Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small molecules is key to understand the molecular principles of chemotherapy and for designing new and more effective drugs. During the early stage of a drug discovery program, it is customary to predict ligand-protein complexes in silico , particularly when screening large compound databases. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology to generate bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites by exploiting only information on the unbound structure and the putative binding sites. The protocol is validated on the paradigm enzyme adenylate kinase, for which we generated a significant fraction of bound-like structures. A fraction of these conformations, employed in ensemble-docking calculations, allowed to find native-like poses of substrates and inhibitors (binding to the active form of the enzyme), as well as catalytically incompetent analogs (binding the inactive form). Our protocol provides a general framework for the generation of bound-like conformations of challenging drug targets that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening, in the prediction of the impact of amino acid mutations on structure and dynamics, and in protein engineering.

The value of protein allostery in rational anticancer drug design: an update

INTRODUCTION

Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

AREAS COVERED

The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

EXPERT OPINION

To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

Targeting Tuberculosis: Novel Scaffolds for Inhibiting Cytochrome bd Oxidase

Discovered in the 1920s, cytochrome bd is a terminal oxidase that has received renewed attention as a drug target since its atomic structure was first determined in 2016. Only found in prokaryotes, we study it here as a drug target for Mycobacterium tuberculosis (Mtb). Most previous drug discovery efforts toward cytochrome bd have involved analogues of the canonical substrate quinone, known as Aurachin D. Here, we report six new cytochrome bd inhibitor scaffolds determined from a computational screen and confirmed on target activity through in vitro testing. These scaffolds provide new avenues for lead optimization toward Mtb therapeutics.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Small molecule allosteric modulation of the adenosine A1 receptor

G protein-coupled receptors (GPCRs) represent the target for approximately a third of FDA-approved small molecule drugs. The adenosine A₁ receptor (A₁R), one of four adenosine GPCR subtypes, has important (patho)physiological roles in humans. A₁R has well-established roles in the regulation of the cardiovascular and nervous systems, where it has been identified as a potential therapeutic target for a number of conditions, including cardiac ischemia-reperfusion injury, cognition, epilepsy, and neuropathic pain. A₁R small molecule drugs, typically orthosteric ligands, have undergone clinical trials. To date, none have progressed into the clinic, predominantly due to dose-limiting unwanted effects. The development of A₁R allosteric modulators that target a topographically distinct binding site represent a promising approach to overcome current limitations. Pharmacological parameters of allosteric ligands, including affinity, efficacy and cooperativity, can be optimized to regulate A₁R activity with high subtype, spatial and temporal selectivity. This review aims to offer insights into the A₁R as a potential therapeutic target and highlight recent advances in the structural understanding of A₁R allosteric modulation.

Capturing Differences in the Regulation of LRRK2 Dynamics and Conformational States by Small Molecule Kinase Inhibitors

Mutations in the human leucine rich repeat protein kinase-2 (LRRK2) create risk factors for Parkinson's disease, and pathological functions of LRRK2 are often correlated with aberrant kinase activity. Past research has focused on developing selective LRRK2 kinase inhibitors. In this study, we combined enhanced sampling simulations with HDX-MS to characterize the inhibitor-induced dynamic changes and the allosteric communications within the C-terminal domains of LRRK2, LRRK2RCKW. We find that the binding of MLi-2 (a type I kinase inhibitor) stabilizes a closed kinase conformation and reduces the global dynamics of LRRK2RCKW, leading to a more compact LRRK2RCKW structure. In contrast, the binding of Rebastinib (a type II kinase inhibitor) stabilizes an open kinase conformation, which promotes a more extended LRRK2RCKW structure. By probing the distinct effects of the type I and type II inhibitors, key interdomain interactions are found to regulate the communication between the kinase domain and the GTPase domain. The intermediate states revealed in our simulations facilitate the efforts toward in silico design of allosteric modulators that control LRRK2 conformations and potentially mediate the oligomeric states of LRRK2 and its interactions with other proteins.

Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains

The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least six of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 ms of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin's binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). In a blind prediction for an isoform (Myh7b) whose blebbistatin sensitivity was unknown, we find good agreement between predicted and measured IC50s (0.67 μM vs. 0.36 μM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

Selective inhibitors of SARM1 targeting an allosteric cysteine in the autoregulatory ARM domain

The nicotinamide adenine dinucleotide hydrolase (NADase) sterile alpha toll/interleukin receptor motif containing-1 (SARM1) acts as a central executioner of programmed axon death and is a possible therapeutic target for neurodegenerative disorders. While orthosteric inhibitors of SARM1 have been described, this multidomain enzyme is also subject to intricate forms of autoregulation, suggesting the potential for allosteric modes of inhibition. Previous studies have identified multiple cysteine residues that support SARM1 activation and catalysis, but which of these cysteines, if any, might be selectively targetable by electrophilic small molecules remains unknown. Here, we describe the chemical proteomic discovery of a series of tryptoline acrylamides that site-specifically and stereoselectively modify cysteine-311 (C311) in the noncatalytic, autoregulatory armadillo repeat (ARM) domain of SARM1. These covalent compounds inhibit the NADase activity of WT-SARM1, but not C311A or C311S SARM1 mutants, show a high degree of proteome-wide selectivity for SARM1_C311 and stereoselectively block vincristine- and vacor-induced neurite degeneration in primary rodent dorsal root ganglion neurons. Our findings describe selective, covalent inhibitors of SARM1 targeting an allosteric cysteine, pointing to a potentially attractive therapeutic strategy for axon degeneration-dependent forms of neurological disease.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Accelerating GPCR Drug Discovery With Conformation-Stabilizing VHHs

The human genome encodes 850 G protein-coupled receptors (GPCRs), half of which are considered potential drug targets. GPCRs transduce extracellular stimuli into a plethora of vital physiological processes. Consequently, GPCRs are an attractive drug target class. This is underlined by the fact that approximately 40% of marketed drugs modulate GPCRs. Intriguingly 60% of non-olfactory GPCRs have no drugs or candidates in clinical development, highlighting the continued potential of GPCRs as drug targets. The discovery of small molecules targeting these GPCRs by conventional high throughput screening (HTS) campaigns is challenging. Although the definition of success varies per company, the success rate of HTS for GPCRs is low compared to other target families (Fujioka and Omori, 2012; Dragovich et al., 2022). Beyond this, GPCR structure determination can be difficult, which often precludes the application of structure-based drug design approaches to arising HTS hits. GPCR structural studies entail the resource-demanding purification of native receptors, which can be challenging as they are inherently unstable when extracted from the lipid matrix. Moreover, GPCRs are flexible molecules that adopt distinct conformations, some of which need to be stabilized if they are to be structurally resolved. The complexity of targeting distinct therapeutically relevant GPCR conformations during the early discovery stages contributes to the high attrition rates for GPCR drug discovery programs. Multiple strategies have been explored in an attempt to stabilize GPCRs in distinct conformations to better understand their pharmacology. This review will focus on the use of camelid-derived immunoglobulin single variable domains (VHHs) that stabilize disease-relevant pharmacological states (termed ConfoBodies by the authors) of GPCRs, as well as GPCR:signal transducer complexes, to accelerate drug discovery. These VHHs are powerful tools for supporting in vitro screening, deconvolution of complex GPCR pharmacology, and structural biology purposes. In order to demonstrate the potential impact of ConfoBodies on translational research, examples are presented of their role in active state screening campaigns and structure-informed rational design to identify de novo chemical space and, subsequently, how such matter can be elaborated into more potent and selective drug candidates with intended pharmacology.

Molecular Simulations and Drug Discovery of Adenosine Receptors

G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.

The Impact of the Secondary Binding Pocket on the Pharmacology of Class A GPCRs

G-protein coupled receptors (GPCRs) are considered important therapeutic targets due to their pathophysiological significance and pharmacological relevance. Class A receptors represent the largest group of GPCRs that gives the highest number of validated drug targets. Endogenous ligands bind to the orthosteric binding pocket (OBP) embedded in the intrahelical space of the receptor. During the last 10 years, however, it has been turned out that in many receptors there is secondary binding pocket (SBP) located in the extracellular vestibule that is much less conserved. In some cases, it serves as a stable allosteric site harbouring allosteric ligands that modulate the pharmacology of orthosteric binders. In other cases it is used by bitopic compounds occupying both the OBP and SBP. In these terms, SBP binding moieties might influence the pharmacology of the bitopic ligands. Together with others, our research group showed that SBP binders contribute significantly to the affinity, selectivity, functional activity, functional selectivity and binding kinetics of bitopic ligands. Based on these observations we developed a structure-based protocol for designing bitopic compounds with desired pharmacological profile.

Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites

The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking

Estrogen-related receptor α (ERRα), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERRα have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERRα determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERRα agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERRα with EC50 values ranging from 1.11 to 21.70 μM, with novel scaffolds different from known ERRα agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERRα with an IC50 of 0.82 μM. The predicted binding modes showed that these compounds were anchored in ERRα-LBP via interactions with several residues of ERRα. Overall, this study not only identified the novel potent ERRα agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERRα agonists.

Hidden allosteric sites and De-Novo drug design

INTRODUCTION

Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge.

AREAS COVERED

This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator.

EXPERT OPINION

In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.

Homology Modeling and Molecular Dynamics Simulations of Trypanosoma cruzi Phosphodiesterase b1

Cyclic nucleotide phosphodiesterases have been implicated in the proliferation, differentiation and osmotic regulation of trypanosomatids; in some trypanosomatid species, they have been validated as molecular targets for the development of new therapeutic agents. Because the experimental structure of Trypanosoma cruzi PDEb1 (TcrPDEb1) has not been solved so far, an homology model of the target was created using the structure of Trypanosoma brucei PDEb1 (TbrPDEb1) as a template. The model was refined by extensive enhanced sampling molecular dynamics simulations, and representative snapshots were extracted from the trajectory by combined clustering analysis. This structural ensemble was used to develop a structure-based docking model of the target. The docking accuracy of the model was validated by redocking and cross-docking experiments using all available crystal structures of TbrPDEb1, whereas the scoring accuracy was validated through a retrospective screen, using a carefully curated dataset of compounds assayed against TbrPDEb1 and/or TcrPDEb1. Considering the results from in silico validations, the model may be applied in prospective virtual screening campaigns to identify novel hits, as well as to guide the rational design of potent and selective inhibitors targeting this enzyme.

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning

One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.

VARIDT 2.0: structural variability of drug transporter

The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs' variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT (https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

Structure-Based Discovery of Novel Nonpeptide Inhibitors Targeting SARS-CoV-2 Mpro

The continual spread of novel coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), posing a severe threat to the health worldwide. The main protease (Mpro, alias 3CLpro) of SARS-CoV-2 is a crucial enzyme for the maturation of viral particles and is a very attractive target for designing drugs to treat COVID-19. Here, we propose a multiple conformation-based virtual screening strategy to discover inhibitors that can target SARS-CoV-2 Mpro. Based on this strategy, nine Mpro structures and a protein mimetics library with 8960 commercially available compounds were prepared to carry out ensemble docking for the first time. Five of the nine structures are apo forms presented in different conformations, whereas the other four structures are holo forms complexed with different ligands. The surface plasmon resonance assay revealed that 6 out of 49 compounds had the ability to bind to SARS-CoV-2 Mpro. The fluorescence resonance energy transfer experiment showed that the biochemical half-maximal inhibitory concentration (IC50) values of the six compounds could hamper Mpro activities ranged from 0.69 ± 0.05 to 2.05 ± 0.92 μM. Evaluation of antiviral activity using the cell-based assay indicated that two compounds (Z1244904919 and Z1759961356) could strongly inhibit the cytopathic effect and reduce replication of the living virus in Vero E6 cells with the half-maximal effective concentrations (EC50) of 4.98 ± 1.83 and 8.52 ± 0.92 μM, respectively. The mechanism of the action for the two inhibitors were further elucidated at the molecular level by molecular dynamics simulation and subsequent binding free energy analysis. As a result, the discovered noncovalent reversible inhibitors with novel scaffolds are promising antiviral drug candidates, which may be used to develop the treatment of COVID-19.

Conformational dynamics of androgen receptors bound to agonists and antagonists

The androgen receptor (AR) is critical in the progression of prostate cancer (PCa). Small molecule antagonists that bind to the ligand binding domain (LBD) of the AR have been successful in treating PCa. However, the structural basis by which the AR antagonists manifest their therapeutic efficacy remains unclear, due to the lack of detailed structural information of the AR bound to the antagonists. We have performed accelerated molecular dynamics (aMD) simulations of LBDs bound to a set of ligands including a natural substrate (dihydrotestosterone), an agonist (RU59063) and three antagonists (bicalutamide, enzalutamide and apalutamide) as well as in the absence of ligand (apo). We show that the binding of AR antagonists at the substrate binding pocket alter the dynamic fluctuations of H12, thereby disrupting the structural integrity of the agonistic conformation of AR. Two antagonists, enzalutamide and apalutamide, induce considerable structural changes to the agonist conformation of LBD, when bound close to H12 of AR LBD. When the antagonists bind to the pocket with different orientations having close contact with H11, no significant conformational changes were observed, suggesting the AR remains in the functionally activated (agonistic) state. The simulations on a drug resistance mutant F876L bound to enzalutamide demonstrated that the mutation stabilizes the agonistic conformation of AR LBD, which compromises the efficacy of the antagonists. Principal component analysis (PCA) of the structural fluctuations shows that the binding of enzalutamide and apalutamide induce conformational fluctuations in the AR, which are markedly different from those caused by the agonist as well as another antagonist, bicalutamide. These fluctuations could only be observed with the use of aMD.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

Biomolecular modeling thrives in the age of technology

The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

Mechanistic in silico modeling of bisphenols to predict estrogen and glucocorticoid disrupting potentials

Endocrine disrupting chemicals (EDCs) can act as agonists, antagonists or mixed agonists/antagonists toward estrogen receptor α (ERα) and glucocorticoid receptor (GR) in a tissue- and cell-specific manner. However, the activation/inhibition mechanism by which structurally different chemicals induce various types of disruption remain ambiguous. This unrevealed theory limited the in silico modeling of EDCs and the prioritization of potential EDCs for experimental testing. As a kind of chemical widely used in manufacture, bisphenols (BPs) have attracted great attentions on their potential endocrine disrupting effects. BPs used in this study exhibited pure agonistic, pure antagonistic or mixed agonistic/antagonistic activities toward ERα and/or GR. According to the mechanistic modeling, the pure agonistic and pure antagonistic activities were attributed to a single type of protein conformation induced by BPs-ERα and/or BPs-GR interactions, whereas the mixed agonistic/antagonistic activities were attributed to multiple conformations that concomitantly exist. After interacting with BPs, the active conformation recruits coactivator to induce agonistic activity and the blocked conformation inhibits coactivator to induce antagonistic activity, whereas the concomitantly-existing multiple conformations (active, blocked and competing conformations) recruit coactivator, recruit corepressor and/or inhibit coactivator to dually induce the agonistic and antagonistic activities. Therefore, the in silico modeling in this study can not only predict ERα and GR disrupting activities but also, especially, identify the potential mechanisms. This mechanistic study breaks the current bottleneck of computational toxicology and can be widely used to prioritize potential estrogen/glucocorticoid disruptor for experimental testing in both pre-clinic and clinic studies.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

BACKGROUND

Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

OBJECTIVE

The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

METHOD

This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

RESULTS

This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

CONCLUSION

The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.

Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor

BACKGROUND

Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A₁ receptor (A₁AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A₁AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A₁AR.

METHODS

Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A₁AR.

RESULTS

Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased.

CONCLUSIONS

Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A₁AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking.

GENERAL SIGNIFICANCE

Ensemble docking is a promising approach for drug discovery targeting flexible receptors.

Peptide-Liganded G Protein-Coupled Receptors as Neurotherapeutics

Peptide-liganded G protein-coupled receptors (GPCRs) are a growing fraction of GPCR drug targets, concentrated in two of the five major GPCR structural classes. The basic physiology and pharmacology of some within the rhodopsin class, for example, the enkephalin (μ opioid receptor, MOR) and angiotensin (ATR) receptors, and most in class B, all the members of which are peptide receptors, are well-known, whereas others are less so. Furthermore, with the notable exception of opioid peptide receptors, the ability to translate from peptide to "drug-like" (i.e., low-molecular-weight nonpeptide) molecules, with desirable oral absorption, brain penetrance, and serum stability, has met with limited success. Yet, peripheral peptide administration in patients with metabolic disorders is clinically effective, suggesting that "drug-like" molecules for peptide receptor targets may not always be required for disease intervention. Here, we consider recent developments in GPCR structure analysis, intracellular signaling, and genetic analysis of peptide and peptide receptor knockout phenotypes in animal models. These lines of research converge on a better understanding of how peptides facilitate adaptive behaviors in mammals. They suggest pathways to translate this burgeoning information into identified drug targets for neurological and psychiatric illnesses such as obesity, addiction, anxiety disorders, and neurodegenerative diseases. Advances centered on the peptide ligands oxytocin, vasopressin, GLP-1, ghrelin, PACAP, NPY, and their GPCRs are considered here. These represent the spectrum of progress across the "virtual pipeline", of peptide receptors associated with many established drugs, those of long-standing interest for which clinical application is still under development, and those just coming into focus through basic research.

Current Advances in Allosteric Modulation of Muscarinic Receptors

Allosteric modulators are ligands that bind to a site on the receptor that is spatially separated from the orthosteric binding site for the endogenous neurotransmitter. Allosteric modulators modulate the binding affinity, potency, and efficacy of orthosteric ligands. Muscarinic acetylcholine receptors are prototypical allosterically-modulated G-protein-coupled receptors. They are a potential therapeutic target for the treatment of psychiatric, neurologic, and internal diseases like schizophrenia, Alzheimer’s disease, Huntington disease, type 2 diabetes, or chronic pulmonary obstruction. Here, we reviewed the progress made during the last decade in our understanding of their mechanisms of binding, allosteric modulation, and in vivo actions in order to understand the translational impact of studying this important class of pharmacological agents. We overviewed newly developed allosteric modulators of muscarinic receptors as well as new spin-off ideas like bitopic ligands combining allosteric and orthosteric moieties and photo-switchable ligands based on bitopic agents.

Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery

BACKGROUND

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases is crucial. To the best of our knowledge, this is the first systematic review on this issue.

OBJECTIVE

The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review.

RESULTS

Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced.

CONCLUSION

In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

Common activation mechanism of class A GPCRs

Class A G-protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology. Understanding receptor activation mechanism is critical for discovering novel therapeutics since about one-third of all marketed drugs target members of this family. GPCR activation is an allosteric process that couples agonist binding to G-protein recruitment, with the hallmark outward movement of transmembrane helix 6 (TM6). However, what leads to TM6 movement and the key residue level changes of this movement remain less well understood. Here, we report a framework to quantify conformational changes. By analyzing the conformational changes in 234 structures from 45 class A GPCRs, we discovered a common GPCR activation pathway comprising of 34 residue pairs and 35 residues. The pathway unifies previous findings into a common activation mechanism and strings together the scattered key motifs such as CWxP, DRY, Na+ pocket, NPxxY and PIF, thereby directly linking the bottom of ligand-binding pocket with G-protein coupling region. Site-directed mutagenesis experiments support this proposition and reveal that rational mutations of residues in this pathway can be used to obtain receptors that are constitutively active or inactive. The common activation pathway provides the mechanistic interpretation of constitutively activating, inactivating and disease mutations. As a module responsible for activation, the common pathway allows for decoupling of the evolution of the ligand binding site and G-protein-binding region. Such an architecture might have facilitated GPCRs to emerge as a highly successful family of proteins for signal transduction in nature.

Mechanism of allosteric activation of human mRNA cap methyltransferase (RNMT) by RAM: insights from accelerated molecular dynamics simulations

The RNA guanine-N7 methyltransferase (RNMT) in complex with RNMT-activating miniprotein (RAM) catalyses the formation of a N7-methylated guanosine cap structure on the 5' end of nascent RNA polymerase II transcripts. The mRNA cap protects the primary transcript from exonucleases and recruits cap-binding complexes that mediate RNA processing, export and translation. By using microsecond standard and accelerated molecular dynamics simulations, we provide for the first time a detailed molecular mechanism of allosteric regulation of RNMT by RAM. We show that RAM selects the RNMT active site conformations that are optimal for binding of substrates (AdoMet and the cap), thus enhancing their affinity. Furthermore, our results strongly suggest the likely scenario in which the cap binding promotes the subsequent AdoMet binding, consistent with the previously suggested cooperative binding model. By employing the network community analyses, we revealed the underlying long-range allosteric networks and paths that are crucial for allosteric regulation by RAM. Our findings complement and explain previous experimental data on RNMT activity. Moreover, this study provides the most complete description of the cap and AdoMet binding poses and interactions within the enzyme's active site. This information is critical for the drug discovery efforts that consider RNMT as a promising anti-cancer target.

In Vitro to in Vivo Translation of Allosteric Modulator Concentration-Effect Relationships: Implications for Drug Discovery

Allosteric modulation of GPCRs represents an increasingly explored approach in drug development. Due to complex pharmacology, however, the relationship(s) between modulator properties determined in vitro with in vivo concentration-effect phenomena is frequently unclear. We investigated key pharmacological properties of a set of metabotropic glutamate receptor 5 (mGlu₅) positive allosteric modulators (PAMs) and their relevance to in vivo concentration-response relationships. These studies identified a significant relationship between in vitro PAM cooperativity (αβ), as well as the maximal response obtained from a simple in vitro PAM concentration-response experiment, with in vivo efficacy for reversal of amphetamine-induced hyperlocomotion. This correlation did not exist with PAM potency or affinity. Data across PAMs were then converged to calculate an in vivo concentration of glutamate putatively relevant to the mGlu₅ PAM mechanism of action. This work demonstrates the ability to merge in vitro pharmacology profiles with relevant behavioral outcomes and also provides a novel method to estimate neurotransmitter concentrations in vivo.

Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)

Demand for novel GPCR modulators is increasing as the association between the GPCR signaling pathway and numerous diseases such as cancers, psychological and metabolic disorders continues to be established. In silico structure-based drug design (SBDD) offers an outlet where researchers could exploit the accumulating structural information of GPCR to expedite the process of drug discovery. The coupling of structure-based approaches such as virtual screening and molecular docking with molecular dynamics and/or Monte Carlo simulation aids in reflecting the dynamics of proteins in nature into previously static docking studies, thus enhancing the accuracy of rationally designed ligands. This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands.

Docking simulation and antibiotic discovery targeting the MlaC protein in Gram-negative bacteria

To maintain the lipid asymmetry of the cell envelope in Gram-negative bacteria, the MlaC protein serves as a lipid transfer factor and delivers phospholipids from the outer to the inner membrane. A strategy of antibiotic discovery is to design a proper compound that can tightly bind to the MlaC protein and inhibit the MlaC function. In this study, we performed virtual screening on multiple MlaC structures obtained from molecular dynamics simulations to identify potential MlaC binders. Our results suggested that clorobiocin is a compound that could bind to the MlaC protein. Through the comparison of the bound geometry between clorobiocin and novobiocin, we pointed out that the methyl-pyrrole group of the noviose sugar in clorobiocin forms hydrophobic interactions with amino acids in the phospholipid binding pocket, which allows the compound to bind deep in the active site. This also explains why clorobiocin shows a tighter binding affinity than novobiocin. Our study highlights a practical path of antibiotic development against Gram-negative bacteria.

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

Allosteric Modulation of Class A GPCRs: Targets, Agents, and Emerging Concepts

G-protein-coupled receptors (GPCRs) have been tractable drug targets for decades with over one-third of currently marketed drugs targeting GPCRs. Of these, the class A GPCR superfamily is highly represented, and continued drug discovery for this family of receptors may provide novel therapeutics for a vast range of diseases. GPCR allosteric modulation is an innovative targeting approach that broadens the available small molecule toolbox and is proving to be a viable drug discovery strategy, as evidenced by recent FDA approvals and clinical trials. Numerous class A GPCR allosteric modulators have been discovered recently, and emerging trends such as the availability of GPCR crystal structures, diverse functional assays, and structure-based computational approaches are improving optimization and development. This Perspective provides an update on allosterically targeted class A GPCRs and their disease indications and the medicinal chemistry approaches toward novel allosteric modulators and highlights emerging trends and opportunities in the field.

Recent advances in computational studies of GPCR-G protein interactions

Protein-protein interactions are key in cellular signaling. G protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are able to transduce extracellular signals (e.g., hormones and neurotransmitters) to intracellular proteins, in particular the G proteins. Since GPCRs serve as primary targets of ~1/3 of currently marketed drugs, it is important to understand mechanisms of GPCR signaling in order to design selective and potent drug molecules. This chapter focuses on recent advances in computational studies of the GPCR-G protein interactions using bioinformatics, protein-protein docking and molecular dynamics simulation approaches.

GPCR Allosteric Modulator Discovery

G protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology; about one-third of all marketed drugs target members of this family. GPCR allosteric ligands hold the promise of improved subtype selectivity, spatiotemporal sensitivity, and possible biased property over typical orthosteric ligands. However, only a small number of GPCR allosteric ligands have been approved as drugs or in clinical trials since the discovery process is very challenging. The rapid development of GPCR structural biology leads to the discovery of several allosteric sites and sheds light on understanding the mechanism of GPCR allosteric ligands, which is critical for discovering novel therapeutics. This book chapter summarized different GPCR allosteric modulating mechanisms and discussed validated mechanisms based on allosteric modulator-GPCR complex structures.

Identification of SLAC1 anion channel residues required for CO2/bicarbonate sensing and regulation of stomatal movements

Increases in CO2 concentration in plant leaves due to respiration in the dark and the continuing atmospheric [CO2] rise cause closing of stomatal pores, thus affecting plant-water relations globally. However, the underlying CO2/bicarbonate (CO2/HCO3-) sensing mechanisms remain unknown. [CO2] elevation in leaves triggers stomatal closure by anion efflux mediated via the SLAC1 anion channel localized in the plasma membrane of guard cells. Previous reconstitution analysis has suggested that intracellular bicarbonate ions might directly up-regulate SLAC1 channel activity. However, whether such a CO2/HCO3- regulation of SLAC1 is relevant for CO2 control of stomatal movements in planta remains unknown. Here, we computationally probe for candidate bicarbonate-interacting sites within the SLAC1 anion channel via long-timescale Gaussian accelerated molecular dynamics (GaMD) simulations. Mutations of two putative bicarbonate-interacting residues, R256 and R321, impaired the enhancement of the SLAC1 anion channel activity by CO2/HCO3- in Xenopus oocytes. Mutations of the neighboring charged amino acid K255 and residue R432 and the predicted gate residue F450 did not affect HCO3- regulation of SLAC1. Notably, gas-exchange experiments with slac1-transformed plants expressing mutated SLAC1 proteins revealed that the SLAC1 residue R256 is required for CO2 regulation of stomatal movements in planta, but not for abscisic acid (ABA)-induced stomatal closing. Patch clamp analyses of guard cells show that activation of S-type anion channels by CO2/HCO3-, but not by ABA, was impaired, indicating the relevance of R256 for CO2 signal transduction. Together, these analyses suggest that the SLAC1 anion channel is one of the physiologically relevant CO2/HCO3- sensors in guard cells.

Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles

Molecular docking can account for receptor flexibility by combining the docking score over multiple rigid receptor conformations, such as snapshots from a molecular dynamics simulation. Here, we evaluate a number of common snapshot selection strategies using a quality metric from stratified sampling, the efficiency of stratification, which compares the variance of a selection strategy to simple random sampling. We also extend the metric to estimators of exponential averages (which involve an exponential transformation, averaging, and inverse transformation) and minima. For docking sets of over 500 ligands to four different proteins of varying flexibility, we observe that, for estimating ensemble averages and exponential averages, many clustering algorithms have similar performance trends: for a few snapshots (less than 25), medoids are the most efficient, while, for a larger number, optimal (the allocation that minimizes the variance) and proportional (to the size of each cluster) allocation become more efficient. Proportional allocation appears to be the most consistently efficient for estimating minima.

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

To investigate large library docking's ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D2 and serotonin 5-HT2A receptors were targeted, seeking selectivity against the histamine H1 receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D2/5-HT2A ligand with 21-fold selectivity versus the H1 receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field.