GPCR structures in drug design, emerging opportunities with new structures

AbstractIn recent years, GPCR targets from diverse regions of phylogenetic space have been determined. This effort has culminated this year in the determination of representatives of all major classes of GPCRs (A, B, C, and F). Although much of the now well established knowledge on GPCR structures has been known for some years, the new high-resolution structures allow structural insight into the causes of ligand efficacy, biased signaling, and allosteric modulation. In this digest the structural basis for GPCR signaling in the light of the new structures is reviewed and the use of the new non-class A GPCRs for drug design is discussed

Energy decomposition analysis approaches and their evaluation on prototypical protein–drug interaction patterns

The partitioning of the energy in ab initio quantum mechanical calculations into its chemical origins (e.g., electrostatics, exchange-repulsion, polarization, and charge transfer) is a relatively recent development; such concepts of isolating chemically meaningful energy components from the interaction energy have been demonstrated by variational and perturbation based energy decomposition analysis approaches. The variational methods are typically derived from the early energy decomposition analysis of Morokuma [Morokuma, J. Chem. Phys., 1971, 55, 1236], and the perturbation approaches from the popular symmetry-adapted perturbation theory scheme [Jeziorski et al., Methods and Techniques in Computational Chemistry: METECC-94, 1993, ch. 13, p. 79]. Since these early works, many developments have taken place aiming to overcome limitations of the original schemes and provide more chemical significance to the energy components, which are not uniquely defined. In this review, after a brief overview of the origins of these methods we examine the theory behind the currently popular variational and perturbation based methods from the point of view of biochemical applications. We also compare and discuss the chemical relevance of energy components produced by these methods on six test sets that comprise model systems that display interactions typical of biomolecules (such as hydrogen bonding and pi-pi stacking interactions) including various treatments of the dispersion energy

Second M-3 muscarinic receptor binding site contributes to bronchoprotection by tiotropium

Background and Purpose The bronchodilator tiotropium binds not only to its main binding site on the M-3 muscarinic receptor but also to an allosteric site. Here, we have investigated the functional relevance of this allosteric binding and the potential contribution of this behaviour to interactions with long-acting beta-adrenoceptor agonists, as combination therapy with anticholinergic agents and beta-adrenoceptor agonists improves lung function in chronic obstructive pulmonary disease. Experimental Approach ACh, tiotropium, and atropine binding to M-3 receptors were modelled using molecular dynamics simulations. Contractions of bovine and human tracheal smooth muscle strips were studied. Key Results Molecular dynamics simulation revealed extracellular vestibule binding of tiotropium, and not atropine, to M-3 receptors as a secondary low affinity binding site, preventing ACh entry into the orthosteric binding pocket. This resulted in a low (allosteric binding) and high (orthosteric binding) functional affinity of tiotropium in protecting against methacholine-induced contractions of airway smooth muscle, which was not observed for atropine and glycopyrrolate. Moreover, antagonism by tiotropium was insurmountable in nature. This behaviour facilitated functional interactions of tiotropium with the beta-agonist olodaterol, which synergistically enhanced bronchoprotective effects of tiotropium. This was not seen for glycopyrrolate and olodaterol or indacaterol but was mimicked by the interaction of tiotropium and forskolin, indicating no direct beta-adrenoceptor-M-3 receptor crosstalk in this effect. Conclusions and Implications We propose that tiotropium has two binding sites at the M-3 receptor that prevent ACh action, which, together with slow dissociation kinetics, may contribute to insurmountable antagonism and enhanced functional interactions with beta-adrenoceptor agonists

A “stepping stone” approach for obtaining quantum free energies of hydration

We present a method which uses DFT (quantum, QM) calculations to improve free energies of binding computed with classical force fields (classical, MM). To overcome the incomplete overlap of configurational spaces between MM and QM, we use a hybrid Monte Carlo approach to generate quickly correct ensembles of structures of intermediate states between a MM and a QM/MM description, hence taking into account a great fraction of the electronic polarization of the quantum system, while being able to use thermodynamic integration to compute the free energy of transition between the MM and QM/MM. Then, we perform a final transition from QM/MM to full QM using a one-step free energy perturbation approach. By using QM/MM as a stepping stone toward the full QM description, we find very small convergence errors (<1 kJ/mol) in the transition to full QM. We apply this method to compute hydration free energies, and we obtain consistent improvements over the MM values for all molecules we used in this study. This approach requires large-scale DFT calculations as the full QM systems involved the ligands and all waters in their simulation cells, so the linear-scaling DFT code ONETEP was used for these calculations

A HYBRID CODING STRATEGY FOR OPTIMIZED TEST DATA COMPRESSION

Store-and-generate techniques encode a given test set and regenerate the original test set during the test with the help of a decoder. Previous research has shown that runlength coding, particularly alternating run-length coding, can provide high compression ratios for the test data. However, experimental data show that longer run-lengths are distributed sparsely in the code space and often occur only once, which implies an inefficient encoding. In this study a hybrid encoding strategy is presented which overcomes this problem by combining both the advantages of run-length and dictionary-based encoding. The compression ratios strongly depend on the strategy of mapping don't cares in the original test set to zeros or ones. To find the best assignment an algorithm is proposed which minimizes the total size of the test data consisting of the encoded test set and the dictionary. Experimental results show that the proposed approach works particularly well for larger examples yielding a significant reduction of the total test data storage compared to pure alternating runlength coding

What can we learn from molecular dynamics simulations for GPCR drug design?

Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R)

Electrostatic embedding in large-scale first principles quantum mechanical calculations on biomolecules

Biomolecular simulations with atomistic detail are often required to describe interactions with chemical accuracy for applications such as the calculation of free energies of binding or chemical reactions in enzymes. Force fields are typically used for this task but these rely on extensive parameterisation which in cases can lead to limited accuracy and transferability, for example for ligands with unusual functional groups. These limitations can be overcome with first principles calculations with methods such as density functional theory (DFT) but at a much higher computational cost. The use of electrostatic embedding can significantly reduce this cost by representing a portion of the simulated system in terms of highly localised charge distributions. These classical charge distributions are electrostatically coupled with the quantum system and represent the effect of the environment in which the quantum system is embedded. In this paper we describe and evaluate such an embedding scheme in which the polarisation of the electronic density by the embedding charges occurs self-consistently during the calculation of the density. We have implemented this scheme in a linear-scaling DFT program as our aim is to treat with DFT entire biomolecules (such as proteins) and large portions of the solvent. We test this approach in the calculation of interaction energies of ligands with biomolecules and solvent and investigate under what conditions these can be obtained with the same level of accuracy as when the entire system is described by DFT, for a variety of neutral and charged specie