A Multi-Angle Approach to Predict Peptide-GPCR Complexes: The N/OFQ-NOP System as a Successful AlphaFold Application Case Study.

With nearly 700 structures solved and a growing number of customized structure prediction algorithms being developed at a fast pace, G protein-coupled receptors (GPCRs) are an optimal test case for validating new approaches for the prediction of receptor active state and ligand bioactive conformation complexes. In this study, we leveraged the availability of hundreds of peptide GPCRs in the active state and both classical homology and artificial intelligence (AI) based protein modeling combined with docking and AI-based peptide structure prediction approaches to predict the nociceptin/orphanin FQ-NOP receptor active state complex (N/OFQ-NOPa). The In Silico generated hypotheses were validated via the design, synthesis, and pharmacological characterization of novel linear N/OFQ(1-13)-NH2 analogues, leading to the discovery of a novel antagonist (3B; pKB = 6.63) bearing a single ring-constrained residue in place of the Gly2-Gly3 motif of the N/OFQ message sequence (FGGF). While the experimental validation was ongoing, the availability of the Cryo-EM structure of the predicted complex enabled us to unambiguously validate the generated hypotheses. To the best of our knowledge, this is the first example of a peptide-GPCR complex predicted with atomistic accuracy (full complex Cα RMSD < 1.0 Å) and of the N/OFQ message moiety being successfully modified with a rigid scaffold.

Enhancing torsional sampling using fully adaptive simulated tempering.

Enhanced sampling algorithms are indispensable when working with highly disconnected multimodal distributions. An important application of these is the conformational exploration of particular internal degrees of freedom of molecular systems. However, despite the existence of many commonly used enhanced sampling algorithms to explore these internal motions, they often rely on system-dependent parameters, which negatively impact efficiency and reproducibility. Here, we present fully adaptive simulated tempering (FAST), a variation of the irreversible simulated tempering algorithm, which continuously optimizes the number, parameters, and weights of intermediate distributions to achieve maximally fast traversal over a space defined by the change in a predefined thermodynamic control variable such as temperature or an alchemical smoothing parameter. This work builds on a number of previously published methods, such as sequential Monte Carlo, and introduces a novel parameter optimization procedure that can, in principle, be used in any expanded ensemble algorithms. This method is validated by being applied on a number of different molecular systems with high torsional kinetic barriers. We also consider two different soft-core potentials during the interpolation procedure and compare their performance. We conclude that FAST is a highly efficient algorithm, which improves simulation reproducibility and can be successfully used in a variety of settings with the same initial hyperparameters.

Near-Complete Response to Osimertinib for Advanced Non-Small-Cell Lung Cancer in a Pretreated Patient Bearing Rare Compound Exon 20 Mutation (S768I + V774M): A Case Report.

Third-generation tyrosine kinase inhibitors are the first-line gold standard in treating advanced non-small-cell lung cancer bearing common EGFR mutations, but data documenting clinical efficacy in uncommon mutations are currently limited. In this paper, we describe the case of a patient bearing uncommon compound EGFR mutations in exon 20, who experienced a near-complete response to third-line Osimertinib, with metabolic complete response of pulmonary, nodal and ostheolytic lesions. This radiological assessment corresponded to an ECOG PS improvement (from three to one) and a substantial clinical benefit for the patients. Out of two mutations, S768I was associated with poor response to third-generation TKI and V774M had unknown clinical significance, highlighting the complexity of the correct management of these kinds of mutations. We reviewed the literature to document the up-to-date preclinical and clinical data concerning third-generation tyrosine kinase inhibitors for the treatment of patients bearing uncommon EGFR mutations.

A3 adenosine receptor activation mechanisms: molecular dynamics analysis of inactive, active, and fully active states.

We investigated the Gi-coupled A3 adenosine receptor (A3AR) activation mechanism by running 7.2 µs of molecular dynamics (MD) simulations. Based on homology to G protein-coupled receptor (GPCR) structures, three constitutively active mutant (CAM) and the wild-type (WT) A3ARs in the apo form were modeled. Conformational signatures associated with three different receptor states (inactive R, active R*, and bound to Gi protein mimic) were predicted by analyzing and comparing the CAMs with WT receptor and by considering site-directed mutagenesis data. Detected signatures that were correlated with receptor state included: Persistent salt-bridges involving key charged residues for activation (including a novel, putative ionic lock), rotameric state of conserved W6.48, and Na+ ions and water molecules present. Active-coupled state signatures similar to the X-ray structures of ß2 adrenergic receptor-Gs protein and A2AAR-mini-Gs and the recently solved cryo-EM A1AR-Gi complexes were found. Our MD analysis suggests that constitutive activation might arise from the D1073.49-R1083.50 ionic lock destabilization in R and the D1073.49-R1113.53 ionic lock stabilization in R* that presumably lowers the energy barrier associated with an R to R* transition. This study provides new opportunities to understand the underlying interactions of different receptor states of other Gi protein-coupled GPCRs.

A3 Adenosine Receptors as Modulators of Inflammation: From Medicinal Chemistry to Therapy.

The A3 adenosine receptor (A3 AR) subtype is a novel, promising therapeutic target for inflammatory diseases, such as rheumatoid arthritis (RA) and psoriasis, as well as liver cancer. A3 AR is coupled to inhibition of adenylyl cyclase and regulation of mitogen-activated protein kinase (MAPK) pathways, leading to modulation of transcription. Furthermore, A3 AR affects functions of almost all immune cells and the proliferation of cancer cells. Numerous A3 AR agonists, partial agonists, antagonists, and allosteric modulators have been reported, and their structure-activity relationships (SARs) have been studied culminating in the development of potent and selective molecules with drug-like characteristics. The efficacy of nucleoside agonists may be suppressed to produce antagonists, by structural modification of the ribose moiety. Diverse classes of heterocycles have been discovered as selective A3 AR blockers, although with large species differences. Thus, as a result of intense basic research efforts, the outlook for development of A3 AR modulators for human therapeutics is encouraging. Two prototypical selective agonists, N6-(3-Iodobenzyl)adenosine-5'-N-methyluronamide (IB-MECA; CF101) and 2-chloro-N6-(3-iodobenzyl)-adenosine-5'-N-methyluronamide (Cl-IB-MECA; CF102), have progressed to advanced clinical trials. They were found safe and well tolerated in all preclinical and human clinical studies and showed promising results, particularly in psoriasis and RA, where the A3 AR is both a promising therapeutic target and a biologically predictive marker, suggesting a personalized medicine approach. Targeting the A3 AR may pave the way for safe and efficacious treatments for patient populations affected by inflammatory diseases, cancer, and other conditions.

Demystifying P2Y1 Receptor Ligand Recognition through Docking and Molecular Dynamics Analyses.

We performed a molecular modeling analysis of 100 nucleotide-like bisphosphates and 46 non-nucleotide arylurea derivatives previously reported as P2Y1R binders using the recently solved hP2Y1R structures. We initially docked the compounds at the X-ray structures and identified the binding modes of representative compounds highlighting key patterns in the structure-activity relationship (SAR). We subsequently subjected receptor complexes with selected key agonists (2MeSADP and MRS2268) and antagonists (MRS2500 and BPTU) to membrane molecular dynamics (MD) simulations (at least 200 ns run in triplicate, simulation time 0.6-1.6 µs per ligand system) while considering alternative protonation states of nucleotides. Comparing the temporal evolution of the ligand-protein interaction patterns with available site-directed mutagenesis (SDM) data and P2Y1R apo state simulation provided further SAR insights and suggested reasonable explanations for loss/gain of binding affinity as well as the most relevant charged species for nucleotide ligands. The MD analysis also predicted local conformational changes required for the receptor inactive state to accommodate nucleotide agonists.

Structural Probing and Molecular Modeling of the A3 Adenosine Receptor: A Focus on Agonist Binding.

Adenosine is an endogenous modulator exerting its functions through the activation of four adenosine receptor (AR) subtypes, termed A1, A2A, A2B and A3, which belong to the G protein-coupled receptor (GPCR) superfamily. The human A3AR (hA3AR) subtype is implicated in several cytoprotective functions. Therefore, hA3AR modulators, and in particular agonists, are sought for their potential application as anti-inflammatory, anticancer, and cardioprotective agents. Structure-based molecular modeling techniques have been applied over the years to rationalize the structure-activity relationships (SARs) of newly emerged A3AR ligands, guide the subsequent lead optimization, and interpret site-directed mutagenesis (SDM) data from a molecular perspective. In this review, we showcase selected modeling-based and guided strategies that were applied to elucidate the binding of agonists to the A3AR and discuss the challenges associated with an accurate prediction of the receptor extracellular vestibule through homology modeling from the available X-ray templates.

Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.

Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires classical MD experiments in a long microsecond time scale that are affordable only with a high-level computational capacity. In order to overcome this limiting factor, we have recently implemented an alternative MD approach, named supervised molecular dynamics (SuMD), and successfully applied it to G protein-coupled receptors (GPCRs). SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand, and also from its receptor binding affinity. In this article, we present an extension of the SuMD application domain including different types of proteins in comparison with GPCRs. In particular, we have deeply analyzed the ligand-protein recognition pathways of six different case studies that we grouped into two different classes: globular and membrane proteins. Moreover, we introduce the SuMD-Analyzer tool that we have specifically implemented to help the user in the analysis of the SuMD trajectories. Finally, we emphasize the limit of the SuMD applicability domain as well as its strengths in analyzing the complexity of ligand-protein recognition pathways.

Understanding allosteric interactions in G protein-coupled receptors using Supervised Molecular Dynamics: A prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000.

The search for G protein-coupled receptors (GPCRs) allosteric modulators represents an active research field in medicinal chemistry. Allosteric modulators usually exert their activity only in the presence of the orthosteric ligand by binding to protein sites topographically different from the orthosteric cleft. They therefore offer potentially therapeutic advantages by selectively influencing tissue responses only when the endogenous agonist is present. The prediction of putative allosteric site location, however, is a challenging task. In facts, they are usually located in regions showing more structural variation among the family members. In the present work, we applied the recently developed Supervised Molecular Dynamics (SuMD) methodology to interpret at the molecular level the positive allosteric modulation mediated by LUF6000 toward the human adenosine A3 receptor (hA3 AR). Our data suggest at least two possible mechanisms to explain the experimental data available. This study represent, to the best of our knowledge, the first case reported of an allosteric recognition mechanism depicted by means of molecular dynamics simulations.

DockBench: An Integrated Informatic Platform Bridging the Gap between the Robust Validation of Docking Protocols and Virtual Screening Simulations.

Virtual screening (VS) is a computational methodology that streamlines the drug discovery process by reducing costs and required resources through the in silico identification of potential drug candidates. Structure-based VS (SBVS) exploits knowledge about the three-dimensional (3D) structure of protein targets and uses the docking methodology as search engine for novel hits. The success of a SBVS campaign strongly depends upon the accuracy of the docking protocol used to select the candidates from large chemical libraries. The identification of suitable protocols is therefore a crucial step in the setup of SBVS experiments. Carrying out extensive benchmark studies, however, is usually a tangled task that requires users' proficiency in handling different file formats and philosophies at the basis of the plethora of existing software packages. We present here DockBench 1.0, a platform available free of charge that eases the pipeline by automating the entire procedure, from docking benchmark to VS setups. In its current implementation, DockBench 1.0 handles seven docking software packages and offers the possibility to test up to seventeen different protocols. The main features of our platform are presented here and the results of the benchmark study of human Checkpoint kinase 1 (hChk1) are discussed as validation test.

Bridging molecular docking to membrane molecular dynamics to investigate GPCR-ligand recognition: the human A2A adenosine receptor as a key study.

G protein-coupled receptors (GPCRs) represent the largest family of cell-surface receptors and about one-third of the actual targets of clinically used drugs. Following the progress made in the field of GPCRs structural determination, docking-based screening for novel potent and selective ligands is becoming an increasingly adopted strategy in the drug discovery process. However, this methodology is not yet able to anticipate the "bioactive" binding mode and discern it among other conformations. In the present work, we present a novel approach consisting in the integration of molecular docking and membrane MD simulations with the aim to merge the rapid sampling of ligand poses into in the binding site, typical of docking algorithms, with the thermodynamic accuracy of MD simulations in describing, at the molecular level, the stability a GPCR-ligand complex embedded into explicit lipid-water environment. To validate our approach, we have chosen as a key study the human A(2A) adenosine receptor (hA(2A) AR) and selected four receptor-antagonist complexes and one receptor-agonist complex that have been recently crystallized. In light of the obtained results, we believe that our novel strategy can be extended to other GPCRs and might represent a valuable tool to anticipate the "bioactive" conformation of high-affinity ligands.

Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study.

Recent advances in structural biology revealed that water molecules play a crucial structural role in the protein architecture and ligand binding of G protein-coupled receptors. In this work, we present an alternative approach to monitor the time-dependent organization of water molecules during the final stage of the ligand-receptor recognition process by means of membrane molecular dynamics simulations. We inspect the variation of fluid dynamics properties of water molecules upon ligand binding with the aim to correlate the results with the binding affinities. The outcomes of this analysis are transferred into a bidimensional graph called water fluid dynamics maps, that allow a fast graphical identification of protein "hot-spots" characterized by peculiar shape and electrostatic properties that can play a critical role in ligand binding. We hopefully believe that the proposed approach might represent a valuable tool for structure-based drug discovery that can be extended to cases where crystal structures are not yet available, or have not been solved at high resolution.

Alternative quality assessment strategy to compare performances of GPCR-ligand docking protocols: the human adenosine A(2A) receptor as a case study.

The progress made in the field of G protein-coupled receptors (GPCRs) structural determination has increased the adoption of docking-driven approaches for the identification or optimization of novel potent and selective ligands. In this work, we compared the performances of the 16 different docking/scoring combinations using the recently released crystal structures of the human A2A AR (hA2A AR) in complex with both agonists and antagonists. The proposed evaluation strategy encompasses the use of three complementary "quality descriptors": (a) the number of conformations generated by a docking algorithm having a RMSD value lower than the crystal structure resolution (R); (b) a novel consensus-based function defined as "protocol score"; and (c) the interaction energy maps (IEMs) analysis, based on the identification of key ligand-receptor interactions observed in the crystal structures.

The path to the G protein-coupled receptor structural landscape: Major milestones and future directions.

G protein-coupled receptors (GPCRs) play a crucial role in cell function by transducing signals from the extracellular environment to the inside of the cell. They mediate the effects of various stimuli, including hormones, neurotransmitters, ions, photons, food tastants and odorants, and are renowned drug targets. Advancements in structural biology techniques, including X-ray crystallography and cryo-electron microscopy (cryo-EM), have driven the elucidation of an increasing number of GPCR structures. These structures reveal novel features that shed light on receptor activation, dimerization and oligomerization, dichotomy between orthosteric and allosteric modulation, and the intricate interactions underlying signal transduction, providing insights into diverse ligand-binding modes and signalling pathways. However, a substantial portion of the GPCR repertoire and their activation states remain structurally unexplored. Future efforts should prioritize capturing the full structural diversity of GPCRs across multiple dimensions. To do so, the integration of structural biology with biophysical and computational techniques will be essential. We describe in this review the progress of nuclear magnetic resonance (NMR) to examine GPCR plasticity and conformational dynamics, of atomic force microscopy (AFM) to explore the spatial-temporal dynamics and kinetic aspects of GPCRs, and the recent breakthroughs in artificial intelligence for protein structure prediction to characterize the structures of the entire GPCRome. In summary, the journey through GPCR structural biology provided in this review illustrates how far we have come in decoding these essential proteins architecture and function. Looking ahead, integrating cutting-edge biophysics and computational tools offers a path to navigating the GPCR structural landscape, ultimately advancing GPCR-based applications.

Revisiting a receptor-based pharmacophore hypothesis for human A(2A) adenosine receptor antagonists.

The application of both structure- and ligand-based design approaches represents to date one of the most useful strategies in the discovery of new drug candidates. In the present paper, we investigated how the application of docking-driven conformational analysis can improve the predictive ability of 3D-QSAR statistical models. With the use of the crystallographic structure in complex with the high affinity antagonist ZM 241385 (4-(2-[7-amino-2-(2-furyl)[1,2,4]-triazolo[2,3-a][1,3,5]triazin-5-ylamino]ethyl)phenol), we revisited a general pharmacophore hypothesis for the human A(2A) adenosine receptor of a set of 751 known antagonists, by applying an integrated ligand- and structure-based approach. Our novel pharmacophore hypothesis has been validated by using an external test set of 29 newly synthesized human adenosine receptor antagonists.

Novel Aryl Sulfonamide Derivatives as NLRP3 Inflammasome Inhibitors for the Potential Treatment of Cancer.

The NLRP3 inflammasome is a critical component of innate immunity that senses diverse pathogen- and host-derived molecules. However, its aberrant activation has been associated with the pathogenesis of multiple diseases, including cancer. In this study, we designed and synthesized a series of aryl sulfonamide derivatives (ASDs) to inhibit the NLRP3 inflammasome. Among these, compounds 6c, 7n, and 10 specifically inhibited NLRP3 activation at nanomolar concentrations without affecting the activation of the NLRC4 and AIM2 inflammasomes. Furthermore, we demonstrated that these compounds reduce interleukin-1ß (IL-1ß) production in vivo and attenuate melanoma tumor growth. Moreover, metabolic stability in liver microsomes of 6c, 7n, and 10 was studied along with plasma exposure in mice of the most interesting compound 6c. Therefore, we generated potent NLRP3 inflammasome inhibitors, which can be considered in future medicinal chemistry and pharmacological studies aimed at developing a new therapeutic approach for NLRP3 inflammasome-driven cancer.

Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo.

The sampling problem is one of the most widely studied topics in computational chemistry. While various methods exist for sampling along a set of reaction coordinates, many require system-dependent hyperparameters to achieve maximum efficiency. In this work, we present an alchemical variation of adaptive sequential Monte Carlo (SMC), an irreversible importance resampling method that is part of a well-studied class of methods that have been used in various applications but have been underexplored in computational biophysics. Afterward, we apply alchemical SMC on a variety of test cases, including torsional rotations of solvated ligands (butene and a terphenyl derivative), translational and rotational movements of protein-bound ligands, and protein side chain rotation coupled to the ligand degrees of freedom (T4-lysozyme, protein tyrosine phosphatase 1B, and transforming growth factor ß). We find that alchemical SMC is an efficient way to explore targeted degrees of freedom and can be applied to a variety of systems using the same hyperparameters to achieve a similar performance. Alchemical SMC is a promising tool for preparatory exploration of systems where long-timescale sampling of the entire system can be traded off against short-timescale sampling of a particular set of degrees of freedom over a population of conformers.

A QM/MM study of the binding of RAPTA ligands to cathepsin B.

We have carried out quantum mechanical (QM) and QM/MM (combined QM and molecular mechanics) calculations, as well as molecular dynamics (MD) simulations to study the binding of a series of six RAPTA (Ru(II)-arene-1,3,5-triaza-7-phosphatricyclo-[3.3.1.1] decane) complexes with different arene substituents to cathepsin B. The recently developed QM/MM-PBSA approach (QM/MM combined with Poisson-Boltzmann solvent-accessible surface area solvation) has been used to estimate binding affinities. The QM calculations reproduce the antitumour activities of the complexes with a correlation coefficient (r (2)) of 0.35-0.86 after a conformational search. The QM/MM-PBSA method gave a better correlation (r (2) = 0.59) when the protein was fixed to the crystal structure, but more reasonable ligand structures and absolute binding energies were obtained if the protein was allowed to relax, indicating that the ligands are strained when the protein is kept fixed. In addition, the best correlation (r (2) = 0.80) was obtained when only the QM energies were used, which suggests that the MM and continuum solvation energies are not accurate enough to predict the binding of a charged metal complex to a charged protein. Taking into account the protein flexibility by means of MD simulations slightly improves the correlation (r (2) = 0.91), but the absolute energies are still too large and the results are sensitive to the details in the calculations, illustrating that it is hard to obtain stable predictions when full flexible protein is included in the calculations.

Sensitivity of Binding Free Energy Calculations to Initial Protein Crystal Structure.

Binding free energy calculations using alchemical free energy (AFE) methods are widely considered to be the most rigorous tool in the computational drug discovery arsenal. Despite this, the calculations suffer from accuracy, precision, and reproducibility issues. In this publication, we perform a high-throughput study of more than a thousand AFE calculations, utilizing over 220 µs of total sampling time, on three different protein systems to investigate the impact of the initial crystal structure on the resulting binding free energy values. We also consider the influence of equilibration time and discover that the initial crystal structure can have a significant effect on free energy values obtained at short timescales that can manifest itself as a free energy difference of more than 1 kcal/mol. At longer timescales, these differences are largely overtaken by important rare events, such as torsional ligand motions, typically resulting in a much higher uncertainty in the obtained values. This work emphasizes the importance of rare event sampling and long-timescale dynamics in free energy calculations even for routinely performed alchemical perturbations. We conclude that an optimal protocol should not only concentrate computational resources on achieving convergence in the alchemical coupling parameter (λ) space but also on longer simulations and multiple repeats.

Probe Confined Dynamic Mapping for G Protein-Coupled Receptor Allosteric Site Prediction.

Targeting G protein-coupled receptors (GPCRs) through allosteric sites offers advantages over orthosteric sites in identifying drugs with increased selectivity and potentially reduced side effects. In this study, we developed a probe confined dynamic mapping protocol that allows the prediction of allosteric sites at both the GPCR extracellular and intracellular sides, as well as at the receptor-lipid interface. The applied harmonic wall potential enhanced sampling of probe molecules in a selected area of a GPCR while preventing membrane distortion in molecular dynamics simulations. The specific probes derived from GPCR allosteric ligand structures performed better in allosteric site mapping compared to commonly used cosolvents. The M2 muscarinic, ß2 adrenergic, and P2Y1 purinergic receptors were selected for the protocol's retrospective validation. The protocol was next validated prospectively to locate the binding site of [5-fluoro-4-(hydroxymethyl)-2-methoxyphenyl]-(4-fluoro-1H-indol-1-yl)methanone at the D2 dopamine receptor, and subsequent mutagenesis confirmed the prediction. The protocol provides fast and efficient prediction of key amino acid residues surrounding allosteric sites in membrane proteins and facilitates the structure-based design of allosteric modulators.