X‐Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A2A Adenosine Receptor Antagonists

We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X‐ray crystallography to reveal the binding mode of an antagonist series to the A2A adenosine receptor (AR). Eight A2AAR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A2AAR were experimentally determined and investigated through a cycle of ligand‐FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X‐ray crystallography of the A2AAR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A2AAR, an emerging target in immuno‐oncologyThis work was financially supported by the Swedish Research Council (Grant 521‐2014‐2118); Consellería de Cultura, Educación e Ordenación Universitaria of the Galician Government (Grant ED431B2017/70); Centro Singular de Investigación de Galicia accreditation 2016–2019 (Grant ED431G/09), and the European Regional Development Fund (ERDF). Additional support from the Swedish strategic research program eSSENCE is acknowledged. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC). This research program has been developed in the frame of the European COST action ERNEST (Grant CA 18133) and GLISTEN (Grant CA 1207)S

Free energy calculations of G protein-coupled receptor modulation : New methods and applications

G protein-coupled receptors (GPCRs) are membrane proteins that transduce the signals of extracellular ligands, such as hormones, neurotransmitters and metabolites, through an intracellular response via G proteins. They are abundant in human physiology and approximately 34% of the marketed drugs target a GPCR. Upon activation, the receptor undergoes conformational changes to accommodate the binding of the G protein. Our insights in the structural determinants of ligand binding and receptor activation have increased tremendously over the past decade. This has largely been a cause of the growing amount of experimentally determined structures, which provide crucial insights in ligand binding mechanisms. However, in a typical drug design project it is unlikely that such structures can be generated for all ligands. In those cases, computationally derived models of the protein-ligand complex can be generated. Rigorous free energy calculations such as the free energy perturbation (FEP) method, can subsequently provide the missing link between those structures and experimental ligand binding data, and provide further insights in the binding mechanism. In this thesis, two workflows are presented to calculate free energies of binding for ligands to wildtype (QligFEP) and mutant (QresFEP) receptors. Both methods were tested on a set of solvation free energies of sidechain mimics. QligFEP was furthermore applied on three protein-ligand binding datasets, including pair comparisons between topologically unrelated molecules (scaffold hopping). QresFEP was used to calculate protein-ligand binding affinities to mutants of the neuropeptide Y1, and to predicte the effect of receptor modifications on the thermal stability of T4 lysozyme. The remainder of this work focussed on the application of these protocols in the design, synthesis and molecular pharmacology of ligands for the family of adenosine receptor (ARs). These receptors, involved in many physiological processes such as promotion of sleep (caffeine is a well-known inhibitor), have recently been pursued as drug targets in immuno-oncology. QligFEP was used in the design of novel series of antagonists for the A3AR and A2BAR. QresFEP was used to study ligand binding to the A1AR and in a multidisciplinary approach to characterize binding to the orphan receptor GPR139. Both approaches were combined to design a series of A2AAR antagonist, and to propose a binding mode later confirmed by new crystal structures. Finally, a new application of FEP is introduced based on conformational equilibria between the active and inactive A2AAR, to elucidate the regulation mechanism of receptor activation by ligands and receptor mutations

Free energy calculations of G protein-coupled receptor modulation : New methods and applications

G protein-coupled receptors (GPCRs) are membrane proteins that transduce the signals of extracellular ligands, such as hormones, neurotransmitters and metabolites, through an intracellular response via G proteins. They are abundant in human physiology and approximately 34% of the marketed drugs target a GPCR. Upon activation, the receptor undergoes conformational changes to accommodate the binding of the G protein. Our insights in the structural determinants of ligand binding and receptor activation have increased tremendously over the past decade. This has largely been a cause of the growing amount of experimentally determined structures, which provide crucial insights in ligand binding mechanisms. However, in a typical drug design project it is unlikely that such structures can be generated for all ligands. In those cases, computationally derived models of the protein-ligand complex can be generated. Rigorous free energy calculations such as the free energy perturbation (FEP) method, can subsequently provide the missing link between those structures and experimental ligand binding data, and provide further insights in the binding mechanism. In this thesis, two workflows are presented to calculate free energies of binding for ligands to wildtype (QligFEP) and mutant (QresFEP) receptors. Both methods were tested on a set of solvation free energies of sidechain mimics. QligFEP was furthermore applied on three protein-ligand binding datasets, including pair comparisons between topologically unrelated molecules (scaffold hopping). QresFEP was used to calculate protein-ligand binding affinities to mutants of the neuropeptide Y1, and to predicte the effect of receptor modifications on the thermal stability of T4 lysozyme. The remainder of this work focussed on the application of these protocols in the design, synthesis and molecular pharmacology of ligands for the family of adenosine receptor (ARs). These receptors, involved in many physiological processes such as promotion of sleep (caffeine is a well-known inhibitor), have recently been pursued as drug targets in immuno-oncology. QligFEP was used in the design of novel series of antagonists for the A3AR and A2BAR. QresFEP was used to study ligand binding to the A1AR and in a multidisciplinary approach to characterize binding to the orphan receptor GPR139. Both approaches were combined to design a series of A2AAR antagonist, and to propose a binding mode later confirmed by new crystal structures. Finally, a new application of FEP is introduced based on conformational equilibria between the active and inactive A2AAR, to elucidate the regulation mechanism of receptor activation by ligands and receptor mutations

QligFEP : an automated workflow for small molecule free energy calculations in Q

The process of ligand binding to a biological target can be represented as the equilibrium between the relevant solvated and bound states of the ligand. This which is the basis of structure-based, rigorous methods such as the estimation of relative binding affinities by free energy perturbation (FEP). Despite the growing capacity of computing power and the development of more accurate force fields, a high throughput application of FEP is currently hampered due to the need, in the current schemes, of an expert user definition of the alchemical transformations between molecules in the series explored. Here, we present QligFEP, a solution to this problem using an automated workflow for FEP calculations based on a dual topology approach. In this scheme, the starting poses of each of the two ligands, for which the relative affinity is to be calculated, are explicitly present in the MD simulations associated with the (dual topology) FEP transformation, making the perturbation pathway between the two ligands univocal. We show that this generalized method can be applied to accurately estimate solvation free energies for amino acid sidechain mimics, as well as the binding affinity shifts due to the chemical changes typical of lead optimization processes. This is illustrated in a number of protein systems extracted from other FEP studies in the literature: inhibitors of CDK2 kinase and a series of A(2A) adenosine G protein-coupled receptor antagonists, where the results obtained with QligFEP are in excellent agreement with experimental data. In addition, our protocol allows for scaffold hopping perturbations to identify the binding affinities between different core scaffolds, which we illustrate with a series of Chk1 kinase inhibitors. QligFEP is implemented in the open-source MD package Q, and works with the most common family of force fields: OPLS, CHARMM and AMBER

UnCorrupt SMILES: a novel approach to de novo design

Generative deep learning models have emerged as a powerful approach for de novo drug design, as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors. Here, we propose to fix these invalid outputs post hoc. In similar tasks, transformer models from the field of natural language processing have been shown to be very effective. Therefore, here this type of model was trained to translate invalid Simplified Molecular-Input Line-Entry System (SMILES) into valid representations. The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-directed RNN, a generative adversarial network (GAN), and a variational autoencoder (VAE). This study has found that the percentage of invalid outputs from these specific generative models ranges between 4 and 89 %, with different models having different error type distributions. Post hoc correction of SMILES increases model validity, with the SMILES corrector fixing 35 to 80 % of invalid model outputs. While, corrector models trained with one error per input sequence alter 60 to 90 % of invalid inputs, a higher performance was obtained for transformer models trained with multiple errors per input. In this case, the best model was able to correct 60 to 95 % of invalid generator outputs. Further analysis showed that these fixed molecules are comparable to the correct molecules from the de novo generators with regard to novelty and similarity. Additionally, the SMILES corrector can also be used to expand the amount of interesting new molecules within the targeted chemical space. Introducing different errors into existing molecules yields novel analogs with a uniqueness of 39 % and a novelty of approximately 20 %. The results of this research demonstrate that SMILES correction is a viable post hoc extension and can enhance the search for better drug candidates

Free-Energy Calculations for Bioisosteric Modifications of A(3) Adenosine Receptor Antagonists

Adenosine receptors are a family of G protein-coupled receptors with increased attention as drug targets on different indications. We investigate the thermodynamics of ligand binding to the A(3) adenosine receptor subtype, focusing on a recently reported series of diarylacetamidopyridine inhibitors via molecular dynamics simulations. With a combined approach of thermodynamic integration and one-step perturbation, we characterize the impact of the charge distribution in a central heteroaromatic ring on the binding affinity prediction. Standard charge distributions according to the GROMOS force field yield values in good agreement with the experimental data and previous free energy calculations. Subsequently, we examine the thermodynamics of inhibitor binding in terms of the energetic and entropic contributions. The highest entropy penalties are found for inhibitors with methoxy substituents in meta position of the aryl groups. This bulky group restricts rotation of aromatic rings attached to the pyrimidine core which leads to two distinct poses of the ligand. Our predictions support the previously proposed binding pose for the o-methoxy ligand, yielding in this case a very good correlation with the experimentally measured affinities with deviations below 4 kJ/mol

Theoretical Infrared Spectra : Quantitative Similarity Measures and Force Fields

Infrared spectroscopy can provide significant insight into the structures and dynamics of molecules of all sizes. The information that is contained in the spectrum is, however, often not easily extracted without the aid of theoretical calculations or simulations. We present here the calculation of the infrared spectra of a database of 703 gas phase compounds with four different force fields (CGenFF, GAFF-BCC, GAFF-ESP, and OPLS) using normal-mode analysis. Modern force fields increasingly use virtual sites to describe, e.g., lone-pair electrons or the o -holes on halogen atoms. This requires some adaptation of code to perform normal-mode analysis of such compounds, the implementation of which into the GROMACS software is briefly described as well. For the quantitative comparison of the obtained spectra with experimental reference data, we discuss the application of two different statistical correlation coefficients, Pearson and Spearman. The advantages and drawbacks of the different methods of comparison are discussed, and we find that both methods of comparison give the same overall picture, showing that present force field methods cannot match the performance of quantum chemical methods for the calculation of infrared spectra

Structure-Based Design of Potent and Selective Ligands at the Four Adenosine Receptors

The four receptors that signal for adenosine, A1, A2A, A2B and A3 ARs, belong to the superfamily of G protein-coupled receptors (GPCRs). They mediate a number of (patho)physiological functions and have attracted the interest of the biopharmaceutical sector for decades as potential drug targets. The many crystal structures of the A2A, and lately the A1 ARs, allow for the use of advanced computational, structure-based ligand design methodologies. Over the last decade, we have assessed the efficient synthesis of novel ligands specifically addressed to each of the four ARs. We herein review and update the results of this program with particular focus on molecular dynamics (MD) and free energy perturbation (FEP) protocols. The first in silico mutagenesis on the A1AR here reported allows understanding the specificity and high affinity of the xanthine-antagonist 8-Cyclopentyl-1,3-dipropylxanthine (DPCPX). On the A2AAR, we demonstrate how FEP simulations can distinguish the conformational selectivity of a recent series of partial agonists. These novel results are complemented with the revision of the first series of enantiospecific antagonists on the A2BAR, and the use of FEP as a tool for bioisosteric design on the A3AR