Site Identification By Ligand Competitive Saturation Research Articles

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms.

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation.

The chemokine receptor CXCR4 is a critical target for the treatment of several cancer types and HIV-1 infections. While orthosteric and allosteric modulators have been developed targeting its extracellular or transmembrane regions, the intramembrane region of CXCR4 may also include allosteric binding sites suitable for the development of allosteric drugs. To investigate this, we apply the Gaussian Network Model (GNM) to the monomeric and dimeric forms of CXCR4 to identify residues essential for its local and global motions located in the hinge regions of the protein. Residue interaction network (RIN) analysis suggests hub residues that participate in allosteric communication throughout the receptor. Mutual residues from the network models reside in regions with a high capacity to alter receptor dynamics upon ligand binding. We then investigate the druggability of these potential allosteric regions using the site identification by ligand competitive saturation (SILCS) approach, revealing two putative allosteric sites on the monomer and three on the homodimer. Two screening campaigns with Glide and SILCS-Monte Carlo docking using FDA-approved drugs suggest 20 putative hit compounds including antifungal drugs, anticancer agents, HIV protease inhibitors, and antimalarial drugs. In vitro assays considering mAB 12G5 and CXCL12 demonstrate both positive and negative allosteric activities of these compounds, supporting our computational approach. However, in vivo functional assays based on the recruitment of β-arrestin to CXCR4 do not show significant agonism and antagonism at a single compound concentration. The present computational pipeline brings a new perspective to computer-aided drug design by combining conformational dynamics based on network analysis and cosolvent analysis based on the SILCS technology to identify putative allosteric binding sites using CXCR4 as a showcase.

Identification and neuroprotective properties of NA-184, a calpain-2 inhibitor.

Our laboratory has shown that calpain-2 activation in the brain following acute injury is directly related to neuronal damage and the long-term functional consequences of the injury, while calpain-1 activation is generally neuroprotective and calpain-1 deletion exacerbates neuronal injury. We have also shown that a relatively selective calpain-2 inhibitor, referred to as C2I, enhanced long-term potentiation and learning and memory, and provided neuroprotection in the controlled cortical impact (CCI) model of traumatic brain injury (TBI) in mice. Using molecular dynamic simulation and Site Identification by Ligand Competitive Saturation (SILCS) software, we generated about 130 analogs of C2I and tested them in a number of invitro and invivo assays. These led to the identification of two interesting compounds, NA-112 and NA-184. Further analyses indicated that NA-184, (S)-2-(3-benzylureido)-N-((R,S)-1-((3-chloro-2-methoxybenzyl)amino)-1,2-dioxopentan-3-yl)-4-methylpentanamide, selectively and dose-dependent inhibited calpain-2 activity without evident inhibition of calpain-1 at the tested concentrations in mouse brain tissues and human cell lines. Like NA-112, NA-184 inhibited TBI-induced calpain-2 activation and cell death in mice and rats, both male and females. Pharmacokinetic and pharmacodynamic analyses indicated that NA-184 exhibited properties, including stability in plasma and liver and blood-brain barrier permeability, that make it a good clinical candidate for the treatment of TBI.

Computational study of ligand dissociation pathways and free-energy barriers using the site-identification by ligand competitive saturation (SILCS) method

Computational study of ligand dissociation pathways and free-energy barriers using the site-identification by ligand competitive saturation (SILCS) method

Site Identification by Ligand Competitive Saturation (SILCS) Based Design and Synthesis of Novel Allosteric Inhibitors of Protein Tyrosine Phosphatase 1B (PTP 1B)

Site Identification by Ligand Competitive Saturation (SILCS) Based Design and Synthesis of Novel Allosteric Inhibitors of Protein Tyrosine Phosphatase 1B (PTP 1B)

Non-β Lactam Inhibitors of the Serine β-Lactamase blaCTX-M15 in Drug-Resistant Salmonella typhi.

Antibiotic resistance by bacterial pathogens against widely used β-lactam drugs is a major concern to public health worldwide, resulting in high healthcare cost. The present study aimed to extend previous research by investigating the potential activity of reported compounds against the S. typhi β-lactamase protein. 74 compounds from computational screening reported in our previous study against β-lactamase CMY-10 were subjected to docking studies against blaCTX-M15. Site-Identification by Ligand Competitive Saturation (SILCS)-Monte Carlo (SILCS-MC) was applied to the top two ligands selected from molecular docking studies to predict and refine their conformations for binding conformations against blaCTX-M15. The SILCS-MC method predicted affinities of -8.6 and -10.7 kcal/mol for Top1 and Top2, respectively, indicating low micromolar binding to the blaCTX-M15 active site. MD simulations initiated from SILCS-MC docked orientations were carried out to better characterize the dynamics and stability of the complexes. Important interactions anchoring the ligand within the active site include pi-pi stacked, amide-pi, and pi-alkyl interactions. Simulations of the Top2-blaCTX-M15 complex exhibited stability associated with a wide range of hydrogen-bond and aromatic interactions between the protein and the ligand. Experimental β-lactamase (BL) activity assays showed that Top1 has 0.1 u/mg BL activity, and Top2 has a BL activity of 0.038 u/mg with a minimum inhibitory concentration of 1 mg/mL. The inhibitors proposed in this study are non-β-lactam-based β-lactamase inhibitors that exhibit the potential to be used in combination with β-lactam antibiotics against multidrug-resistant clinical isolates. Thus, Top1 and Top2 represent lead compounds that increase the efficacy of β-lactam antibiotics with a low dose concentration.

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

Nitro-benzylideneoxymorphone, a bifunctional mu and delta opioid receptor ligand with high mu opioid receptor efficacy.

Introduction: There is a major societal need for analgesics with less tolerance, dependence, and abuse liability. Preclinical rodent studies suggest that bifunctional ligands with both mu (MOPr) and delta (DOPr) opioid peptide receptor activity may produce analgesia with reduced tolerance and other side effects. This study explores the structure-activity relationships (SAR) of our previously reported MOPr/DOPr lead, benzylideneoxymorphone (BOM) with C7-methylene-substituted analogs. Methods: Analogs were synthesized and tested in vitro for opioid receptor binding and efficacy. One compound, nitro-BOM (NBOM, 12) was evaluated for antinociceptive effects in the warm water tail withdrawal assay in C57BL/6 mice. Acute and chronic antinociception was determined, as was toxicologic effects on chronic administration. Molecular modeling experiments were performed using the Site Identification by Ligand Competitive Saturation (SILCS) method. Results: NBOM was found to be a potent MOPr agonist/DOPr partial agonist that produces high-efficacy antinociception. Antinociceptive tolerance was observed, as was weight loss; this toxicity was only observed with NBOM and not with BOM. Modeling supports the hypothesis that the increased MOPr efficacy of NBOM is due to the substituted benzylidene ring occupying a nonpolar region within the MOPr agonist state. Discussion: Though antinociceptive tolerance and non-specific toxicity was observed on repeated administration, NBOM provides an important new tool for understanding MOPr/DOPr pharmacology.

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced μex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.

Structure-Based Design of Potent Iminosugar Inhibitors of Endoplasmic Reticulum α-Glucosidase I with Anti-SARS-CoV-2 Activity.

Enveloped viruses depend on the host endoplasmic reticulum (ER) quality control (QC) machinery for proper glycoprotein folding. The endoplasmic reticulum quality control (ERQC) enzyme α-glucosidase I (α-GluI) is an attractive target for developing broad-spectrum antivirals. We synthesized 28 inhibitors designed to interact with all four subsites of the α-GluI active site. These inhibitors are derivatives of the iminosugars 1-deoxynojirimycin (1-DNJ) and valiolamine. Crystal structures of ER α-GluI bound to 25 1-DNJ and three valiolamine derivatives revealed the basis for inhibitory potency. We established the structure-activity relationship (SAR) and used the Site Identification by Ligand Competitive Saturation (SILCS) method to develop a model for predicting α-GluI inhibition. We screened the compounds against SARS-CoV-2 in vitro to identify those with greater antiviral activity than the benchmark α-glucosidase inhibitor UV-4. These host-targeting compounds are candidates for investigation in animal models of SARS-CoV-2 and for testing against other viruses that rely on ERQC for correct glycoprotein folding.

Molecular simulations of state-specific drug interactions with multiple cardiac ion channels to reveal mechanisms of arrhythmogenesis.

Unintended drug block of cardiac ion channels remains a major problem in drug development. The voltage-gated potassium channel KV11.1 also known as hERG is a major drug anti-target binding a diverse set of small molecule drugs that potently reduce the critical repolarizing current IKr. Many drugs that bind the hERG channel promote deadly arrythmias while some hERG blockers present significantly lower proarrhythmic risk. Two hypotheses were proposed to elucidate this discrepancy: (1) preferential drug binding to the inactivated state of the hERG channel confers greater proarrhythmic risk and (2) simultaneous drug binding to other cardiac ion channels can ameliorate the risk associated with hERG channel block. Here, we present a state-specific molecular modeling assessment of drug binding to different conformations of the hERG and voltage-gated sodium NaV1.5 and calcium CaV1.2 channels. We have developed structural models of these cardiac ion channels in open and inactivated conformations and performed all-atom molecular dynamics (MD) simulations to validate structural stabilities and assess ion conduction. Ligand docking was then performed using Site Identification by Ligand Competitive Saturation (SILCS), a pre-compute ensemble molecular docking technique. SILCS allows us to perform a high-throughput assessment of ligand binding affinities using molecular fragment energy maps derived from MD simulations of state-specific ion channel models. Bayesian machine learning was used to provide improved correlation of SILCS-computed affinities with experimental data. Using SILCS multi-ligand docking we also estimated interactions of drugs with sex hormones in the hERG channel pore to assess a potential molecular mechanism for an increased proarrhythmia risk in females. We aim to use SILCS computed state-specific drug affinity data to inform multi-scale functional kinetic models of cardiac electrophysiology to estimate emergent drug effects on the cardiac action potential and heart rhythm.

HDX-MS guided ensemble reweighting approach reveals cryptic drug binding sites in the cytoplasmic heme binding protein, PhuS.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a valuable approach to probe protein structure and dynamics. However, interpretation of HDX-MS data is often qualitative and typically limited to peptide-level resolution. Ongoing efforts aim to integrate experimental HDX-MS data with molecular dynamics (MD) approaches, to provide a high-resolution interpretation of the HDX-MS data. One such approach is an HDX-MS based maximum-entropy reweighting approach (HDXer) to reweight computationally generated ensembles to HDX-MS data. Here, HDXer is used in conjunction with enhanced MD (eMD) to characterize the structural dynamics of PhuS. PhuS is a cytoplasmic heme binding protein from P. aeruginosa which shuttles exogenous heme to Heme Oxygenase (HemO) for degradation and is essential for virulence. Through the implementation of a workflow that integrates eMD, HDXer, and dimensional reduction, we had previously modeled the native state ensemble of apo-PhuS revealing a locally diverse apo-PhuS ensemble characterized by large rearrangements of helices surrounding the heme binding. Here, the representative structures from the modeled native ensemble were used as starting points for computer aided drug design (CADD). Implementing the Site Identification by Ligand Competitive Saturation (SILCS) CADD method approximately 1.3 million compounds were screened for each representative structure as well as the published apo PhuS crystal structure, yielding a library of 50 chemically representative candidate compounds. The candidate compounds were then screened using a fluorescence quenching assay. From these assays, several hits were confirmed to bind apo PhuS at nanomolar binding affinities. Most significantly, none of the compounds were derived from CADD using the published apo PhuS crystal structure, demonstrating the utility of HDX-MS guiding ensemble reweighting in revealing cryptic drug binding site in CADD approaches.

Development of IKs activating compounds for treatment of long QT syndrome.

The purpose of this study is to optimize the therapeutic potential of IKs (KCNQ1/KCNE1) channel activating polyunsaturated fatty acids (PUFAs) by using computational and experimental methods to increase their potency and specificity. We are interested in activating the IKs channel because loss of function mutations in this channel can cause long QT syndrome, specifically type 1 (LQT1). LQT1 can lead to certain cardiac arrhythmia and even sudden cardiac death. We hypothesized that using computational methods, such as site identification by ligand competitive saturation (SILCS), combined with physiological experiments will provide sufficient information for us to edit our PUFA structures to enhance their binding affinities and thus IKs activation. We recently developed a new compound using these methods that has increased activation effects and affinity for the IKs channel and we are now working to optimize this further.

In silico identification of a β2-adrenoceptor allosteric site that selectively augments canonical β2AR-Gs signaling and function

Activation of β2-adrenoceptors (β2ARs) causes airway smooth muscle (ASM) relaxation and bronchodilation, and β2AR agonists (β-agonists) are front-line treatments for asthma and other obstructive lung diseases. However, the therapeutic efficacy of β-agonists is limited by agonist-induced β2AR desensitization and noncanonical β2AR signaling involving β-arrestin that is shown to promote asthma pathophysiology. Accordingly, we undertook the identification of an allosteric site on β2AR that could modulate the activity of β-agonists to overcome these limitations. We employed the site identification by ligand competitive saturation (SILCS) computational method to comprehensively map the entire 3D structure of in silico-generated β2AR intermediate conformations and identified a putative allosteric binding site. Subsequent database screening using SILCS identified drug-like molecules with the potential to bind to the site. Experimental assays in HEK293 cells (expressing recombinant wild-type human β2AR) and human ASM cells (expressing endogenous β2AR) identified positive and negative allosteric modulators (PAMs and NAMs) of β2AR as assessed by regulation of β-agonist-stimulation of cyclic AMP generation. PAMs/NAMs had no effect on β-agonist-induced recruitment of β-arrestin to β2AR- or β-agonist-induced loss of cell surface expression in HEK293 cells expressing β2AR. Mutagenesis analysis of β2AR confirmed the SILCS identified site based on mutants of amino acids R131, Y219, and F282. Finally, functional studies revealed augmentation of β-agonist-induced relaxation of contracted human ASM cells and bronchodilation of contracted airways. These findings identify a allosteric binding site on the β2AR, whose activation selectively augments β-agonist-induced Gs signaling, and increases relaxation of ASM cells, the principal therapeutic effect of β-agonists.

Abstract 15504: High-Throughput Assessment of Molecular Mechanisms of Drug-Induced Arrhythmia Proclivities and Their Sex Dependence

Introduction: The hERG K + channel is responsible for the repolarizing cardiac current I Kr . Drug block of hERG can prolong the QT interval on the surface electrocardiogram and in some cases promote deadly arrhythmias. Previous studies suggested that drugs that preferentially bind to the hERG channel’s inactivated state have higher proarrhythmic proclivities. Electrophysiology experiments and multiscale simulations also predicted that sex hormones can suppress hERG current and exacerbate drug-induced cardiotoxicity. Hypothesis: Atomistic simulations can make reliable correlations between a drug’s chemical structure and its state-dependent hERG binding affinities allowing us to distinguish safe from dangerous hERG blockers. Methods: We utilized site identification by ligand competitive saturation (SILCS), a pre-compute ensemble molecular docking technique, to predict hERG-channel binding poses and affinities of drug molecules. To improve agreement of SILCS results with experimental and molecular dynamics simulation data, we used a Bayesian machine learning protocol to optimize docking parameters. Results: We analyzed state-dependent binding of a sample set of 55 known hERG blockers and seven sex hormones to available structural models of the hERG channel. SILCS computed dissociation constants, K D , show good correlation with experimental IC 50 values for our open-state model (Panel A) and predict drug and hormone binding sites in agreement with previous studies (Panel B) validating the methodology. Conclusions: Our computational methodology allows us to do high-throughput screening and differentiation of open vs. inactivated-state hERG channel drug binding and can also predict how hormones may modulate a drug’s hERG blocking activity. This provides more selective criteria for differentiating cardiac safe and dangerous drugs compared to using only hERG block and QT prolongation as surrogate markers of pro-arrhythmia risk.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules.

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

HERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties.

Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson's R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.

In Silico Identification of a β2 Adrenergic Receptor Allosteric Site that Selectively Augments Canonical β2AR‐Gs Signaling and Function

Objective & HypothesisActivation of β2‐adrenergic receptors (β2ARs) leads to airway smooth muscle (ASM) relaxation and bronchodilation. While this facilitates β2ARs agonists (β‐agonists) as the front‐line treatments for asthma and related obstructive airway diseases, their therapeutic efficacy of β‐agonists is limited by agonist‐induced β2AR desensitization and activation of non‐canonical β2AR signaling involving β‐arrestin. Accordingly, we undertook the identification of an allosteric site on β2AR and potential ligands, and ascertain their effects in‐vitro. We hypothesize that activation of specific allosteric site on β2AR could selectively modulate the activity of β‐agonists to overcome these limitations.MethodsWe employed the site identification by ligand competitive saturation (SILCS) computational method to map the entire 3D structure of an in silico‐generated β2AR intermediate conformation. Further, in‐silico database was screened to identify potential ligands using SILCS‐PHARM approach. Levels of cyclic‐AMP were used to assess activation of β2AR‐Gs activation using ELISA. Activation of downstream effector of cAMP, protein kinase A (PKA) was assessed by western blotting and luciferase assay. β‐arrestin recruitment to β2AR was assessed using Bioluminescence Resonance Energy Transfer (BRET) assay. ELISA and immunofluorescence were used to assess cell surface expression. Mutagenesis analysis was used to investigate the potential binding site for the allosteric modulators. Lastly, functional effects of the allosteric modulators were determined by assessing relaxation of human ASM cells embedded in 3D‐collagen gels and murine airways.ResultsDatabase screening using SILCS identified not only putative allosteric site but also potential drug‐like compounds that can bind to the site. Experimental assays in HEK293 cells and human ASM cells identified positive and negative allosteric modulators (PAM and NAM respectively) of β2AR as assessed by generation of cAMP. Co‐stimulation of cells with PAM or NAM modulated β‐agonist‐induced PKA activation. Allosteric modulators had no effect on β‐agonist‐induced recruitment of β‐arrestin to β2AR or β‐agonist‐induced loss of cell surface expression in HEK293 cells expressing β2AR. Mutagenesis analysis of β2AR confirmed the SILCS identified allosteric modulator binding site that comprises of F282, Y219, and R131. Finally, functional studies revealed augmentation of β‐agonist‐induced relaxation of contracted human ASM cells and bronchodilation of contracted murine airways by PAM.ConclusionThese findings identify a novel allosteric binding site on the β2AR, whose activation selectively augments β‐agonist‐induced Gs signaling and increases relaxation of ASM cells, the principal therapeutic effect of β‐agonists in obstructive lung disease such as asthma and COPD.

Optimization of novel lead compounds for treatment of long QT syndrome

The purpose of this study is to optimize the therapeutic potential of IKs (KCNQ1/KCNE1) channel activating Poly Unsaturated Fatty Acids (PUFAs) by using computational and experimental methods to increase their potency and specificity. We hypothesize that the computational method, Site Identification by Ligand Competitive Saturation (SILCS), will further elucidate the chemical environment and potential compound interactions in the known PUFA binding sites on the IKs channel allowing us to edit our PUFA structures to enhance their binding affinities and thus IKs activation.