Atom equivalents for converting DFT energies calculated on molecular mechanics structures to formation enthalpies

New atom equivalents are introduced to convert BP/DN**//MMFF energies into formation enthalpies. As a result of using molecular mechanics structures, poor results are obtained for compounds outside the scope of the force field, such as those bearing  NF₂ groups or some nitrogenous systems. Notwithstanding these limitations, present procedures compare well with the results of previous atom equivalents schemes. Indeed, rms deviations from experiment are below 9 kJ/mol for hydrocarbons, and close to 16 kJ/mol for a variety of compounds reasonably well described by MMFF. The explicit inclusion of thermal and vibrational contributions, using calculated frequencies, does not improve the results. This study demonstrates that cost‐effective approaches to formation enthalpies may be developed on the basis of a combination of DFT with a suitable molecular mechanics force field. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 367–379, 2000     

Pharmacophore and receptor models for neurokinin receptors

Three neurokinin (NK) antagonist pharmacophore models (Models 1-3) accounting for hydrogen bonding groups in the 'head' and 'tail' of NK receptor ligands have been developed by use of a new procedure for treatment of hydrogen bonds during superimposition. Instead of modelling the hydrogen bond acceptor vector in the strict direction of the lone pair, an angle is allowed between the hydrogen bond acceptor direction and the ideal lone pair direction. This approach adds flexibility to hydrogen bond directions and produces more realistic RMS values. By using this approach, two novel pharmacophore models were derived (Models 2 and 3) and a hydrogen bond acceptor was added to a previously published NK2 pharmacophore model [Poulsen et al., J. Comput.-Aided Mol. Design, 16 (2002) 273] (Model 1). Model 2 as well as Model 3 are described by seven pharmacophore elements: three hydrophobic groups, three hydrogen bond acceptors and a hydrogen bond donor. Model 1 contains the same hydrophobic groups and hydrogen bond donor as Models 2 and 3, but only one hydrogen bond acceptor. The hydrogen bond acceptors and donor are represented as vectors. Two of the hydrophobic groups are always aromatic rings whereas the other hydrophobic group can be either aromatic or aliphatic. In Model 1 the antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. Model 2 has the same two aromatic rings in a parallel displaced conformation whereas Model 3 has the rings in an edge to face conformation. The pharmacophore models were evaluated using both a structure (NK receptor homology models) and a ligand based approach. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 21 non-peptide antagonists from several structurally diverse classes were fitted to the pharmacophore models. More antagonists could be fitted to Model 2 with a low RMS and a low conformational energy penalty than to Models 1 and 3. Pharmacophore Model 2 was also able to explain the NK1, NK2 and NK3 subtype selectivity of the compounds fitted to the model. Three NK 7TM receptor models were constructed, one for each receptor subtype. The location of the antagonist binding site in the three NK receptor models is identical. Compounds fitted to pharmacophore Model 2 could be docked into the NK1, NK2 and NK3 receptor models after adjustment of the conformation of the flexible linker connecting the head and tail. Models I and 3 are not compatible with the receptor models.     

A pharmacophore model for NK2 antagonist comprising compounds from several structurally diverse classes

A neurokinin 2 (NK2) antagonist pharmacophore model has been developed on the basis of five non-peptide antagonists from several structurally diverse classes. To evaluate the pharmacophore model, another 20 antagonists were fitted to the model. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 23 of the 25 antagonists were fitted to the model in a low energy conformation with a low RMS value. The pharmacophore model is described by four pharmacophore elements: Three hydrophobic groups and a hydrogen bond donor represented as a vector. The hydrophobic groups are generally aromatic rings, but this is not a requirement. The antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. The model was able to explain the enantioselectivity of SR48968 and GR159897.     

Conformational properties of the macrocyclic trichothecene mycotoxin verrucarin A in solution

Phase-sensitive nuclear Overhauser enhancement spectroscopy (NOESY) experiments, (3)J couplings and computational molecular modeling (MM2* and MMFF force fields) were employed to examine the conformational properties of verrucarin A in chloroform solutions. The MMFF force field calculations resulted in a family of 12 low-energy structures along with their populations, the latter being determined by the NMR analysis of molecular flexibility in solution(NAMFIS) deconvolution analysis. The concluded model was capable of reproducing successfully the experimental NOESY cross-peak volumes and the proton-coupling constants. Among the 12 conformers, the one which was similar to the structure of verrucarin A in the solid state was the predominant accounting for 75% of the total relative population, although other low-energy conformations contributed to a lesser degree in order to explain the experimental data.     

GPU‐accelerated molecular mechanics computations

In this article, we describe an improved cell‐list approach designed to match the Kepler architecture of General‐purpose graphics processing units (GPGPU). We explain how our approach improves load balancing for the above algorithm and how warp intrinsics are used to implement Newton's third law for the nonbonded force calculations. We also talk through our approach to exclusions handling together with a method to calculate bonded forces and 1–4 electrostatic scaling using a single Cuda kernel. Performance benchmarks are included in the last sections to show the linear scaling of our implementation using a step minimization method. In addition, multiple performance benchmarks demonstrate the contribution of various optimizations we used for our implementations. © 2013 Wiley Periodicals, Inc.     

Conformational analysis of kainate in aqueous solution in relation to its binding to AMPA and kainic acid receptors

Conformational analyses for kainate in aqueous solution have been performed by using the MM3*, AMBER* and MMFF94 force fields in conjunction with the Generalized Born Solvent Accessible Surface (GB/SA) hydration model. A comparison of calculated results with experimentally determined conformational data indicates that MM3*-GB/SA strongly overestimates the stability of a hydrogen bonded ion-pair in aqueous solution in comparison with the separated and solvated ions. This results in an incorrect prediction by MM3* of the most stable conformer of kainate in aqueous solution, whereas AMBER* and MMFF94 correctly predict the lowest energy conformer. Calculated conformational energy penalties for binding of kainate to the AMPA iGluR2 receptor indicate that the lower affinity of kainate for AMPA receptors compared to its affinity for kainic acid (KA) receptors is not due to a higher energy bioactive conformation of kainate at AMPA receptors. This conclusion is strongly supported by an analysis of a recently reported nonselective AMPA/KA ligand and a comparison of the conformational and structural properties of this ligand with iGluR2-bound kainate. This comparison strongly suggests that kainate binds to AMPA and KA receptors in closely the same conformation.     

Binding of α-hydroxy-β-amino acid inhibitors to methionine aminopeptidase. The performance of two types of scoring functions

The binding mode of a recently described set of -hydroxy--amino acid inhibitors of methionine aminopeptidase type 2 is suggested in the present work. The binding mode is supported by analysis of published structures of transition state analogues co-crystallised with E. coli methionine aminopeptidase and by a comparison of molecular interaction fields calculated using GRID for E. coli and human methionine aminopeptidase. Based on the suggested binding mode two types of scoring functions have been evaluated and compared. These are the commercially available consensus score, CScore, and scoring of the ligands employing energies calculated using the Merck Molecular Force Field (MMFF). Enriched subsets of ligands were obtained when using CScore, but these scores could not be used to assess the relative affinities of individual compounds. Although still not sufficiently accurate for reliable predictive purposes, an improved correlation was obtained between structure and affinity using a combined force field energy including contributions from solvation and conformational energy penalty for binding.     

Identifying conformational changes of the β2 adrenoceptor that enable accurate prediction of ligand/receptor interactions and screening for GPCR modulators

The new β₂ Adrenoceptor (β₂AR) crystal structures provide a high-resolution snapshot of receptor interactions with two particular partial inverse agonists, (−)-carazolol and timolol. However, both experimental and computational studies of GPCR structure are significantly complicated by the existence of multiple conformational states coupled to ligand type and receptor activity. Agonists and antagonists induce or stabilize distinct changes in receptor structure that mediate a range of pharmacological activities. In this work, we (1) established that the existing β₂AR crystallographic conformers can be extended to describe ligand/receptor interactions for additional antagonist types, (2) generated agonist-bound receptor conformations, and (3) validated these models for agonist and antagonist virtual ligand screening (VLS). Using a ligand directed refinement protocol, we derived a single agonist-bound receptor conformation that selectively retrieved a diverse set of full and partial β₂AR agonists in VLS trials. Additionally, the impact of extracellular loop two conformation on VLS was assessed by docking studies with rhodopsin-based β₂AR homology models, and loop-deleted receptor models. A general strategy for constructing and selecting agonist-bound receptor pocket conformations is presented, which may prove broadly useful in creating agonist and antagonist bound models for other GPCRs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.