Thomas Seidel

Molecule-pharmacophore superpositioning and pattern matching in computational drug design

Three-dimensional (3D) pharmacophore modeling is a technique for describing the interaction of a small molecule ligand with a macromolecular target. Since chemical features in a pharmacophore model are well known and highly transparent for medicinal chemists, these models are intuitively understandable and have been increasingly successful in computational drug discovery in the past few years. The performance and applicability of pharmacophore modeling depends on two main factors: the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3D pharmacophore models and small molecules. An overview of key technologies and latest developments in the area of 3D pharmacophores is given and provides insight into different approaches as implemented by the 3D pharmacophore modeling packages like Catalyst, MOE, Phase and LigandScout.

Strategies for 3D pharmacophore-based virtual screening

3D pharmacophore-based techniques have become one of the most important approaches for the fast and accurate virtual screening of databases with millions of compounds. The success of 3D pharmacophores is largely based on their intuitive interpretation and creation, but the virtual screening with such three-dimensional geometric models still poses a considerable algorithmic and conceptual challenge. Most current implementations favor fast screening speed at the detriment of accuracy. This review describes the general strategies and algorithms employed for 3D pharmacophore searching by some current pharmacophore modeling platforms and will highlight their differences.

Evaluating the stability of pharmacophore features using molecular dynamics simulations

Molecular dynamics simulations of twelve protein-ligand systems were used to derive a single, structure based pharmacophore model for each system. These merged models combine the information from the initial experimental structure and from all snapshots saved during the simulation. We compared the merged pharmacophore models with the corresponding PDB pharmacophore models, i.e., the static models generated from an experimental structure in the usual manner. The frequency of individual features, of feature types and the occurrence of features not present in the static model derived from the experimental structure were analyzed. We observed both pharmacophore features not visible in the traditional approach, as well as features which disappeared rapidly during the molecular dynamics simulations and which may well be artifacts of the initial PDB structure-derived pharmacophore model. Our approach helps mitigate the sensitivity of structure based pharmacophore models to the single set of coordinates present in the experimental structure. Further, the frequency with which specific features occur during the MD simulation may aid in ranking the importance of individual features.

Identification of the putative binding pocket of valerenic acid on GABAA receptors using docking studies and site-directed mutagenesis

β2/3‐subunit‐selective modulation of GABAA receptors by valerenic acid (VA) is determined by the presence of transmembrane residue β2/3N265. Currently, it is not known whether β2/3N265 is part of VAs binding pocket or is involved in the transduction pathway of VAs action. The aim of this study was to clarify the localization of VAs binding pocket on GABAA receptors.

Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.

Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations

Pharmacophore modeling is a widely used technique in computer-aided drug discovery. Structure-based pharmacophore models of a ligand in complex with a protein have proven to be useful for supporting in silico hit discovery, hit to lead expansion, and lead optimization. As a structure-based approach it depends on the correct interpretation of ligand–protein interactions. There are legitimate concerns about the fidelity of the bound ligand and about non-physiological contacts with parts of the crystal and the solvent effects that influence the protein structure. A possible way to refine the structure of a protein–ligand system is to use the final structure of a given MD simulation. In this study we compare pharmacophore models built using the initial protein–ligand structure obtained from the protein data bank (PDB) with pharmacophore models built with the final structure of a molecular dynamics simulation. We show that the pharmacophore models differ in feature number and feature type and that the pharmacophore models built from the last structure of a MD simulation shows in some cases better ability to distinguish between active and decoy ligand structures.Graphical abstract

A Molecular Dynamics-Shared Pharmacophore Approach to Boost Early-Enrichment Virtual Screening: A Case Study on Peroxisome Proliferator-Activated Receptorα

Molecular dynamics (MD) simulations can be used, prior to virtual screening, to add flexibility to proteins and study them in a dynamic way. Furthermore, the use of multiple crystal structures of the same protein containing different co‐crystallized ligands can help elucidate the role of the ligand on a protein′s active conformation, and then explore the most common interactions between small molecules and the receptor. In this work, we evaluated the contribution of the combined use of MD on crystal structures containing the same protein but different ligands to examine the crucial ligand–protein interactions within the complexes. The study was carried out on peroxisome proliferator‐activated receptor α (PPARα). Findings derived from the dynamic analysis of interactions were then used as features for pharmacophore generation and constraints for generating the docking grid for use in virtual screening. We found that information derived from short multiple MD simulations using different molecules within the binding pocket of the target can improve the early enrichment of active ligands in the virtual screening process for this receptor. In the end we adopted a consensus scoring based on docking score and pharmacophore alignment to rank our dataset. Our results showed an improvement in virtual screening performance in early recognition when screening was performed with the Molecular dYnamics SHAred PharmacophorE (MYSHAPE) approach.

Design, Synthesis, and Pharmacological Evaluation of Novel β2/3 Subunit-Selective γ-Aminobutyric Acid Type A (GABAA) Receptor Modulators

Subunit-selective modulation of γ-aminobutyric acid type A receptors (GABAAR) is considered to exert fewer side effects compared to unselective clinically used drugs. Here, the β2/3 subunit-selective GABAAR modulators valerenic acid (VA) and loreclezole (LOR) guided the synthesis of novel subunit-selective ligands with simplified structures. We studied their effects on GABAARs expressed in Xenopus laevis oocytes using two-microelectrode voltage clamp technique. Five compounds showed significantly more efficacious modulation of GABA-evoked currents than VA and LOR with retained potency and selectivity. Compound 18 [( E)-2-Cyano-3-(2,4-dichlorophenyl)but-2-enamide] induced the highest maximal modulation of GABA-induced chloride currents ( Emax: 3114 ± 242%), while 12 [( Z)-3-(2,4-dichlorophenyl)but-2-enenitrile] displayed the highest potency (EC50: 13 ± 2 μM). Furthermore, in hippocampal neurons 12 facilitated phasic and tonic GABAergic inhibition, and in vivo studies revealed significantly more potent protection against pentylenetetrazole (PTZ)-induced seizures compared to VA and LOR. Collectively, compound 12 constitutes a novel, simplified, and subunit-selective GABAAR modulator with low-dose anticonvulsant activity.

GRAIL: GRids of phArmacophore Interaction fieLds

In the absence of experimentally derived, three-dimensional structures of receptors in complex with active ligands, it is of high value to be able to gain knowledge about energetically favorable interaction sites solely from the structure of the receptor binding site. For de novo ligand design as well as for lead optimization, this information retrieved from the protein is inevitable. The herein presented method called GRAIL combines the advantages of traditional grid-based approaches for the identification of interaction sites and the power of the pharmacophore concept. A reduced pharmacophoric abstraction of the target system enables the computation of all relevant interaction grid maps in short amounts of time. This allows one to extend the utility of a grid-based method for the analysis of large amounts of coordinate sets obtained by long-time MD simulations. In this way it is possible to assess conformation dependent characteristics of key interactions over time. Furthermore, conformational changes of the protein can be taken into account easily and information thus obtained well-guides a rational ligand design process. A study employing MD trajectories of the oncology target heat shock protein 90 showcases how well our novel approach GRAIL performs for a set of different inhibitors bound to their target protein and how molecular features of the inhibitors are subject to optimization.

Conformational Sampling of Small Molecules With iCon: Performance Assessment in Comparison With OMEGA

Herein we present the algorithm and performance assessment of our newly developed conformer generator iCon that was implemented in LigandScout 4.0. Two data sets of high-quality X-ray structures of drug-like small molecules originating from the Protein Data Bank (200 ligands) and the Cambridge Structural Database (481 molecules) were used to validate iCons performance in the reproduction of experimental conformations. OpenEyes conformer generator OMEGA was subjected to the same evaluation and served as a reference software in this analysis. We tested several setting patterns in order to identify the most suitable and efficient ones for conformational sampling with iCon; equivalent settings were also tested on OMEGA in order to compare the results obtained from the two programs and better assess iCons performance. Overall, this study proved that iCon is able to generate reliable representative conformational ensembles of drug-like small molecules, yielding results comparable to those showed by OMEGA, and thus is ready to serve as a valuable tool for computer-aided drug design.