model [22]. These structures were then used as input into the docking calculations.…”

Section: Protein-ligand Dockingmentioning

confidence: 99%

Using protein-ligand docking to assess the chemical tractability of inhibiting a protein target

Ward

2010

J Mol Model

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Assessing the difficulty of inhibiting a specific protein by a small molecule can be highly valuable in risk-assessment and prioritisation of a new target. In particular, when the disease linkage for a number of targets is broadly similar, being able to identify the most tractable can have a significant impact on informing target selection. With an increasing focus against new and novel protein classes, being able to assess the most likely targets to yield lead-like chemical start points can guide the selection and the lead-generation strategy implemented. This study exploits protein-ligand docking studies on published protein x-ray crystal structures to provide guidance on the feasibility of identifying small molecule inhibitors against a range of targets.Response to Reviewers: 1) I am not convinced that a biased fragment library (the bias is introduced via the filter criteria for logP, MW, and other parameters) should be able to judge the tractability of any target in general. What if potent fragment binders could not idenitified via docking just because they were removed from the initial fragment collection because of the filter criteria ? This concern is somehow reflected already in Fig 7A and 7B. A more general characterization of the fragment library (as expressed by the mean score of t he top 10% docking hits) is only hardly able to distinguish between druggable and "prodrug" binding sites (SP) or "prodrug" and undruggable sites (XP) . A more rigorous approach could for instance be based on a random selection of compounds rather than on a biased fragment library.The key criteria used to build this fragment library is chemical/structural diversity (there is a reference to the details behind how this library was built). This ensures that the library covers a broad range of scaffolds and interaction types. This allows the mapping of a target binding site by fragment screening (experimentally or computationally). The reason for focusing this fragment library around the stated criteria is that fragment hits with properties in these ranges are suggested as being good chemical start points for fragment-based lead generation projects where achieving an orally bioavailable drug is the aim (i.e. a molecule largely compliant with Lipinski-like criteria). See the report below (also reference 12) from Astex on 'Rule of Three' criteria. Our fragment library, however, has not been filtered as harshly as this library as we want to maximise the information from the screen (which is to some extent addressing the issue raised by the reviewer). The other key reason for using this library is that published fragment libraries (filtered similarly to the library I have used in this study) have been used to assess drugability using experimental screening approaches as shown in the reference 4 in the manuscript (below). Therefore there is validation for the use of libraries filtered by these criteria to assess drugability experimentally so I believe it is a suitable fragment set to assess targets computationally. Due to ...

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A simple formula for dielectric polarisation energies: The Sheffield Solvation Model

Cited by 31 publications

References 11 publications

Improved ligand geometries in crystallographic refinement usingAFITTinPHENIX

Improved ligand geometries in crystallographic refinement usingAFITTinPHENIX

Force-field and quantum-mechanical binding study of selected SAMPL3 host-guest complexes

Using protein-ligand docking to assess the chemical tractability of inhibiting a protein target

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