Journal of Computer-Aided Molecular Design

Methodologies on molecular docking are constantly improving. The problem consists on finding an optimal interplay between the computational cost and a satisfactory physical description of ligand-receptor interaction. In pursuit of an advance in current methods we developed a mixed docking approach combining ligand- and receptor-based strategies in a docking engine, where tridimensional descriptors for shape and charge distribution of a reference ligand guide the initial placement of the docking molecule and an interaction energy-based global minimization follows. This hybrid docking was evaluated with soft-core and force field potentials taking into account ligand pose and scoring. Our approach was found to be competitive to a purely receptor-based dock resulting in improved logAUC values when evaluated with DUD and DUD-E. Furthermore, the smoothed potential as evaluated here, was not advantageous when ligand binding poses were compared to experimentally determined conformations. In conclusion we show that a combination of ligand- and receptor-based strategy docking with a force field energy model results in good reproduction of binding poses and enrichment of active molecules against decoys. This strategy is implemented in our tool, LiBELa, available to the scientific community.

Predicting an accurate binding free energy between a target protein and a ligand can be one of the most important steps in a drug discovery process. Often, many molecules must be screened to find probable high potency ones. Thus, a computational technique with low cost is highly desirable for the estimation of binding free energies of many molecules. Several techniques have thus far been developed for estimating binding free energies. Some techniques provide accurate predictions of binding free energies but high large computational cost. Other methods give good predictions but require tuning of some parameters to predict them with high accuracy. In this study, we propose a method to predict relative binding free energies with accuracy comparable to the results of prior methods but with lower computational cost and with no parameter needing to be carefully tuned. Our technique is based on the free energy variational principle. FK506 binding protein (FKBP) with 18 ligands is taken as a test system. Our results are compared to those from other widely used techniques. Our method provides a correlation coefficient (r 2 ) of 0.80 between experimental and calculated relative binding free energies and yields an average absolute error of 0.70 kcal/mol compared to experimental values. These results are comparable to or better than results from other techniques. We also discuss the possibility to improve our method further.

We describe a graph theoretic method designed to perform efficient searches for substructural patterns in nucleic acid structural coordinate databases using a simplified vectorial representation. Two vectors represent each nucleic acid base and the relative positions of bases with respect to one another are described in terms of distances between the defined start and end points of the vectors on each base. These points comprise the nodes and the distances the edges of a graph, and a pattern search can then be performed using a subgraph isomorphism algorithm. The minimal representation was designed to facilitate searches for complex patterns but was first tested on simple, well-characterised arrangements of bases such as base pairs and GNRA-tetraloop receptor interactions. The method performed very well for these interaction types. A survey of side-by-side base interactions, of which the adenosine platform is the best known example, also locates examples of similar base rearrangements that we consider to be important in structural regulation. A number of examples were found, with GU platforms being particularly prevalent. A GC platform in the RNA of the Thermus thermophilus small ribosomal subunit is in an analogous position to an adenosine platform in other species. An unusual GG platform is also observed close to one of the substrate binding sites in Haloarcula marismortui large ribosomal subunit RNA.

Using structure-based virtual screening approach, a coumarin derivative (1) was identified as β-glucuronidase inhibitor. A focused library of coumarin derivatives was synthesized by eco-benign version of chemical reaction, and all synthetic compounds were characterized by using spectroscopy. These compounds were found to be inhibitor of β-glucuronidase with IC50 values in a micromolar range. All synthetic compounds exhibited interesting inhibitory activity against β-glucuronidase, however, their potency varied substantially from IC50 = 9.9–352.6 µM. Of twenty-one compounds, four exhibited a better inhibitory profile than the initial hit 1. Interestingly, compounds 1e, 1k, 1n and 1p exhibited more potency than the standard inhibitor with IC50 values 34.2, 21.4, 11.7, and 9.9 µM, respectively. We further studied their dose responses and also checked our results by using detergent Triton ×-100. We found that our results are true and not affected by detergent.

Mem-CC (pGlu-Leu-Asn-Tyr-Ser-Pro-Asp-Trp-NH2), Tem-HrTH (pGlu-Leu-Asn-Phe-Ser-Pro-Asn-Trp-NH2) and Del-CC (pGlu-Leu-Asn-Phe-Ser-Pro-Asn-Trp-Gly-Asn-NH2) are adipokinetic hormones, isolated from the corpora cardiaca of different insect species. These hormones regulate energy metabolism during flight and so are intimately involved in an insect's mobility. Secondary structural elements of these peptides and the N7 analogue, [N7]-Mem-CC (pGlu-Leu-Asn-Tyr-Ser-Pro-Asn-Trp-NH2), have been determined in dimethylsulfoxide solution using NMR restrained molecular mechanic simulations. The neuropeptides were all found to have an extended structure for the first 4 residues and a β-turn between residues 4–8. For Tem-HrTH and Del-CC, asparagine (N7) which is postulated to be involved in receptor binding and/or activation, projects outward form the β-turn. Mem-CC does not have an asparagine at position 7 while, for [N7]-Mem-CC, the N7 sidechain folds inside the β-turn preventing its interaction with the receptor.

The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed ‘binding decoys’. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor (kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement structure based docking and scoring approaches in identifying promising hits by virtual screening of molecular libraries.

A new method has been proposed to understand and predict the stability of nitro compounds. This method uses the maximum electron densities at the critical points of two N–O bonds of nitro groups (ρ max), and it is more simple and faster than the existing methods and applicable to bigger systems. The correlations between the ρ max and total energy (E), bond lengths ( $$ R_{{{\text{C}}{-}{\text{NO}}_{2} }} $$ , $$ R_{{{\text{N}}{-}{\text{NO}}_{2} }} $$ and $$ R_{{{\text{O}}{-}{\text{NO}}_{2} }} $$ ), bond dissociation energy (BDE), and impact sensitivity (h 50) reveal that the molecular stability, which can be reflected by E, R, BDE and h 50, generally decreases with the increasing ρ max. The compound with the larger ρ max is less stable. For the nitrating reaction, the smaller ρ max of the product generally implies the easier and faster reaction and the higher occurrence ratio of the product. Therefore, ρ max can be applied to predict the stability of nitro compounds and the easiness of the nitrating reaction.