Rapid and accurate assessment of GPCR-ligand interactions Using the fragment molecular orbital-based density-functional tight-binding method.

The reliable and precise evaluation of receptor-ligand interactions and pair-interaction energy is an essential element of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been used to accelerate QM calculations, and by combining FMO with the density-functional tight-binding (DFTB) method we are able to decrease computational cost 1000 times, achieving results in seconds, instead of hours. We have applied FMO-DFTB to three different GPCR-ligand systems. Our results correlate well with site directed mutagenesis data and findings presented in the published literature, demonstrating that FMO-DFTB is a rapid and accurate means of GPCR-ligand interactions. © 2017 Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Lead diversification. Application to existing drug molecules: mifepristone 1 and antalarmin 8.

A series of C-H functionalisation plate-based chemical screens and other C-H activation protocols were developed for the chemical diversification of drug molecules. In this Letter, metalloporphyrin and other catalytic oxidation systems are described in addition to chlorination. Mifepristone and antalarmin are used as substrates. The products obtained and the biological data demonstrate the potential utility of this approach.

Accurate Scoring in Seconds with the Fragment Molecular Orbital and Density-Functional Tight-Binding Methods.

The accurate evaluation of receptor-ligand interactions is an essential part of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been combined with the density-functional tight-binding (DFTB) method to compute energy calculations of biological systems in seconds. FMO-DFTB outperformed GBVI/WSA in identifying a set of 10 binders versus a background of 500 decoys applied to human k-opioid receptor. The significant increase in the speed and the high accuracy achieved with FMO-DFTB calculations allows FMO to be applied in areas of drug discovery that were not previously accessible to traditional QM methodologies. For the first time, it is now possible to perform FMO calculations in a high-throughput manner.

Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method.

G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanics (QM) approaches are often too computationally expensive to be of practical use in time-sensitive situations, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed, and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule toward ligand binding, including an analysis of their chemical nature. Such information is essential for an efficient structure-based drug design (SBDD) process. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions.

Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method.

G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information. Using the fragment molecular orbital (FMO) quantum mechanics method to analyze 35 crystal structures representing different branches of the class A GPCR family, we have identified 69 topologically equivalent TM residues that form a consensus network of 51 inter-TM interactions, providing novel results that are consistent with and help to rationalize experimental data. This discovery establishes a comprehensive picture of how defined molecular forces govern specific interhelical interactions which, in turn, support the structural stability, ligand binding, and activation of GPCRs.

Computational prediction of GPCR oligomerization.

There has been a recent and prolific expansion in the number of GPCR crystal structures being solved: in both active and inactive forms and in complex with ligand, with G protein and with each other. Despite this, there is relatively little experimental information about the precise configuration of GPCR oligomers during these different biologically relevant states. While it may be possible to identify the experimental conditions necessary to crystallize a GPCR preferentially in a specific structural conformation, computational approaches afford a potentially more tractable means of describing the probability of formation of receptor dimers and higher order oligomers. Ensemble-based computational methods based on structurally determined dimers, coupled with a computational workflow that uses quantum mechanical methods to analyze the chemical nature of the molecular interactions at a GPCR dimer interface, will generate the reproducible and accurate predictions needed to predict previously unidentified GPCR dimers and to inform future advances in our ability to understand and begin to precisely manipulate GPCR oligomers in biological systems. It may also provide information needed to achieve an increase in the number of experimentally determined oligomeric GPCR structures.

Characterising GPCR-ligand interactions using a fragment molecular orbital-based approach.

There has been fantastic progress in solving GPCR crystal structures. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanical approaches (QM) are often too computationally expensive, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule towards ligand binding, including an analysis of their chemical nature.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method.

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity. It is essential for an efficient structure-based drug design (SBDD) process. FMO enables ab initio approaches to be applied to systems that conventional quantum-mechanical (QM) methods would find challenging. The key advantage of the Fragment Molecular Orbital Method (FMO) is that it can reveal atomistic details about the individual contributions and chemical nature of each residue and water molecule toward ligand binding which would otherwise be difficult to detect without using QM methods. In this chapter, we demonstrate the typical use of FMO to analyze 19 crystal structures of ß1 and ß2 adrenergic receptors with their corresponding agonists and antagonists.

The role of tetrahydrobiopterin in catalysis by nitric oxide synthase.

Electronic structure calculations show that the cofactor H4B can be a key factor in a proton transfer relay in nitric oxide synthase, and that 4-amino-H4B cannot fulfill this role.

Guiding lead optimization with GPCR structure modeling and molecular dynamics.

G-protein coupled receptor (GPCR) modeling approaches are widely used in the hit-to-lead and lead optimization stages of drug discovery. Modern protocols that involve molecular dynamics simulation can address key issues such as the free energy of binding (affinity), ligand-induced GPCR flexibility, ligand binding kinetics, conserved water positions and their role in ligand binding and the effects of mutations. The goals of these calculations are to predict the structures of the complexes between existing ligands and their receptors, to understand the key interactions and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this review we present a brief survey of various computational approaches illustrated through a hierarchical GPCR modeling protocol and its prospective application in three industrial drug discovery projects.

Application of an Integrated GPCR SAR-Modeling Platform To Explain the Activation Selectivity of Human 5-HT2C over 5-HT2B.

Agonism of the 5-HT2C serotonin receptor has been associated with the treatment of a number of diseases including obesity, psychiatric disorders, sexual health, and urology. However, the development of effective 5-HT2C agonists has been hampered by the difficulty in obtaining selectivity over the closely related 5-HT2B receptor, agonism of which is associated with irreversible cardiac valvulopathy. Understanding how to design selective agonists requires exploration of the structural features governing the functional uniqueness of the target receptor relative to related off targets. X-ray crystallography, the major experimental source of structural information, is a slow and challenging process for integral membrane proteins, and so is currently not feasible for every GPCR or GPCR-ligand complex. Therefore, the integration of existing ligand SAR data with GPCR modeling can be a practical alternative to provide this essential structural insight. To demonstrate this, we integrated SAR data from 39 azepine series 5-HT2C agonists, comprising both selective and unselective examples, with our hierarchical GPCR modeling protocol (HGMP). Through this work we have been able to demonstrate how relatively small differences in the amino acid sequences of GPCRs can lead to significant differences in secondary structure and function, as supported by experimental data. In particular, this study suggests that conformational differences in the tilt of TM7 between 5-HT2B and 5-HT2C, which result from differences in interhelical interactions, may be the major source of selectivity in G-protein activation between these two receptors. Our approach also demonstrates how the use of GPCR models in conjunction with SAR data can be used to explain activity cliffs.

The Discovery of a Potent, Selective, and Peripherally Restricted Pan-Trk Inhibitor (PF-06273340) for the Treatment of Pain.

The neurotrophin family of growth factors, comprised of nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), neurotrophin 3 (NT3), and neurotrophin 4 (NT4), is implicated in the physiology of chronic pain. Given the clinical efficacy of anti-NGF monoclonal antibody (mAb) therapies, there is significant interest in the development of small molecule modulators of neurotrophin activity. Neurotrophins signal through the tropomyosin related kinase (Trk) family of tyrosine kinase receptors, hence Trk kinase inhibition represents a potentially "druggable" point of intervention. To deliver the safety profile required for chronic, nonlife threatening pain indications, highly kinase-selective Trk inhibitors with minimal brain availability are sought. Herein we describe how the use of SBDD, 2D QSAR models, and matched molecular pair data in compound design enabled the delivery of the highly potent, kinase-selective, and peripherally restricted clinical candidate PF-06273340.

A Simple Ring Current Model for Describing In-Plane Aromaticity in Pericyclic Reactions.

The ring current model allows the characterization of the in-plane aromaticity present in the transition structures of several pericyclic reactions. The differences between in-plane and pi aromaticity can be readily characterized by means of this model. In the presence of a magnetic field, in-plane aromaticity is manifested by a ring current moving along the molecular plane, thus yielding a maximum diamagnetic shielding at the ring point of the electron density. This shielding decays monotonically above and below the molecular plane. In contrast, pi aromaticity can be characterized by two ring currents moving at a certain distance above and below the molecular plane. This distance corresponds to the covalent radius of the atoms involved. Therefore, the diamagnetic shielding is not maximum at the ring point of the electron density.

Drug discovery applications for KNIME: an open source data mining platform.

Technological advances in high-throughput screening methods, combinatorial chemistry and the design of virtual libraries have evolved in the pursuit of challenging drug targets. Over the last two decades a vast amount of data has been generated within these fields and as a consequence data mining methods have been developed to extract key pieces of information from these large data pools. Much of this data is now available in the public domain. This has been helpful in the arena of drug discovery for both academic groups and for small to medium sized enterprises which previously would not have had access to such data resources. Commercial data mining software is sometimes prohibitively expensive and the alternate open source data mining software is gaining momentum in both academia and in industrial applications as the costs of research and development continue to rise. KNIME, the Konstanz Information Miner, has emerged as a leader in open source data mining tools. KNIME provides an integrated solution for the data mining requirements across the drug discovery pipeline through a visual assembly of data workflows drawing from an extensive repository of tools. This review will examine KNIME as an open source data mining tool and its applications in drug discovery.

Predicting intrinsic aqueous solubility by a thermodynamic cycle.

We report methods to predict the intrinsic aqueous solubility of crystalline organic molecules from two different thermodynamic cycles. We find that direct computation of solubility, via ab initio calculation of thermodynamic quantities at an affordable level of theory, cannot deliver the required accuracy. Therefore, we have turned to a mixture of direct computation and informatics, using the calculated thermodynamic properties, along with a few other key descriptors, in regression models. The prediction of log intrinsic solubility (referred to mol/L) by a three-variable linear regression equation gave r(2)=0.77 and RMSE=0.71 for an external test set comprising drug molecules. The model includes a calculated crystal lattice energy which provides a computational method to account for the interactions in the solid state. We suggest that it is not necessary to know the polymorphic form prior to prediction. Furthermore, the method developed here may be applicable to other solid-state systems such as salts or cocrystals.

Pharmacophoric fingerprint method (TOPP) for 3D-QSAR modeling: application to CYP2D6 metabolic stability.

The application of a new 3-point pharmacophore-fingerprinting package (TOPP, Triplets Of Pharmacophoric Points) to develop QSAR models is discussed. In the CYP2D6 metabolic stability case, these 3D pharmacophoric fingerprints have shown to be as valid as other 3D descriptors and 2D features. Interestingly, it was found in the 3D models that the use of more realistic substrate conformations, by an additional docking step, did not improve the statistical results significantly. A detailed analysis of the generated pharmacophoric hypotheses is consistent with the previously proposed dual interaction mode of substrates within the active site of CYP2D6.

Accuracy vs time dilemma on the prediction of NMR chemical shifts: a case study (chloropyrimidines).

The nuclear magnetic shieldings of two chloropyrimidine species have been predicted and analyzed by means of ab initio and DFT methods. The results have been compared with the experimental values and with those from other database-related approaches. These dataset-based techniques are found to be particularly valuable because of the accurate and instantaneous prediction of the 13C chemical shifts. On the other hand, only a few quantum chemistry based approaches were showed to be the most precise to predict 1H chemical shifts and to elucidate unequivocally the 1H NMR spectra of the regioisomeric mixture under study. Special emphasis was put on incorporating the solvent effect, implicitly, or explicitly. The influence of the level of theory and basis set in the predicted values has also been discussed.

Theoretical prediction of the enantiomeric excess in asymmetric catalysis. An alignment-independent molecular interaction field based approach.

[reaction: see text] The enantiomeric excess of three different asymmetric catalyses has been predicted in excellent agreement with the experiments using a 3D-QSPR approach. In particular, GRid INdependent Descriptors generated from molecular interaction fields together with a simple partial least-squares method were found to be adequate to describe the enantioselectivity induced by these metal-ligand complexes.

Carbonyl-ene reactions catalyzed by bis(oxazoline) copper (II) complexes proceed by a facile stepwise mechanism: DFT and ONIOM (DFT:PM3) studies.

The mechanism of a carbonyl-ene addition reaction catalyzed by a bis(oxazoline) copper (II) complex has been studied using DFT methods. We find that the reaction proceeds by a stepwise mechanism with very low barriers and have identified the role of the metal catalyst. We find that the more computationally economic ONIOM method gives accurate geometries for the stationary structures on the potential energy surface but that accurate energetics must be calculated at the full DFT level.